# Explainable AI is Responsible AI: How Explainability Creates Trustworthy and
Socially Responsible Artificial Intelligence

Stephanie Baker<EMAIL_ADDRESS>0000-0003-0467-7791 James Cook
University1/14-88 McGregor RoadCairnsQLDAustralia4878 and  Wei Xiang  La Trobe
UniversityCorner Plenty Road & Kingsbury DriveMelbourneVICAustralia3083
<EMAIL_ADDRESS>
(dd Month 2023; dd Month 2023; dd Month 2023)

###### Abstract.

Artificial intelligence (AI) has been clearly established as a technology with
the potential to revolutionize fields from healthcare to finance - if
developed and deployed responsibly. This is the topic of responsible AI, which
emphasizes the need to develop trustworthy AI systems that minimize bias,
protect privacy, support security, and enhance transparency and
accountability. Explainable AI (XAI) has been broadly considered as a building
block for responsible AI (RAI), with most of the literature considering it as
a solution for improved transparency. This work proposes that XAI and
responsible AI are significantly more deeply entwined. In this work, we
explore state-of-the-art literature on RAI and XAI technologies. Based on our
findings, we demonstrate that XAI can be utilized to ensure fairness,
robustness, privacy, security, and transparency in a wide range of contexts.
Our findings lead us to conclude that XAI is an essential foundation for every
pillar of RAI.

explainability, explainable AI, responsible AI, trustworthy AI

††ccs: Computing methodologies Artificial intelligence††ccs: Computing
methodologies Machine learning††ccs: General and reference Surveys and
overviews

##  1\. Introduction

In our increasingly data-driven society, artificial intelligence (AI) has
become pervasive for decision-making in a wide range of fields. AI has immense
potential to make positive change in fields ranging from agriculture (Saiz-
Rubio and Rovira-Más, 2020) to healthcare (Baker and Xiang, 2023). However, as
with any tool, AI can also cause great harm if developed and used improperly
or inappropriately; regardless of whether this is done unintentionally or
maliciously. The consequences of bad AI practice can range from bias affecting
underprivileged or underrepresented groups (Ensign et al., 2018; Dastin,
2019), to financial harm (Rinta-Kahila et al., 2022), to physical and mental
harm (Norori et al., 2021; Laestadius et al., 2022).

The need to develop AI in a way that benefits human life and societies has
lead to the emergence of responsible AI (RAI). RAI is fundamentally the field
of applying ethics to the development and utilization of AI, to ensure that AI
systems and used for the good of humanity (Anagnostou et al., 2022; Trocin et
al., 2021). As with all moral philosophy, there is no single consensus on what
makes AI ethical and therefore responsible. However, efforts have been made to
establish RAI characteristics, frameworks, and guidelines in academia
(Anagnostou et al., 2022; Kaur et al., 2022; Li et al., 2023), industry
(Microsoft, 2023; Google, 2023; Samsung, 2023; Meta AI, 2021; IBM, 2023), and
goverments and political bodies (CSIRO, 2023; National Institute of Standards
and Technology and (2022), U.S. Department of Commerce; Ministry of Science
and Technology of the People’s Republic of China, 2022; European Commission,
2021; Kingdom of Saudi Arabia, 2022; Ministry of Economy Trade and Industry
(2022), Japan; HM Government (2021), United Kingdom; India AI and National
Association of Software and Service Companies (2022), India). Frequently
identified pillars of RAI across these diverse sources include fairness,
robustness, transparency, accountability, privacy, and safety.

Explainability is also considered as a pillar of RAI by many works (Barredo
Arrieta et al., 2020; Li et al., 2023; Kaur et al., 2022), often connected to
transparency. Explainability is broadly considered by the field of explainable
AI (XAI), which is focused on providing humans with insight into the reasoning
of AI models during their decision-making processes. Researchers in the XAI
domain have developed methods for producing explanations that include text-
based (Chun et al., 2022; Arous et al., 2021), visual (Selvaraju et al., 2017;
Lundberg and Lee, 2017), and feature importance (Lundberg and Lee, 2017;
Ribeiro et al., 2016) methods.

Given that XAI techniques improve transparency (Shin, 2021; Alam and Mueller,
2021), it is understandable that some researchers have classed explainability
as a pillar of RAI. However, our review of the literature finds that
considering XAI as one discrete pillar of RAI, separate to all others, is
insufficient to capture the potential impact of explainability on RAI. In this
review, we find substantial evidence that XAI should instead be considered as
the foundation of RAI; this key difference is shown in Fig. 1.

Figure 1. Comparison of RAI frameworks (a) as commonly defined in the
literature with explainability as a pillar, and (b) as proposed by this work
with explainability as the foundational concept.

In our review, XAI is shown to support assessment of fairness (Miron et al.,
2021; Nakao et al., 2022; Pradhan et al., 2022), robustness (Sharma et al.,
2020; Malik et al., 2022), and safety (Atif et al., 2022; Sado et al., 2023;
Jia et al., 2022a), thus supporting responsible AI operation and enabling
improvement as needed. The transparency offered by XAI is shown to improve
interpretability and understability of AI through human evaluation in several
studies (Aechtner et al., 2022; Ding et al., 2022; Singla et al., 2023), which
in turn supports accountability for decision making in critical sectors such
as finance (Bücker et al., 2022) and law (Padovan et al., 2023). Finally, it
is illustrated that XAI can be combined with privacy-preserving techniques to
ensure that the requirement of privacy is met without compromising on other
RAI requirements (Bozorgpanah et al., 2022; Gaudio et al., 2023; Raza et al.,
2022; Saldanha et al., 2022).

###  1.1. Original Contributions

The literature contains several high-quality reviews on the topics of XAI and
RAI; however, these are typically considered as separate topics. In this work,
we fill a key gap in the literature by illustrating that XAI is foundational
to RAI. The key contributions of this work are as follows:

  * •

We conduct a broad-scoping review of the literature on both XAI and RAI,
highlighting the technologies, principals, and frameworks developed by
previous works.

  * •

We propose a novel framework that considers explainable AI as the foundation
of responsible AI. This is in contrast to previous works, which have
considered XAI as a single pillar of RAI.

  * •

We demonstrate that XAI is foundational to the RAI principles of fairness,
robustness, transparency, accountability, privacy and safety through rigorous
exploration of the XAI literature.

  * •

Building upon our findings, we illustrate real-world use cases where XAI
directly supports RAI in applications within the key fields of generative AI,
healthcare, and transportation.

###  1.2. Comparison to Other Works

The field of XAI has been extremely active in recent years, and responsible AI
has also become a hot topic. As such, several high-quality surveys have
examined these two topics. However, the concept of XAI as the foundational
technology of RAI has not been considered in previous works. This is shown in
Table 1, where we compare several recent surveys in the literature. We exclude
surveys that review XAI and/or RAI in a domain-specific context (i.e.,
healthcare or finance).

Table 1. Comparison of recent surveys on explainable or responsible AI. Key: ✓✓ The topic is explored in depth. ✓ The topic is briefly mentioned. ✗ The topic is not discussed. Reference |  XAI |  RAI |  Use Cases |  XAI as the foundation of RAI
---|---|---|---|---
Barredo Arrieta et al. (2020) (Barredo Arrieta et al., 2020) |  ✓✓ |  ✓ |  ✗ |  ✗
Ahmed et al. (2022) (Ahmed et al., 2022) |  ✓✓ |  ✗ |  ✓ |  ✗
Anagnostou et al. (2022) (Anagnostou et al., 2022) |  ✓ |  ✓✓ |  ✓ |  ✗
Ashok et al. (2022) (Ashok et al., 2022) |  ✗ |  ✓ |  ✗ |  ✗
Kaur et al. (2022) (Kaur et al., 2022) |  ✓ |  ✓✓ |  ✗ |  ✗
Minh et al. (2022) (Minh et al., 2022) |  ✓✓ |  ✗ |  ✓ |  ✗
Dwivedi et al. (2023) (Dwivedi et al., 2023) |  ✓✓ |  ✗ |  ✗ |  ✗
Li et al. (2023) (Li et al., 2023) |  ✓ |  ✓✓ |  ✗ |  ✗
Saeed et al. (2023) (Saeed and Omlin, 2023) |  ✓✓ |  ✗ |  ✗ |  ✗
This work |  ✓✓ |  ✓✓ |  ✓✓ |  ✓✓

Responsible AI has been considered in recent surveys by Kaur et al. (Kaur et
al., 2022) and Li et al. (Li et al., 2023). In both of these works, the
literature on RAI was reviewed and findings were used to propose principals
and guidelines for the development and deployment of RAI. In each work,
explainability was identified as a characteristic of RAI but was not explored
in depth. Characteristics of transparency, accountability, fairness, and
robustness were identified by each work.

Another work by Ashok et al. (Ashok et al., 2022) sought to outline broader
ethical principles for digital technology development, including but not
limited to AI. They ultimately proposed 14 ethical characteristics, which
included accountability, fairness, and privacy. Explainability was not
discussed.

RAI was explored through the lens of multiple critical use cases in another
recent survey by Anagnostou et al. (Anagnostou et al., 2022). Their work first
explored the problems in critical sectors including healthcare and
transportation, before identifying several characteristics required of RAI.
These characteristics were similar to other works and included transparency,
accountability, and fairness. Explainability was mentioned as a strategy for
supporting transparency, but was not explored in depth.

Dwivedi et al. (Dwivedi et al., 2023) recently conducted a survey that
explored explainability techniques and provides guidance on Python programming
packages that allow for implementation. Similarly, a recent review by Minh et
al. (Minh et al., 2022) explored XAI techniques in depth, with some discussion
of applications. Furthermore, a recent survey by Ahmed et al. (Ahmed et al.,
2022) discussed AI, XAI, and use cases of XAI in Industry 4.0. However, these
three works did not discuss responsible or ethical AI concepts.

Another recent survey conducted by Saeed et al. (Saeed and Omlin, 2023)
explored XAI with a particular focus on future challenges. They also briefly
discuss how XAI can be used to create trustworthy AI systems, which supports
RAI. Perhaps the survey that most considered the relationship between XAI and
RAI was conducted by Barredo Arrieta et al. (Barredo Arrieta et al., 2020).
They comprehensively reviewed the literature on XAI, before discussing RAI as
a separate but related topic. They briefly highlight how XAI could support RAI
in terms of transparency and fairness. However, their review overlooked the
usefulness of XAI in areas of RAI including robustness.

Overall, the literature is rich with surveys on RAI and XAI separately.
However, there is relatively little discussion in the literature of the strong
relationship between these two topics. In this review, we fill that gap in the
literature by investigating how XAI can be used to create AI systems that
align with existing RAI frameworks and characteristics.

###  1.3. Structure of the Paper

The remainder of this paper is structured as follows: Section 2 addresses the
question of what makes AI explainable, and explores the types and applications
of explainable AI. Section 3 then discusses responsible AI, including why
responsibility is important and the characteristics that make AI responsible.
Section 4 then examines how the pillars of responsible AI can be achieved
through explainability. In Section 5, we present several real-world use cases
to highlight the importance of explainability to produce responsible and
trustworthy AI for sectors including healthcare and transportation. We present
recommendations for future works in the AI domain in Section 6, before
concluding the paper in Section 7. The key contents of this paper are
illustrated in Fig. 2.

Figure 2. Overview of the key contents of this paper

##  2\. Explainable AI

###  2.1. What is Explainable AI?

Explainable AI (XAI) describes strategies and processes used to make AI models
more understandable and interpretable to their developers and end-users,
without significant compromise on performance (Barredo Arrieta et al., 2020;
Adadi and Berrada, 2018; Ahmed et al., 2022). Audience is critical to
consider; strategies utilised to understand a model as the developer may
differ from those needed to explain a model to non-technical end users. The
key motivations for explainability are improved transparency and trust in AI
models, particularly those used in critical applications (Saeed and Omlin,
2023).

The need for explainable AI is primarily due to the increasing complexity of
AI models. Early AI models, such as linear and logistic regression algorithms,
can be considered as explainable AI models due to being understandable by
design. In such models, the internal mechanisms linking input to output can be
readily visualised and interpreted. However, these models are often limited in
terms of performance; their simplicity prevents them from accurately
interpreting complex data.

As computing resources have improved, increasingly complex AI algorithms have
been developed to support better interpretation of complex data. Advanced AI
algorithms that continue to grow in prevalence include deep neural networks
(DNNs) and random forest. Such models have demonstrated high performance in
many domains (Piccialli et al., 2021; Himeur et al., 2021; Muhammad et al.,
2021), however their complexity limits insight into what happens in between
input and output; they are not inherently understandable. The increase of
high-performing yet opaque models, often termed black-box models (Adadi and
Berrada, 2018), has lead to the substantially increasing interest in XAI for
interpreting complex models.

The following subsections dive deeper into the existing literature on XAI,
focusing on both explainability by design, where models are intentionally
designed to be inherently interpretable, and post-hoc explainability, whereby
the model is retrospectively explained after its development or use. Within
both types of explainability, there are methods designed to provide local
explanations (i.e., explain a single prediction) or global explanations (i.e.,
explain the general decision-making process of the model). Additionally, some
strategies are model-agnostic (i.e., they can be applied to all or many
models), while others are model-specific (they can only be applied to a select
model). We introduce these terms here as they are relevant to our discussions
in the next two subsections.

###  2.2. Explainability by Design

Several AI models can be considered explainable by design due to their
simplicity; their inner workings are transparent and thus inherently
interpretable. Models that fall into this category include linear regression,
generalised additive models, logistic regression, decision trees, k-nearest
neighbour, and low-dimensionality support vector machines.

####  2.2.1. Linear Regression

Linear regression (LR) seeks to find a line of best fit to continuous data, as
illustrated in Fig. 3(a). In its simplest form with one independent variable,
this type of regression is widely understood and thus inherently explainable.
Multivariate LR is more common in the literature as it can consider multiple
independent variables, each of which can be explained via individual LR plots.
This is still relatively interpretable for lower numbers of variables, however
weightings of variables may need to be explained. Despite their advantages in
explainability, the performance of LR is limited by its inability to capture
non-linear relationships, or complex relationships between variables.
Nonetheless, multivariate LR remains in use with recent works utilising it to
identify radiomics features associated with clinical outcomes (Granata et al.,
2022), water and wastewater forecasting (Jia et al., 2022b), and pandemic
preparedness assessment (Bollyky et al., 2022).

(a)

(b)

(c)

(d)

(e)

(f)

Figure 3. Examples of models that are explainable by design, namely (a) linear
regression, (b) generalised additive models, (c) logistic regression, (d)
decision trees, (e) k-nearest neighbour, and (f) low-dimensionality SVM.

####  2.2.2. Generalised Additive Models

Generalised additive models (GAMs) overlap with linear regression in that they
attempt to fit a line to the data. However, they differ in that they are not
constrained to linear features. They are comprised of multiple ‘splines’ -
non-linear smooth functions - added together to give an overall line
describing the data, as illustrated in Fig. 3(b). While there is some
complexity in their development, their decisions can be readily explained to
the end-user through illustrations of the fitted GAM lines for each variable
considered; as with linear regression, interpretabiilty decreases as the
number of variables increases. Due to their ability to capture non-linear
relationships, GAMs have recently been used for applications including
predicting time to crop maturity (Marcillo et al., 2021), electrical load
forecasting (Pachauri and Ahn, 2022), and bushfire prediction (Phelps and
Woolford, 2021).

####  2.2.3. Logistic Regression

Commonly used for classification problems, logistic regression finds a
logistic function that best fits to a given set of data. The binary
classification case is shown in Fig. 3(c); the threshold (grey) line splits
the outputs into the two classes. This can again be clearly illustrated and
explained. Multiple logistic regression can be performed where multiple
variables exist, remaining explainable for lower numbers of variables.
Multiclass logistic regression is more complex, however recent papers have
attempted to improve explainability through weight heat-maps (Bennetot et al.,
2022). Logistic regression has been relatively popular in XAI literature, used
for applications including image recognition (Bennetot et al., 2022),
telehealth record sentiment analysis (Obeid et al., 2020), adverse reaction to
medication prediction (Zhan et al., 2020), and predicting student outcomes
(Jang et al., 2022; Kostopoulos et al., 2021). However, logistic regression is
often outperformed by more advanced models (Aslam et al., 2022; Anand et al.,
2021).

####  2.2.4. Decision Trees

Decision trees (DTs) utilise multiple input features to make a series of
decisions resulting in an output. The features are typically ‘branching’
points, with the tree splitting into separate paths after each feature is
considered. The is illustrated by Fig. 3(d), which shows a simple DT for
choosing a sport to play based on weather conditions. DTs are explainable due
to their clear logical flow, resembling flowcharts that are common in many
industries. However, they are limited in their implementation and have become
less popular in the literature. DT ensemble methods such as random forest (RF)
are more common in the literature, however explainability decreases as the
number of trees increases; many RF papers explain their models using post-hoc
methods (Peng et al., 2021; Liang et al., 2022; Wang et al., 2021) discussed
in the next section.

####  2.2.5. k-Nearest Neighbour

In $k$-nearest neighbour (KNN) models, a prediction is made by identifying the
k closest datapoints to the new datapoint of interest, and using their values
to determine what output value should be assigned to a new input. In the
classification example illustrated in Fig. 3(e), this would result in the new
data being classified as Class 2. KNN can also be used for regression tasks.
The premise of using proximate data to make a decision is intuitive, and can
be easily visualised where k is small. However, as the value of k increases,
the interpretability of the model decreases. The simplicity of KNN also leads
to limitations, including susceptibility to unimportant features and ignoring
the distances of each neighbour (Uddin et al., 2022b). Classic KNN is
primarily seen in the literature for benchmarking or validating feature
selection algorithms (Tubishat et al., 2021; Song et al., 2021), however KNN
variants have been recently used in applications including severity assessment
for Parkinson’s disease (Zhao et al., 2022) and prediction of cardiovascular
disease (Ghosh et al., 2021).

####  2.2.6. Support Vector Machines

Support vector machines (SVMs) are generally not considered to be explainable,
as they are challenging to visualise or explain for higher dimensionality
inputs. However, we argue that this is also true of algorithms such as linear
regression. Low-dimensionality SVMs split data into classes based on a line or
hyperplane. For simple binary classification scenarios with up to three
features (i.e., three dimensions), the clusters and separating line or plane
can be readily illustrated to the end user; an example for 2D binary
classification is given in Fig. 3(f). Due to the interpretability of low-
dimensionality SVM, some early studies have sought to represent complex data
in lower-dimensional feature spaces so that explainable 3D SVM can be utilised
(Zhang et al., 2022c).

###  2.3. Post-Hoc Explainability

(a)

(b)

(c)

(d)

(e)

Figure 4. Global feature importance approaches to post-hoc explainability,
namely (a) permutation importance, (b) single-feature ICE and PDP, (c) two-
feature PDP, (d) SHAP bar plot, and (e) SHAP beeswarm plot.

####  2.3.1. Permutation Importance

Permutation importance is a model-agnostic explainability technique that seeks
to quantify feature importances in a global context, applied after a model is
trained. It involves randomly shuffling all values for a particular feature
and assessing whether this has an impact on model performance. The output of
permutation importance is a list of weights indicating how important each
feature is. These weights represent the impact of shuffling the particular
feature on a metric of interest. An example output for a house pricing task is
shown in Fig. 4(a), with features that caused the highest increase in
prediction error considered to be the most important. Permutation importance
is a computationally expensive technique, however both the output and the
method itself are easily understood. This has lead to it being widely used in
applications including disease biomarker discovery (Mi et al., 2021), digital
soil mapping (Taghizadeh-Mehrjardi et al., 2021), wildfire susceptibility
analysis (Zhang et al., 2021), and prediction of biohydrogen production
efficiency (Hosseinzadeh et al., 2022). A key limitation is that its results
can be misleading where two variables are highly correlated; shuffling one may
have little effect on the output due to the presence of the other.

####  2.3.2. Partial Dependence Plots and Individual Conditional Expectation

Partial dependence plots (PDPs) and individual conditional expectation (ICE)
plots are model-agnostic methods which seek to understand the changes in
prediction if a single feature is modified in the global context. After a
model is trained, the particular feature is modified to assess the impact on
the output. ICE plots illustrate the impact of the feature for every instance
in the database; e.g., every instance is plotted as a single line. PDP plots
find the average of the impact across all features, and therefore result in a
single line. An example for the impact of land size on house pricing is
illustrated in Fig. 4(b).

ICE and PDP each have advantages and disadvantages. ICE can reveal
heterogeneous effects, such as when changing a feature has a negative effect
on half of the instances, but a positive effect in the other half. In such a
case, PDP would display a flat, horizontal line - a result that is misleading
and not useful. However, the interpretability of ICE decreases as the number
of instances increases; plots become crowded, and it becomes harder to
separate instances or identify atypical instances. PDP avoids this problem by
illustrating an average.

PDP is also useful in in graphing the dependencies of two features. As shown
in Fig. 4(c), PDP can be used to generate contour plots that illustrate the
relationship of two features to the output of interest - in our example, the
relationship of house and land size to house price. This cannot be achieved
with ICE, as the large number of overlapping contours would not be
interpretable.

As they each have their respective benefits, ICE and PDP are commonly applied
together; they have seen use in applications including acute kidney injury
risk assessment (Kim et al., 2021), identification of factors in crash
severity (Afshar et al., 2022), water stream degradation assessment (Maloney
et al., 2022), and mortality risk prediction in intensive care settings
(Safaei et al., 2022). PDP has also been commonly applied alone in the
literature. Recent works have applied PDP to applications including biochar
yield prediction (Khan et al., 2022), concrete strength prediction (Quan Tran
et al., 2022), crop yield assessment (Zhi et al., 2022), and deterioration in
hepatitis patients (Peng et al., 2021).

####  2.3.3. Shapley Additive Explanations

Shapley Additive Explanations (SHAP) (Lundberg and Lee, 2017) is a model-
agnostic explainability approach that uses a game theory approach to assess
the impact on model performance when different combinations of features
(‘players’) participate in the prediction (‘game’). It assesses all
combinations of features and uses an additive approach to summarise the impact
of each feature.

SHAP can be utilised in several different ways. Examples in the global context
are illustrated by Figs. 4(d) and 4(e). In Fig. 4(d), feature importances as
determined by SHAP are simply graphed as bar charts. Meanwhile, Fig. 4(e)
shows a ‘beeswarm’ chart. Features are listed in order of determined
importance. Each dot represents a single instance, and its colour indicates
whether the feature had a low (blue) through to high (red) value; for example,
large distances to the CBD are shown to have a negative impact on the output
of house prices.

(a)

(b)

Figure 5. Local usage of SHAP explanations for (a) data with discrete
features, and (b) image-based data.

SHAP can also be used in a local context. For an individual prediction using
feature-based data, force plots can be generated to explain which features
contributes the final output, including the direction in which they
contributed - as shown in Fig. 5(a). For image-based data, heatmaps can be
generated to illustrate the regions of an image that SHAP considers most
important when making a classification. Examples of these are seen in Figs.
5(b); these examples were generated using the pre-trained ResNet model (Keras,
2023) for ImageNet data (Stanford Vision Lab, 2020). The helicopter was
incorrectly labelled as a warplane, but the presence of blue squares indicates
a lower level of confidence. This transparency can help maintain trust even
where predictions are wrong.

SHAP has been extremely popular in recent literature due to the range of
clearly interpretable graphical methods that can be implemented to understand
its results. SHAP has been used to explain feature importances in applications
including mortality risk assessment (Baker et al., 2021), concrete creep
prediction (Liang et al., 2022), plastic waste forecasting (Fan et al., 2022),
wastewater processing analytics (Wang et al., 2022), electric vehicle route
planning (Ullah et al., 2022), and fault detection for rotating machinery
(Brito et al., 2022). SHAP has also been applied for image explanations in a
wide range of settings, including brain tumour diagnosis (Gaur et al., 2022),
offshore oil slick identification (Amri et al., 2022), and spam image
detection (Zhang et al., 2022a).

####  2.3.4. Local Interpretable Model-Agnostic Explanations

Local Interpretable Model-Agnostic Explanations (LIME) (Ribeiro et al., 2016)
is an explainability tool that does what its name suggests - offers model-
agnostic explanations in a local context. It modifies a single data sample by
altering features, and then fits a linear model to the pertubed dataset in the
vicinity of the original data sample. This is then used to generate
explanations that are accurate for the local context, but invalid in the
global context.

(a)

(b)

Figure 6. Local explanations with LIME for (a) feature-based data, and (b)
image-based data.

LIME returns scores for feature importances in the local context, and thus
many graphical methods for interpretation resemble those seen with previous
feature importance methods. Fig. 6(a) illustrates LIME’s graphical outputs
when assessing a single prediction; it displays the overall prediction, a list
of the most important features, and a ‘waterfall’ graph of the most important
features. Features on the right pushed the predicted house price higher, while
those on the left pushed the prediction lower. LIME can also be used on image
and text data, highlighting regions of an image or words within a document
that contributes to an overall output prediction, as shown in Fig. 6(b). For
each example in Fig. 6(b), the visible section in the left image indicates the
areas that contributed towards the prediction. The green and red areas
highlighted in the right image are those that had positive and negative
impacts on the prediction, respectively.

Alongside SHAP, LIME is one of the most popular explainability methods in the
literature. LIME has been used on feature-based data for applications
including activity recognition with wearable sensors (Uddin and Soylu, 2021),
steel beam damage prediction (Onchis and Gillich, 2021), bankruptcy prediction
(Park et al., 2021), and stock market forecasting (Wu et al., 2022). It has
also been broadly used for image and waveform classification tasks including
heartbeat detection from electrocardiogram (Neves et al., 2021), COVID-19
detection in chest x-rays (Ahsan et al., 2021), and quality assessment for
augmented synthetic aperture radar images (Zhu et al., 2022). Finally, LIME
has been used for text analysis tasks including depression identification from
symptom self-reporting (Uddin et al., 2022a) and identification of resource
needs following natural disaster based on Twitter posts (Behl et al., 2021).

####  2.3.5. Class Activation Mapping

Class activation mapping (CAM) is a model-specific method for providing local
explanations of image-based predictions made by CNNs. Classical CAM by
inserting a global average pooling (GAP) layer after the final convolutional
layer of a convolutional neural network (CNN), and weighting the GAP outputs
to generate a heatmap. An example of outputs is illustrated in Fig. 7, with
the red ‘hotter’ areas contributing the most to a prediction, through to blue
‘cooler’ areas which contribute the least.

Figure 7. Example of outputs generated by CAM, Grad-CAM, and other tools
utilising a heatmapping approach.

Classical CAM has also inspired a large number of variants in recent years,
including Score-CAM (Wang et al., 2020), Gradient-weighted CAM (Grad-CAM)
(Selvaraju et al., 2017), and HiResCAM (Draelos and Carin, 2020). One recent
paper proposed a CAM method using SHAP scores (Zheng et al., 2022). The key
purpose of each variant has been to produce visualisations that more
faithfully illustrate a model’s decision making process. However, it is
challenging to prove superiority of one method against another without
extensive manual comparison of explanations by end-users; this has not been
broadly conducted in the literature.

Variants of CAM have been utilised in a wide range of image classification
papers, with applications including tree mapping for forest management (Onishi
and Ise, 2021), Alzheimer’s disease diagnosis from structural magnetic
resonance imagery (Zhang et al., 2022b), manufacturing cost prediction from
computer-aided designs (Yoo and Kang, 2021), and guided rehabilitation
training (Qiu et al., 2022).

####  2.3.6. Other Techniques

In the above subsections, we have examined some of the most prevalent XAI
techniques in the literature. However, there are many additional techniques
that are gaining interest in the literature. One example is counterfactuals, a
group of model-agnostic local explanation techniques that seek to explain what
would need to be different for the outcome to have changed; these are
particularly useful for binary classification tasks (Guidotti, 2022).

Anchors focus on building rule-based model simplifications that capture the
key features and identify if-then rules that explain predictions (Ribeiro et
al., 2018). Rules are developed in the global context, and can then be used to
explain local predictions. Anchors have also inspired related approaches such
as the recently proposed CASTLE (cluster-aided space transformation for local
explanations), which has been shown to outperform its predecessor on a number
of databases (La Gatta et al., 2021).

Emerging AI systems such as generative AI have also posed challenges for
explainability, as the underlying models have high complexity. In the natural
language processing (NLP) context, one recent work adapted SHAP scores for the
language context, highlighting text that indicated COVID-19 misinformation
(Ayoub et al., 2021). Another novel method determined layer-wise relevance
propagation (LRP) for each component of a transformer model, using this to
generate heatmaps with higher accuracy than CAM approaches (Chefer et al.,
2021). This approach was also demonstrated to work in the NLP context (Chefer
et al., 2021), and has been applied to problems such as COVID-19 screening
using chest radiography (Mondal et al., 2022).

##  3\. Responsible AI

###  3.1. What is Responsible AI?

Responsible AI (RAI) describes the principles by which AI systems are
developed, deployed, and utilised in order to comply with legal and ethical
standards. As laws and ethics vary from region to region, there is no singular
definition of what makes AI responsible; however, many stakeholders have
sought to define frameworks to guide responsible AI development (Kaur et al.,
2022; Anagnostou et al., 2022; Li et al., 2023).

In much recent literature, RAI has been used synonymously with related terms
such as trustworthy AI (Kaur et al., 2022), ethical AI (Ayling and Chapman,
2022), and fair or equitable AI (Robert et al., 2020; Ti et al., 2021).
However, we suggest that RAI encompasses these concepts; that truly
responsible AI must be trustworthy, fair, and adhere to ethical norms, amongst
other characteristics. Based on our review of the literature on RAI and
related terms, this section illustrates the importance of responsibility in AI
and defines six essential pillars of responsibility.

###  3.2. Importance of Responsibility

AI systems are becoming increasingly prevalent in everyday life, used in
applications ranging from media recommendation systems to self-driving
vehicles. The dangers of irresponsibly developed AI in safety-critical
applications are evident: if autonomous vehicles, weapon systems, or automated
medication delivery devices fail to operate as intended, then lives can be
lost or irreparably altered. It is essential that AI systems making safety-
critical decisions are designed responsibly to ensure that they are reliable,
accountable, and free from biases.

In other domains, the dangers of irresponsible AI design are less obvious but
can be equally serious. One notable example is facial recognition, which is
used by law enforcement (New South Wales Police Force, 2023), retail stores
(CHOICE, 2022), and sporting venues (Bavas, 2019). In each of these settings,
facial recognition technology is primarily being used to identify perpetrators
of crime or misdemeanours. However, researchers have found that commercial
facial recognition tools have higher error rates when identifying women and
racial or ethnic minorities compared to light-skinned men (Buolamwini and
Gebru, 2018; Raji and Buolamwini, 2022). Thus, innocent civilians belonging to
marginalized groups are more likely to be wrongly criminalized due to mistaken
identification. Additionally, predictive policing algorithms trained on images
of previously arrested persons have been shown to exhibit racial biases
(Bowyer et al., 2020). It is widely theorised that biases in facial
recognition algorithms are largely due to data imbalance (Bowyer et al., 2020;
Buolamwini and Gebru, 2018); minority groups are often underrepresented in
data used to train commercial facial recognition models (Buolamwini and Gebru,
2018), and overrepresented in data used to train predictive policing models
(Bowyer et al., 2020). Thus, irresponsible AI can reproduce and reinforce
human biases in justice systems, contributing to a cycle of discriminatory
arrest practices (Malek, 2022).

Facial recognition is just one example of where AI systems can have life-
altering consequences. Medical AI diagnosis systems can fail to accurately
diagnose conditions in gender, racial, and ethnic minorities, largely due to
these groups being underrepresented in medicine and medical data until the
last century (Straw, 2020). Hiring algorithms have also been shown to exhibit
biases based on gender, ethnicity, age, disability, and sexuality due to human
biases (Yam and Skorburg, 2021; Tilmes, 2022; Dastin, 2019). Even where these
characteristics are removed from a resume, NLP approaches can still
discriminate against minority groups based on sociolinguistic patterns
(Deshpande et al., 2020). Automated lending algorithms are also known to
exhibit biases in terms of approval rates and maximum loan sizes for minority
groups (Zou and Khern-am nuai, 2022).

Aside from the potentially life-altering consequences of biased AI, there are
also risks of physical, mental and social harm associated directly with some
types of AI. Generative AI systems are increasingly popular in a range of
fields, ranging from education to psychology. However, there are many recent
and historical examples of generative AIs producing biased musings and hate
speech (Alba, 2022; Wakefield, 2016), perpetuating misinformation (Hsu and
Thompson, 2022), or responding inappropriately to mental health crises
(Martinengo et al., 2022; Laestadius et al., 2022). There is also some
evidence that human-chatbot relationships can become dysfunctional and lead to
social or mental harm (Laestadius et al., 2022). Even state-of-the-art models
such as ChatGPT are already being shown to have dangers; one recent work
demonstrated that ChatGPT provided misleading information about mental health
disorders and treatments (van Dis et al., 2023), while another recent work
suggests that ChatGPT provides “correct but inadequate” information about
medical conditions (Yeo et al., 2023).

Even seemingly innocuous recommendation systems can have dangers. Some studies
have found that recommendation systems on some media platforms can lead to
users being guided towards problematic content based on their previous media
engagement (Alam and Mueller, 2021; Yesilada and Lewandowsky, 2022).
Amplification of misinformation and extremism has been found to be
particularly prevalent on YouTube (Yesilada and Lewandowsky, 2022), creating
‘filter bubbles’ where users are predominantly recommended content that
confirms their existing views. Research has found that the filter bubble can
be easily burst through providing more balanced recommendations, however this
is not being implemented by all platforms (Tomlein et al., 2021).

Overall, there are many ways that AI can cause harm - socially, financially,
mentally, and physically. The potential of AI to cause harm is the key
motivation for RAI. Each of the substantial risks outlined in this section can
be mitigated through strategies to open the black box of AI in order to better
develop, deploy and use AI responsibly - for the good of society and
environments.

###  3.3. Pillars of Responsibility

In this section, we propose a framework comprised of six critical
characteristics for responsible AI: fairness, robustness, transparency,
accountability, privacy, and safety. Each of these pillars has been identified
as critical to RAI based on our review of RAI policies and frameworks recently
proposed in academia, industry, and governance, as highlighted in the
following subsections. The identification of these key pillars then guides our
discussions in Section 4, where we examine how XAI is foundational to all
pillars of RAI.

To overview the prevalence of each pillar in the literature, Table 2
summarises the frequency with which various sources include a particular
pillar in their own responsible AI framework or guidelines. In terms of
academic sources, we focus on four recent frameworks published in reputable
journals (Ashok et al., 2022; Kaur et al., 2022; Li et al., 2023; Anagnostou
et al., 2022). In terms of government and political sources, we have
considered AI frameworks and guidelines developed by government and political
bodies in eight diverse regions - namely Australia’s Commonwealth Scientific
and Industrial Research Organisation (CSIRO) (CSIRO, 2023), the United States’
(US) National Institute of Standards and Technology (NIST) (National Institute
of Standards and Technology and (2022), U.S. Department of Commerce), China’s
Ministry of Science and Technology (MOST) (Ministry of Science and Technology
of the People’s Republic of China, 2022), India’s INDIAai (India AI and
National Association of Software and Service Companies (2022), India), Japan’s
Ministry of Economy Trade and Industry (METI) (Ministry of Economy Trade and
Industry (2022), Japan), Saudi Arabia’s Saudi Authority for Data and
Artificial Intelligence (SADAI) (Kingdom of Saudi Arabia, 2022), the United
Kingdom’s (UK) Office for Artificial Intelligence (HM Government (2021),
United Kingdom), and the European Union’s (EU) High-Level Expert Group on
Artificial Intelligence (AI HLEG) (European Commission, 2021). Lastly, we
considered RAI principles from six industry sources that are prevalent in the
AI space, namely Google (Google, 2023), Microsoft (Microsoft, 2023), Meta
(Meta AI, 2021), Samsung (Samsung, 2023), IBM (IBM, 2023), and Amazon (Amazon
Web Services, 2022).

Table 2. Quantifying the number of considered sources that included each pillar in their respective RAI frameworks or guidelines. A total of 4 academic sources, 8 government and political sources, and 6 industry sources were consulted. Academic sources were papers recently published in reputable journals, government and political sources were selected to be representative of a wide range of global regions, and industry sources were selected based on their prevalence in the AI domain. Source | Pillar
---|---
Type |  Fairness |  Robustness |  Transparency |  Accountability |  Privacy |  Safety
Academia |  4 |  2 |  4 |  4 |  4 |  4
Government |  8 |  8 |  8 |  8 |  8 |  8
Industry |  6 |  4 |  5 |  5 |  6 |  4

####  3.3.1. Fairness

Fairness of an AI system ensures that the model treats people and scenarios
equitably, without discrimination. Several recent works on RAI have identified
fairness as a key attribute of responsibility (Ashok et al., 2022; Kaur et
al., 2022; Li et al., 2023; Anagnostou et al., 2022). Another work by Mehrabi
et al. (Mehrabi et al., 2021) conducted a focused survey on bias and fairness
in AI, arguing that fairness is the most critical issue facing successful
development and deployment of AI systems.

Four of the considered industry sources specifically named fairness as a
fundamental principle of RAI (Meta AI, 2021; Microsoft, 2023; Amazon Web
Services, 2022; IBM, 2023). Of the two that didn’t explicitly name fairness,
the concept is still present - Google’s related principle is to “avoid
creating or reinforcing unfair bias” (Google, 2023), while Samsung discusses
fairness under their principle of “Diversity & Inclusion”.

Government sources from around the world unanimously agree that fairness and
non-discrimination is compulsory for AI systems (National Institute of
Standards and Technology and (2022), U.S. Department of Commerce; CSIRO, 2023;
HM Government (2021), United Kingdom; Kingdom of Saudi Arabia, 2022; Ministry
of Science and Technology of the People’s Republic of China, 2022; Ministry of
Economy Trade and Industry (2022), Japan; European Commission, 2021; India AI
and National Association of Software and Service Companies (2022), India).
However, it is worth noting that not all sources define what fairness means in
their regional context.

Overall, the principle of fairness is perhaps the most agreed upon of all the
pillars of RAI. Fairness is explicitly mentioned by all consulted sources
across academia, governing bodies, and industry.

####  3.3.2. Robustness

Robustness of an AI system ensures that the model is accurate across all
potential use cases, and resilient against malicious attacks. This concept is
perhaps the most familiar to AI developers and users; there has long been
significant value placed on ensuring that AI systems are robust, reliable, and
resilient.

The pillar of robustness has been identified by most academic sources. One
work directly names robustness as a key principle of RAI, focusing on
robustness against adversarial attacks (Li et al., 2023). Meanwhile, another
work identifies the related concept of “technical robustness and safety” as
critical; they suggest that a system meets this criteria if it performs as
expected, is resilient against attacks, and can recover from failure without
causing harm (Kaur et al., 2022). Additionally, the related concept of
reliability is mentioned in one work as a means of ensuring that AI is
understandable (Ashok et al., 2022).

In industry, robustness is widely considered but terminology varies. Meta
(Meta AI, 2021), IBM (IBM, 2023), and Amazon (Amazon Web Services, 2022) all
name robustness as a key principal of RAI, while Microsoft uses the
terminology of reliability (Microsoft, 2023). Google (Google, 2023) do not
directly discuss reliability or responsibility, but under the principle of
ensuring safety they mention the need to “avoid unintended results that create
risks of harm”. This is somewhat related to robustness, but does not cover all
elements of the concept. Interestingly, Samsung (Samsung, 2023) do not discuss
robustness, reliability, or accuracy in their RAI framework.

Most government and political bodies note the need for robustness in RAI
systems. Robustness is noted as key for responsibility by government bodies in
the US (National Institute of Standards and Technology and (2022), U.S.
Department of Commerce), EU (European Commission, 2021), Japan (Ministry of
Economy Trade and Industry (2022), Japan), and Saudi Arabia (Kingdom of Saudi
Arabia, 2022). Those that did not discuss robustness instead noted that the
strongly related concept of reliability is critical for responsible AI systems
(HM Government (2021), United Kingdom; Ministry of Science and Technology of
the People’s Republic of China, 2022; CSIRO, 2023; India AI and National
Association of Software and Service Companies (2022), India).

Overall, robustness and reliability are considered across academia, industry,
and governing bodies to be essential for the development of ethical and
responsible AI.

####  3.3.3. Transparency

Transparency is the principle of ensuring that the decisions made by an AI
system are able to be described and reproduced. For transparency to be
achieved, descriptions should be accessible and understandable to users,
developers, and other stakeholders.

The principle of transparency in RAI is largely agreed upon by academic,
government, and industry sources. In terms of academic sources, three works
name transparency as an essential pillar of RAI (Kaur et al., 2022; Li et al.,
2023; Anagnostou et al., 2022). Another work also discussed the need for
transparency in the context of the overarching principle of “intelligibility”
(Ashok et al., 2022). Each of these works note that transparency also supports
the identification of issues within a model that might cause failures or
biases.

In industry, transparency is often mentioned but individual definitions vary.
Microsoft (Microsoft, 2023), IBM (IBM, 2023), and Samsung (Samsung, 2023) each
note transparency as a key RAI principle in the sense of a model being
understandable to users, however Samsung stipulates that this applies only
where it does not compromise corporate competitiveness. Amazon (Amazon Web
Services, 2022) state that models should be transparent to the extent that
users can decide whether or how to use them, while Meta’s policy (Meta AI,
2021) focuses on transparency around how data is used. Google’s stance on
transparency is vague, mentioning that AI systems should feature “relevant
explanations” and “provide appropriate transparency and control over the use
of data”, but this is in the context of accountability and privacy principles
(Google, 2023).

Government agencies are in consensus regarding transparency - all eight
considered government and political bodies noted transparency as a critical
feature of responsible AI (CSIRO, 2023; National Institute of Standards and
Technology and (2022), U.S. Department of Commerce; HM Government (2021),
United Kingdom; Ministry of Science and Technology of the People’s Republic of
China, 2022; Ministry of Economy Trade and Industry (2022), Japan; Kingdom of
Saudi Arabia, 2022; European Commission, 2021; India AI and National
Association of Software and Service Companies (2022), India). Most noted the
need for transparency primarily for supporting auditability and for ensuring
that end users know how decisions are being made about them by an AI system.

####  3.3.4. Accountability

Accountability of an AI system ensures that a model can justify its decisions.
An AI system is accountable if its functionality and decisions can be
explained to users, governing bodies, and other stakeholders to ensure
compliance with laws and ethical standards. Accountability also means that AI
should be subject to feedback and appeals from users and governing bodies,
with mechanisms for remediation in place where issues arise.

In terms of academic literature, accountability is discussed extensively in
all considered works (Ashok et al., 2022; Kaur et al., 2022; Li et al., 2023;
Anagnostou et al., 2022). One work notes that the principle of transparency
can support accountability, however does not guarantee it (Kaur et al., 2022).
Academic literature emphasises the need for AI systems to be made accountable
to ensure that they can be made liable for their decisions.

Industry sources also commonly include accountability in their principles, but
implementations vary. Meta notes accountability and governance as critical for
RAI, with internal self-governance measures in place along with processes for
user appeals (Meta AI, 2021). Google explicitly states that RAI should “be
accountable to people”, further clarifying that this includes providing
explanations and opportunities for feedback and appeal (Google, 2023).
Conversely, Microsoft states that people should be accountable for AI systems
(Microsoft, 2023). Amazon mentions the related principle of governance as key
for RAI, but provides no clear guidelines on how this should be implemented
(Amazon Web Services, 2022). IBM discusses accountability and governance
extensively in their stance on AI ethics, noting that governance to ensure
accountability should occur both internally and externally; however,
accountability is not one of their five primary principles of RAI (IBM, 2023).
Samsung does not include accountability or governance in their RAI framework
(Samsung, 2023).

Accountability is also considered as a key principle of RAI by all government
and political bodies considered, but descriptions of accountability vary. RAI
principles from governing bodies in the US, China, India, Japan and Australia
focus on holding people accountable for AI systems (National Institute of
Standards and Technology and (2022), U.S. Department of Commerce; Ministry of
Economy Trade and Industry (2022), Japan; CSIRO, 2023; Ministry of Science and
Technology of the People’s Republic of China, 2022; India AI and National
Association of Software and Service Companies (2022), India). Meanwhile,
holding AI itself accountable is the focus of accountability principles
proposed by political bodies in the EU (European Commission, 2021) and the UK
(HM Government (2021), United Kingdom). RAI principles from Saudi Arabia’s
SADAI considers accountability of both people and technology, indicating that
people across the AI supply chain should be accountable for AI systems, as
should the system itself (Kingdom of Saudi Arabia, 2022). RAI criteria from
the US (National Institute of Standards and Technology and (2022), U.S.
Department of Commerce), UK (HM Government (2021), United Kingdom), and Japan
(Ministry of Economy Trade and Industry (2022), Japan) consider transparency
and accountability as a combined principle of RAI, while all other bodies
consider accountability as an individual principle.

Overall, accountability is another prevalent pillar of RAI in the literature.
It is clear that AI systems must be accountable for their decisions in order
for systems and developers to meet legal and ethical requirements.

####  3.3.5. Privacy

Privacy in AI systems relates to ensuring that sensitive information used in
training, validating, testing, or using the model remains private. An AI
system should not be able to be ‘reverse engineered’ to reveal private
information about persons in the training data, nor should it be manipulable
to reveal sensitive information about a person through malicious inputs.

Privacy is named as a key principle of RAI by all considered academic sources
(Kaur et al., 2022; Li et al., 2023; Ashok et al., 2022; Anagnostou et al.,
2022). The definitions of privacy in all instances refer to ensuring that
sensitive data are not revealed. Emphasis is broadly placed on ensuring that
data is protected at all stages of the AI pipeline.

Similarly, all considered industry sources mention the concepts of privacy,
data protection, or security in their RAI principles (Google, 2023; Amazon Web
Services, 2022; Meta AI, 2021; Samsung, 2023; Microsoft, 2023; IBM, 2023).
Implementations of privacy vary somewhat between companies. Meta specify that
their privacy approach requires them “assess privacy risks that involve the
collection, use, or sharing of people’s information” (Meta AI, 2021), however
they do not specify how data sharing or collection will be disclosed.
Conversely, Google notes the need for consent and notice of how data is used
or shared (Google, 2023), and IBM notes the need for at least notice (IBM,
2023). Policies from Microsoft, Amazon and Samsung focus on preventing data
breaches (Microsoft, 2023; Samsung, 2023).

All considered government and political sources also noted the need for
privacy in RAI (Ministry of Economy Trade and Industry (2022), Japan; Ministry
of Science and Technology of the People’s Republic of China, 2022; CSIRO,
2023; HM Government (2021), United Kingdom; National Institute of Standards
and Technology and (2022), U.S. Department of Commerce; Kingdom of Saudi
Arabia, 2022; European Commission, 2021; India AI and National Association of
Software and Service Companies (2022), India). Privacy is directly named as an
RAI principle by six bodies (Kingdom of Saudi Arabia, 2022; Ministry of
Economy Trade and Industry (2022), Japan; Ministry of Science and Technology
of the People’s Republic of China, 2022; CSIRO, 2023; European Commission,
2021; India AI and National Association of Software and Service Companies
(2022), India), while the US NIST used the terminology ‘privacy-enhancing’
(National Institute of Standards and Technology and (2022), U.S. Department of
Commerce). The UK government emphasised the related concepts of data
protection and security. All definitions focused on preserving individual
privacy with strong emphasis on data security. The US NIST’s documentation
(National Institute of Standards and Technology and (2022), U.S. Department of
Commerce) also notes that “privacy-related risks may overlap with security,
bias, and transparency,” highlighting the challenges of ensuring transparency
and fairness without compromising privacy.

Overall, the principle of privacy is well agreed upon - all considered
academic, government, and industry sources note it as essential. However, it
is worth noting that there are varied descriptions of how privacy should be
considered amongst industry sources.

####  3.3.6. Safety

Safety of an AI system is focused on ensuring that it does not harm people,
environments, and societies. The need for safety to prevent harm is clear in a
range of applications, from mental health chatbots to autonomous driving
systems.

All considered literature sources discussed safety, to varying degrees (Kaur
et al., 2022; Ashok et al., 2022; Anagnostou et al., 2022; Li et al., 2023).
One work (Kaur et al., 2022) considered the principles of robustness and
safety together - arguing that a robust system would prevent harm by
preventing failures. Other works discussed safety separately, with one noting
the need for real-time protections (Anagnostou et al., 2022) to mitigate
safety issues should an AI fail.

Industry also broadly agrees that safety is a critical issue. Meta (Meta AI,
2021) and Microsoft (Microsoft, 2023) both group safety with either robustness
or reliability in their frameworks. Guidelines presented by Samsung do not
explicitly name safety, but do discuss limiting potential harm to humans,
environments and ecosystems (Samsung, 2023). Google states that AI should “be
built and tested for safety” (Google, 2023). Interestingly, neither IBM nor
Amazon include safety in their guidelines (IBM, 2023; Amazon Web Services,
2022).

All government and political bodies discuss safety. The US’s NIST names safety
as an attribute of RAI (National Institute of Standards and Technology and
(2022), U.S. Department of Commerce), while guidelines from China (Ministry of
Science and Technology of the People’s Republic of China, 2022), Japan
(Ministry of Economy Trade and Industry (2022), Japan), and the UK (HM
Government (2021), United Kingdom) also discuss safety or minimisation of
harm. Australia’s CSIRO (CSIRO, 2023) addresses safety under the principle of
“Do no harm”, stating that civilian AI systems should be designed to minimise
harm and negative outcomes. Government and political bodies from India (India
AI and National Association of Software and Service Companies (2022), India),
the EU (European Commission, 2021), and Saudi Arabia (Kingdom of Saudi Arabia,
2022) all group safety with robustness or reliability, again highlighting a
link between these two pillars.

Overall, safety is widely agreed to be critical for RAI. Interestingly, many
sources grouped safety with robustness or reliability. However, an AI model
could be robust without being safe if it is designed to do harm. As such, we
argue that safety should be considered as a discrete pillar of RAI.

####  3.3.7. Summary

Our review of current academic, industrial, and government frameworks and
policies on RAI has revealed that there are six critical pillars for RAI:
Fairness, Robustness, Transparency, Accountabiilty, Privacy, and Safety.

Interestingly, we found that government and political bodies are leading the
push for responsible AI. In all instances, government and political bodies had
substantial guidelines in place for the development of responsible and ethical
AI. Additionally, government bodies from a diverse group of regions all agreed
on these six fundamental principles of RAI.

Overall, this section has highlighted that these six principles are critical
in ensuring that AI is developed, deployed, and used responsibly. However, a
significant question remains: how do we address these pillars to create
responsible AI systems? In the next section, we propose an answer: the use of
XAI to underpin truly responsible AI.

##  4\. Explainable is Responsible

In this section, we investigate the use of explainability to enable
responsibility. In particular, we have presented strong evidence illustrating
that explainability is foundational for each of the six pillars of
responsibility identified in the previous section. Through our analysis, we
determine that XAI is foundational for RAI; it would not be possible for RAI
to exist without it.

###  4.1. Fairness

In order to achieve fairness in AI systems, developers must first be able to
identify when bias or discrimination occur so that the problem can be
rectified. Explainability techniques are highly suited to revealing a model’s
biases, both at global and local levels. Consider a home loan example: if
explainability tools such as SHAP or LIME are applied and reveal that a
person’s gender was the largest contributor towards an AI system deciding to
decline their home loan application, then this reveals a clear bias that must
be corrected. This intuition has led to explainability being broadly
considered in the literature for promoting development of fair AI systems.

One recent work examined the fairness of AI models including RF, KNN, and DTs
trained to predict recidivism in juveniles convicted of a crime (Miron et al.,
2021). Their analysis used LIME scoring alongside accuracy and fairness
metrics, comparing models trained with only static inputs (those that cannot
be changed, such as sex and nationality), only dynamic inputs (those that can
be changed, such as social support and school performance), and a combination
of both. Using LIME scoring, they identified that AI models made decisions
largely based on static variables where these were available; the models were
learning to be biased. Further analysis showed that disparity metrics also
increased where static variables were included - specifically, the likelihood
of someone being falsely identified as recidivist was significantly higher
amongst men and foreigners. A trade-off between accuracy and fairness was
identified, with the feature encoding strategy of learning fair
representations (LFR) identified as a candidate for mitigating bias (Zemel et
al., 2013). In this instance, the explanations provided by LIME were essential
in revealing the bias of all developed AI models, and thus enabling bias
mitigation strategies to be applied and assessed.

In the medical domain, one recent work proposed a framework based on their
custom XAI tool for auditing medical diagnostics systems (Panigutti et al.,
2021). Their proposed XAI tool was inspired by both LIME and anchors,
generating a local ‘neighbourhood’ of pertubations, and thereafter extracting
rules to provide as output explanations. The overall framework initially
calculates disparity scores for different groups (including groups based on
sex, ethnicity, and insurance status) based on the Wasserstein distance
between predictions and the ground truth. Their custom XAI tool is then
utilised to explain system misclassifications. They suggest that this allows
for assessment of fairness at two stages: firstly, by visualising the
disparity scores and assessing whether any one group suffers from higher
disparity than another, and secondly, but inspecting local explanations of
misclassifications to assess whether the model demonstrated biases in making
those predictions. Overall, their proposed system supports fairness in an
auditable way.

In another recent work (Nakao et al., 2022), an interactive and explainable
tool was developed for loan decision making. The prototype was based on a
logistic regression model with interfaces that explain feature importances at
global and local levels based on model weights, as well as enabling comparison
to similar applications. The interactive component of the prototype allows
non-technical end users to mark a decision as ‘fair’ or ‘not fair’, and adjust
weightings on different features to see if a decision changes; these labels
and suggested weight changes were then utilised to adjust the model. Prior
‘fair’ and ‘not fair’ labels are incorporated into explanations; users can see
how many times the model’s predictions were labelled as ‘fair’ and ‘not fair’
overall, as well as seeing whether similar applications were deemed to be
fairly assessed by the model or not. Additionally, when the suggested weight
changes were incorporated into the model, it was found to increase fairness
based on disparate impact (DI) assessment. Overall, this is an interesting
approach that shows promise in incorporating human feedback from end-users to
improve the fairness of models, however the authors did caution that there is
always a risk of people introducing their own biases to the model.

Another work that sought to enable human ‘fairness debugging’ through a
proposed system called Gopher (Pradhan et al., 2022), which seeks to quantify
and explain model bias, utilising an approach of data subset removal and
fairness metrics to analyse which types of data are causing biases and make
suggestions on how to correct these biases. Explanations are made in the form
of lists of patterns (groups of one or more co-occurring features) responsible
for biases, with additional explanations provided as to possible updates or
data removals that would reduce the bias of the model. The effectiveness of
this approach was demonstrated on multiple well-known databases.

Overall, the use of explainability for the purpose of supporting fairness has
become established in the literature. Several works used explanation tools
like LIME and SHAP to inspect whether particular features are being unfairly
emphasised by a model. Meanwhile, several works propose systems that instead
assess and explain fairness to developers and end-users, with the intention of
receiving feedback and iterating the model to improve fairness. It is clear
from the literature that XAI strongly supports the fairness principle of RAI.

###  4.2. Robustness

Robustness has been a key focus for many works utilising explainable AI.
Explainability can help to reveal whether a model behaves consistently with
similar inputs, as well as being robust against adversarial attacks (Bai et
al., 2021).

In one recent work, a novel explainability technique entitled Similarity
Difference and Uniqueness (SIDU) was proposed for producing heatmap
explanations of image data that are robust against adversarial attacks
(Muddamsetty et al., 2022). SIDU extracts the final layer of a CNN and
calculates similarity and difference masks relating to different feature
applications. These are then fused to generate a final heatmap. In their
experiments, SIDU produced explanations that were strongly aligned with human-
grounded evaluation via eye tracking, outperforming prevalent literature
models including Grad-CAM. To assess robustness, noise was introduced to input
images, with CNN model predictions then explained using SIDU and competing
literature models. SIDU explanations remained consistent as noise levels
increased, while Grad-CAM explanations rapidly declined with increased noise.
Their work demonstrates that explanations can be used to assess whether a
model is robust against noise, and that explainability tools themselves can be
enhanced to improve robustness.

Another recent work examined the relationship between robustness and
explainability from a different angle, exploring whether robust models are
inherently explainable (Noack et al., 2021). Their study used clean and
adversarial data to train models for improved robustness, as well as training
equivalent non-robust models with only clean data. They then examined how
closely salience maps produced by the models matched target salience maps.
Across two open-access databases, it was shown that salience maps for the
robust models were more closely aligned with target salience maps than those
of the non-robust models. This indicates that models designed to be robust
have more inherently interpretable salience maps, potentially eliminating the
need for post-hoc heatmap explanations where robustness is achieved.

Explainability has also been used to quantify robustness, with one study
proposing a robustness metric based on counterfactual explanations (Sharma et
al., 2020). Their proposed Counterfactual Explanation-based Robustness Score
(CERScore) operates on the principal that when comparing two AI models, the
model where counterfactuals are further away on average from the input
instances is the more robust model. CERScore is also model-agnostic and can be
applied with no knowledge of the underyling architecture, a significant
advantage over previous benchmark robustness metrics. Through a series of
experiments on established AI models, the CERScore authors demonstrated that
their proposed metric produced robustness scores consistent with literature
benchmarks, indicating that explainability can be used to quantify robustness.

XAI has also been used to fine-tune models to improve their robustness. In one
recent study, a DNN model was trained to identify malware using adversarial
training and XAI-based fine tuning (Malik et al., 2022). Following initial
adversarial training, SHAP scores were calculated for the considered input
features. A new database was then developed using input features and their
corresponding SHAP scores to create a second adversarial database. This was
then utilised to train the final DNN model. Testing showed that the XAI-based
fine-tuning approach improved the accuracy of the model on unseen data,
compared to using solely traditional adversarial training approaches.

Overall, recent literature has demonstrated that explainability can be
utilised both to quantify robustness and to provide robustness. Both of these
areas of robustness are critical to RAI. Quantification of robustness is a
useful metric for RAI, as it allows comparison between candidate models.
Additionally, the use of XAI to make models that are more robust is essential
to RAI, as it supports development of models are resilient against adversarial
attacks and invalid inputs.

###  4.3. Transparency

Transparency is perhaps the most self-evident characteristic of RAI that XAI
can provide; the key purpose of XAI is to open the black-box of AI to improve
transparency and comprehensibility (Ding et al., 2022). To achieve
transparency, models must provide explanations that are interpretable and
understandable to end-users. Transparency may include clarifying the inner
workings of a black-box model, or explaining how a model made a decision in a
way that users find acceptable and meaningful.

Several recent studies have sought to understand whether XAI explanations are
transparent, interpretable, or understandable to end-users. In one such study,
a counterfactual-based explanation strategy was developed to explain decisions
made by a chest X-ray CNN classifier to expert radiologists (Singla et al.,
2023). To assess the transparency of their approach, they utilised survey
questions and free-text responses to assess whether explanations improved the
understandability and trust in their proposed XAI system, compared to no
explanation, saliency maps, and cycleGAN (Zhu et al., 2017). Based on a
5-point Likert scale, experts rated the counterfactual explanations the
highest for understandability and justifiability. Additionally, all
explainability tools trialled were rated significantly higher for
understability than the no-explanation case. Several free-text comments also
verified that the experts had a better understanding of the AI model following
counterfactual explanation.

In another study, text-based explanations were generated by analysing the
decision pathways of a DT model trained to assess behavioural anomalies and
cognitive status in a smart home residential care setting (Khodabandehloo et
al., 2021). Clinicians were asked to complete a Likert scale survey to rate
their experience with the provided explanations. Questions about transparency
were rated highly, with all clinicians agreeing that explanations were easily
understandable and essential to understanding how the model classified
anomalies for individual patients. Most clinicians reported that the
explanations would assist them in determining whether assessments made by the
model were correct.

In a study focusing on the education sector (Aechtner et al., 2022), a RF
model was developed to make decisions on admissions to graduate school, with
the decisions then explained using LIME, local SHAP, global SHAP, and PDP.
University students with a range of AI experience were then surveyed using a
Likert scale approach to determine whether explanations improved the
understandability of the model. PDP and LIME were both found to significantly
improve understandability amongst both AI novices and students with some AI
experience. SHAP showed some improvement in understandability, but to a lesser
extent. However, it is unclear which graphical format was used to present SHAP
scores, so the chosen method may have contributed to the lower ranking.

Another recent study sought to understand what level of transparency gave
users the most confidence in an autonomous vehicle’s driving decisions (van
der Waa et al., 2020). They developed a confidence metric derived from case-
based reasoning, which utilised prior situations to determine an autonomous
vehicle’s confidence in a current situation. The model’s confidence in a given
situation was presented to a non-expert cohort, using several different text
structures to explain the confidence score. Survey participants were asked to
rank which text structure they found most useful. Results of the survey found
that the best-perceived explanation included the confidence level along with
general information about prior situations. Explanations that provided
confidence in addition to current or future situation information were also
well received. The worst-ranked explanation was one that provided the
confidence level only, with no transparency about how that confidence level
was reached. These results indicate that transparency via text explanations of
model confidence were critical to improving acceptability of the proposed AI
approach.

Overall, recent literature that has sought to quantify transparency,
understandability, or interpretability of AI systems have indicated a strong
link between explanations and transparency. A wide range of explanation
approaches have been examined by the literature, including text-based
explanations, visual explanations, and feature-based explanations; each has
been shown to improve transparency to some degree. Explanations have also been
shown to support transparency for a wide range of users, with varied levels of
domain and AI expertise.

###  4.4. Accountability

Accountability requires AI models to justify their decisions to ensure that
they can be audited and assessed for compliance with legal and governance
requirements. This principle is related to transparency, as models must be
able to explain their decisions to be accountable.

One sector where accountability is critical is finance. In one recent study,
an auditable pipeline based on various XAI techniques was proposed to provide
transparent and accountable credit scoring (Bücker et al., 2022). The authors
note that logistic regression is popular in credit risk scoring due to the
inherent explainability of these models that has lead to acceptance amongst
regulators, however note that logistic regression does not perform as strongly
as advanced AI techniques for this task. To address this issue, they proposed
a pipeline that begins with global explanations using feature permutation
importance, followed by assessment of the most important features with PDP and
ICE plots. From there, local instances are then able to be explained using
LIME, SHAP, and another variant of SHAP. They indicate that this pipeline
explains advanced AI algorithms in a manner that satisfies prevalent
regulatory and legal requirements for transparency, accountability, and
privacy.

There is also a need for accountable AI systems to establish liability in
legal cases. One recent work identifies XAI as highly suitable for providing a
‘forensic toolset’ that can help establish liability of AI systems in a range
of settings (Padovan et al., 2023). In particular, they illustrate a case
study where an autonomous vehicle changes lanes to avoid a collision with car
A, ultimately causing a collision with car B. The authors demonstrate that the
use of XAI tools can help establish why the AI model made the decision, thus
establishing factual and legal causation as is required by many common law
systems. Ultimately, this enables legal systems to hold AI systems accountable
for their actions, and thereafter to determine whether the AI system in
question or another party is at fault for an accident. It is suggested that
LIME, ICE, and PDP are critical XAI tools for understanding the global model,
while SHAP is essential to understand individual decisions; this is consistent
with suggestions made by the credit scoring study discussed above (Bücker et
al., 2022).

In the medical sector, an auditable pipeline was recently developed for
prediction of oxygen requirement in COVID-19 patients, based on GradCAM
explanations of features identified in chest x-ray input images (Chung et al.,
2022). Their technique included the development of model-derived atlases for
similar feature patches and similar overall predictions, based on areas that
GradCAM highlighted as important. When a prediction is made, the clinician is
presented with 8 similar patches from the atlas alongside the test patch, a
GradCAM heatmap of the overall x-ray, a table comparing the test x-ray with
several similar x-rays both visually and with feature-based similarity scores,
and finally a table showing the model’s confidence in each feature it detects.
Clinicians can also select feature labels of interest to receive further
characterization of the given disease. The use of example-based explanations
and numerical scores for confidence makes the decisions made by AI highly
auditable and accountable, both to expert clinicians and regulatory bodies.

Overall, it is clear that accountability is critical in a wide range of
sectors to ensure that AI models can be held to the same standards as humans.
The literature indicates that XAI can provide an appropriate level of
accountability and auditability across several key sectors, which contributes
significantly towards the development of RAI systems.

###  4.5. Privacy

Privacy is critical for AI systems, as no sensitive information should be
accessible to users of an AI system. Achieving privacy while maintaining
transparency has previously been identified as a key challenge in developing
responsible AI systems (Felzmann et al., 2020).

In standard use, most techniques for global and local explanation are
inherently privacy-preserving as they do not explicitly reveal identifiable
information about data in the training set. However, limited recent studies
have identified that targeted adversarial attacks on CAM-based XAI models have
a nominally increased chance of success in breaching privacy through training
image reconstruction (Zhao et al., 2021) or model duplication (Yan et al.,
2023) compared to equivalent models without explanations.

Critically, these attacks were successful against both explainable and non-
explainable models; thus data protection and privacy-preserving techniques are
essential for developing responsible AI. However, such techniques make AI more
opaque and thus are in conflict with transparency and accountability
principles for RAI. This brings us full-circle back to XAI: several recent
works have demonstrated that XAI techniques continue to provide robust and
meaningful explanations where data protection and privacy-preserving
techniques have been applied, thus supporting privacy to be incorporated into
RAI systems without compromising on other RAI principles.

In one such work, federated learning was applied to develop a model for heart
arrhythmia classification from electrocardiogram (ECG) signals before
explainability was applied (Raza et al., 2022). Federated learning preserves
privacy by enabling individual insitutions to train local models, with these
models then shared to a central node for aggregation. The aggregated model is
then returned to institutions for use. This enables the development of more
robust models without direct data sharing, and thus protects against data
inversion attacks. In the ECG study, federated learning was shown to improve
accuracy in detecting key ECG features and classifying arrhythmia compared to
local models, even where signals were noisy. Explanations were provided using
GradCAM, providing heatmaps of the most important regions in a signal. Model
duplication is still feasible, however reconstruction of local models is
unlikely if weight sharing is protected.

In another work, swarm learning was applied to develop a model for cancer
diagnosis from histopathology slides (Saldanha et al., 2022). Swarm learning
is similar to federated learning in that models are trained locally first,
however aggregation of models occurs by sharing trained local models in a
peer-to-peer structure; this distributed and decentralized architecture
ensures that the failure of a single node does not lead to failure of the
entire network. The cancer histopathology work demonstrated that this enabled
the development of swarm models that exceeded the performance of local models
across all criteria of interest. To explain model predictions, heatmaps were
generated to illustrate regions of interest that lead to a prediction. As with
federated learning, this approach is robust against data inversion attacks as
the final aggregate model has no knowledge of training data. The decentralized
nature of swarm learning also ensures higher robustness in the event of a node
failure.

Another approach to privacy preservation is the notion of differential
privacy, where data is anonymized by injecting noise. One recent study
explored whether this approach had a significant impact on SHAP values, using
various open-access databases, data pertubation methods, and AI models
(Bozorgpanah et al., 2022). Their findings showed that SHAP values were
minimally affected by data protection techniques. Simple linear models were
more affected than more advanced models such as support vector regression,
however overall still provided meaningful explanations. Based on their
findings, the authors concluded that data protection and explanation are not
mutually exclusive.

Differential privacy was also considered in an image context by a recent work
that used image compression to remove spatial and edge information in order to
privatise data prior to training (Gaudio et al., 2023). Their work considered
three image-based use cases: chest x-ray classification, cervix type
classification, and glaucoma detection. Their work indicated that high
accuracy could still be achieved with compressed images. Salience map
explanations were also able to provide meaningful information about relevant
areas of an image without access to the original uncompressed image. Overall,
this approach improves privacy as original images are not used for model
training.

Overall, XAI enables privacy in an indirect manner. AI systems are vulnerable
to many adversarial attacks, and privacy-preserving techniques can compromise
RAI principles such as transparency and accountability. XAI thus supports
privacy by enabling privacy-preserving learning and data protection techniques
to be implemented without violating the other key requirements of RAI.

###  4.6. Safety

Safety of humans, environments and societies is critical for AI systems,
particularly in high-risk settings such as autonomous driving and healthcare.
As XAI helps reveal the inner workings of an AI model, it can similarly assist
in revealing safety risks. Additionally, XAI systems can be developed to sit
above existing AI systems to provide safety in terms of detecting network
intrusion or other attacks, protecting the overall pipeline from adversarial
attacks and thus supporting RAI.

In one recent work, the need for XAI in ensuring safety in healthcare systems
was examined through a case study on predicting when a patient should be
extubated from mechanical ventilation in intensive care (Jia et al., 2022a).
In this application, safety is of the utmost importance as early extubation
can lead to emergency re-intubation, while late extubation increases risk of
complications including pneumonia. Their work identified that XAI techniques
are useful for verifying safety in several stages of AI development. In
particular, they highlight that feature importance and counterfactual example
methods support safety in AI. Feature importances can allow clinicians to
assess whether the model utilised features that meet clinical expectations,
improving confidence that the model makes decisions that will benefit the
patient. Meanwhile, counterfactual explanations can be used to determine how
many features would need to change (and by how much) in order to change the
decision. This allows clinicians to evaluate the confidence of the model in
its decision, which in turn supports patient safety as clinicians can make
informed decisions about whether the model can be trusted in a particular
instance or whether further investigation is needed.

In the autonomous driving domain, XAI has been applied in one recent work to
compare the performance of three AI models classifying traffic signs where
camera failures such as broken lens, ice coverage, or dead pixels occur (Atif
et al., 2022). Their study evaluated the performance decreases for several
types of lens failure, identifying that the model based on AlexNet (Krizhevsky
et al., 2012) was the strongest performer across most lens failure modes. To
understand this further, LIME heatmaps were applied to traffic sign
classification outputs. This illustrated that predictions made by the AlexNet
model used features distributed over a larger section of the inputted image
compared to the other models. The authors hypothesise that this leads to the
higher camera failure tolerance of AlexNet, as the features of interest are
less likely to be obscured by the defect if they are widely spread throughout
the image. In this context, XAI supports safe autonomous driving by enabling
identification of the traffic sign model that performs most reliably under
adverse conditions.

Another recent work reviewed the field of goal-driven AI systems (GDAIs) (Sado
et al., 2023), which include robots and other agents operating independently
to fulfil their individual goals. In particular, they examined the need for
explainable GDAIs in safety-critical applications to ensure that GDAIs do not
cause harm to humans or environments. Their work identified that communication
of actions via explanations such as CAM, SHAP, and textual description are
essential for communicating the decisions of GDAIs. Through their analysis,
the authors identify explanations support human-computer interaction, and thus
support safety by enabling early detection of problems and thus early
intervention to prevent failures, which is particularly crucial for safety-
critical applications.

XAI can also be used to create a layer of protection against adversarial
attacks on digital systems that employ AI. In one recent study, explainability
techniques were utilised as part of an intrusion detection system for
recognising attacks on Internet of Things (IoT) networks (Houda et al., 2022).
Using a pipeline including rule-based explanations, LIME, and SHAP, the
proposed intrusion detection system accurately detects intruder or compromised
nodes on the IoT network, with global and local explanations provided to show
how the decision was made so that human experts can decide on a response path.
The use of XAI in this context helps to protect any AI models operating on the
IoT network data from attack, as rapid and interpretable detection of
intruders greatly reduces the risk of the IoT network being poisoned by
malicious data injection.

Overall, explainability strongly supports safety across diverse AI
applications. XAI techniques improve safety by enabling human experts to
review decisions and decide whether they were made reasonably. Additionally,
XAI can be applied on top of other AI systems to detect security risks such as
network intrusions, reducing the risk of adversarial attacks. XAI is thus an
important tool for ensuring that the RAI principle of safety is met.

###  4.7. Lessons Learned

Through our exploration of the literature, it was found that XAI is
foundational to each key pillar of RAI. The relationship of XAI to the
principles of transparency and accountability was found to be straightforward.
The primary intention of explanation is to make models more interpretable, and
several works have established that users perceive XAI to be more transparent,
understandable, and trustworthy (Aechtner et al., 2022; Ding et al., 2022;
Singla et al., 2023). Transparency is also the first step towards
accountability, where AI models need to be able to explain and justify their
decisions to relevant stakeholders. Works to date have established that XAI is
essential in making AI accountable and, where necessary, liable for its
actions in critical sectors from autonomous driving to healthcare (Padovan et
al., 2023; Chung et al., 2022).

Accountability is also connected to the principles of fairness and safety. In
our exploration of the literature, it was found that XAI tools can highlight
societal biases learned from historic data (Panigutti et al., 2021; Nakao et
al., 2022; Miron et al., 2021; Yogarajan et al., 2022). Human-in-the-loop
fairness debugging was also discussed (Pradhan et al., 2022; Nakao et al.,
2022), with human users able to provide feedback on potential biases based on
model explanations, as a form of fairness accountability. Human-in-the-loop
debugging was also considered in the safety context (Sado et al., 2023), with
explanations provided by goal-driven AIs to human safety auditors. In turn,
humans could provide feedback to the AI models that would improve the safety
and failure tolerance of the systems. Similarly, safety-critical applications
in health (Jia et al., 2022a) and autonomous driving (Atif et al., 2022) were
supported by XAI through the provision of explanations to domain experts, who
could then assess how and why the model is making a decision and determine
whether that decision is appropriate.

Safety is also tied to the principle of robustness; a model that is less
likely to fail, is less likely to cause harm in safety-critical applications.
XAI was found to support the RAI principle of robustness in several direct and
indirect ways. In particular, XAI has been used quantify robustness (Sharma et
al., 2020), enable robustness comparison between models (Muddamsetty et al.,
2022), and support fine-tuning of models to enhance robustness (Malik et al.,
2022). Interestingly, one study also identified that robust models have a
higher degree of inherent explainability (Noack et al., 2021), indicating a
strong relationship between these principles.

In our analysis of XAI robustness literature, one study highlighted that
certain explanations remain accurate in the face of adversarial attacks that
utilise perturbed inputs to compromise a model (Muddamsetty et al., 2022). The
resilience of XAI to adversaries was considered by several works seeking to
improve privacy. In some works, it was found that XAI could still successfully
explain decisions where user privacy was protected by perturbing the dataset
such that adversarial attacks would not be able to unveil genuine and
identifiable information (Bozorgpanah et al., 2022; Gaudio et al., 2023).
Another key approach for preserving privacy was distributed learning, both in
federated (Raza et al., 2022) and swarm learning structures (Saldanha et al.,
2022), where local models are trained on private data, shared securely, and
then aggregated into a final model for the end-user. In this context, XAI
supported privacy indirectly by enabling models to continue meeting other RAI
requirements whilst using an otherwise opaque method of learning.

In this section, we identified that research into XAI and RAI is an active
field that remains in its infancy. The works highlighted have firmly
established that XAI and RAI are inseparable concepts, however much remains to
be explored in how best to utilise XAI to create truly responsible AI systems.
Notably, relatively few works sought to develop frameworks or metrics for
assessing the quality of explanations as they pertain to AI responsibility.
Additionally, many XAI works to date considered only a subset of the six
principles of responsible AI. Thus, there is still a need to create AI systems
that are truly responsible in all attributes. XAI for RAI is a significant and
growing field, and one which remains open for further exploration.

##  5\. Use Cases

Having now established that XAI is foundational to RAI, this section presents
several short case studies that illustrate how explainability can be utilised
to create responsible AI. In particular, we present case studies in content
generation, healthcare, and transport - domains where responsibility is
essential to ensure that AI acts for the benefit of humanity and minimising
risk of harm.

###  5.1. Generative AI

Generative AI (GAI) has recently become a prevalent topic in the literature
and media, largely due to the emergence of large-language generative models
such as ChatGPT. GAI has previously been found to exhibit biases (Alba, 2022;
Wakefield, 2016), produce disinformation (Hsu and Thompson, 2022) or
misleading information (van Dis et al., 2023). These issues are barriers to
GAI meeting RAI requirements, and can pose substantial harm to users.

Explainable AI could be leveraged to make GAI systems more responsible.
Consider a user who asks a GAI model a question about clinical depression. The
model might provide information about symptoms and treatment paths, however
without explanation the user cannot ascertain that the information is correct.
XAI could be used to explain GAI outputs by providing local explanations that
highlight how it understood the prompt, similar to previous research that used
LIME for text analysis tasks (Uddin et al., 2022a). This would allow users to
assess whether their query has been appropriately understood by the GAI model.

In terms of the generated output, text-based explanations where the GAI model
generates an output and an explanation of how it chose this output are
suitable. This approach has been highlighted by one early work in the GAI/XAI
space (Khurana et al., 2021). Case-based reasoning can also be used to
evaluate the similarity between the users prompts, previous prompts, and
legitimate sources to assign confidence weightings to different sections of
its generated output. For example, a GAI model responding to a question about
clinical depression may have higher confidence in some symptoms than others
based on its training set, and this could be highlighted using colour coding -
similar to a previous approach utilised in decision-making systems (van der
Waa et al., 2020). Additionally, example-based approaches could be utilised by
GAI models to effectively cite their sources. Example-based approaches have
previously been utilised in healthcare applications such as chest x-ray
assessment to provide examples of similar chest x-rays and their diagnoses
(Chung et al., 2022). In the GAI context, example-based explainability could
highlight relevant excerpts from the literature - in our clinical depression
example, the GAI model could list five sources that describe the symptom of
‘fatigue’ when a user hovers over the word.

Having GAI models explain their outputs supports responsibility across all key
pillars. For the user, explanation of prompt understanding and output
generation provides transparency that supports them in identifying whether
information is correct. This can enhance user safety and interpretation in
many settings. From the developer perspective, explanations make the model
auditable for RAI attributes so that it can be held accountable for its
actions and continuously improved. For example, explanations of prompts would
allow model auditors to phrase a question in many different ways, assessing
model robustness to phrasing changes. Additionally, auditors could examine
explanations of prompts and outputs for signs of unfair bias, dangerous
information or advice, and privacy breaches such as reconstructing sensitive
data from the training set. Using XAI to ensure transparency and
accountability in GAI can thus support iterative development to enhance the
fairness, privacy, safety, and robustness of these models.

Overall, GAI has significant potential to cause harm if not developed
responsibly. In this case study, we have shown how XAI can serve as the
foundation for responsible GAIs that better serve humanity. This field is
novel with few works published to date, offering much opportunity for future
research.

###  5.2. Medical Diagnostics

The field of medical diagnostics is one where AI holds much potential. In some
scenarios, AI has been shown to outperform expert clinicians (Jiang et al.,
2023), partly due to their ability to learn from large datasets. Responsible
AI development is critical in healthcare due to the sensitive nature of the
underlying data, and the potential for significant harm to humans if incorrect
decisions are made.

One potential source for harm in medical AI models arises from historic
databases reflecting societal biases. Without caution, AI models can learn the
same biases and thus make potentially harmful decisions. Consider the case of
attention-deficit/hyperactivity disorder (ADHD). Due to societal biases, ADHD
has historically been underdiagnosed in women and racial minorities (Garb,
2021). ADHD diagnosis with AI is an active field of research that has shown
promising results, however a recent work cautioned that certain inputs can
introduce unfair bias into models (Loh et al., 2022). XAI can assist in
ensuring fairness in such models, with global and local explanations helping
to reveal if the model places weight on parameters that perpetuate societal
biases. For feature-based data types, explanations such as counterfactuals,
SHAP, and LIME are strong candidates, while CAM would be suitable for image
data.

Similarly, model robustness can be assessed using XAI techniques. By
quantifying how significantly the data would need to change for the prediction
to change using a technique such as CERSCore (Sharma et al., 2020), the
robustness of a model can be assessed. This also supports fairness and safety,
as robust models will not make decisions based on one or two excessively
weighted features.

Global and local explanations also make the model inherently more transparent,
and support accountability. Clinicians and regulatory bodies can review the
decisions made by AI models to ensure that the model operates in accordance
with clinical knowledge and legal requirements. For individual patients,
clinicians can utilise local explanations to understand the confidence of the
model, and thus make assessment on the correctness of the decision. This also
improves patient safety, as the interaction between AI and clinicians further
supports accurate ADHD diagnosis.

In the medical context, the concepts of safety and privacy are intertwined due
to the sensitive nature of medical data. Federated learning and swarm learning
are of interest in the medical space (Raza et al., 2022; Saldanha et al.,
2022) as these methods eliminate the need for data sharing between
institutions; however, distributed learning makes models more opaque.
Fortunately, XAI still provides robust, transparent and meaningful
explanations where distributed learning has been utilised - enabling privacy-
preserving training to be implemented without compromising other
responsibility requirements.

Overall, XAI is critical for RAI in healthcare, due to the direct impact of AI
decisions on human life. In this case study, we have illustrated how XAI can
be utilised to develop an RAI system for ADHD diagnosis. The concepts
discussed here are applicable to many other diagnostic use cases.

###  5.3. Autonomous Vehicles

Autonomous vehicles (AVs), vessels and aircraft pose significant risk to human
life if not developed responsibly. AI systems in this domain must be able to
make rapid decisions, and poor decisions can lead to injury or loss of life -
both for passengers and bystanders.

XAI is essential for ensuring that AVs are piloted by RAIs. Transparency about
decision making on the road can help to support user trust, and enable the
human driver to regain manual control if needed. For example, if an AV speeds
up after seeing a new speed limit sign, this decision could be explained to
the user by showing the sign and decision, and highlighting the regions that
lead to the decision using an approach such as CAM. If the decision is
incorrect, the user could make manual speed adjustment and report the error to
the car manufacturer. Similarly, feature-based explanations of speed changes
based on weather conditions using LIME or a similar method would support the
user in assessing the suitability of the decision made.

This transparency makes the AV accountable to the user and to car
manufacturers. XAI can further ensure accountability when events such as
collisions resulting in injury occur, as explanations of how and why
parameters were used to make a decision can aid in determining whether the AI
model acted reasonably and in line with legal requirements (Padovan et al.,
2023).

Explanations used to ensure transparency and accountability also support
robustness and safety. Users can make informed decisions about whether the AV
has made a good choice, and feedback can be provided to vehicle manufacturers
for iterative improvement to enhance robustness and safety. Legal
accountability further encourages manufacturers to ensure that their systems
are resilient and safe. During the development stage, XAI can also be utilised
to compare the robustness of AI models, to identify the system that makes the
safest decisions in various contexts (Atif et al., 2022).

Fairness in the AV context has primarily been considered in terms of the Moral
Machine Experiment (MME) (Awad et al., 2018), which placed humans in the
metaphorical driver’s seat of an AV that was guaranteed to collide with
someone - and asked them to choose which person or group of people the car
should hit. This experiment revealed human preferences contain biases based on
attributes such as sex, age, and societal status. Thus, AVs are at risk of
inheriting biases that could put life at risk. While the MME presents an
extreme case, it is clear that there is a need to explain the decisions made
by AVs - both for legal accountability purposes in extreme events, and to
ensure that we are not sharing the road with vehicles that are prejudiced.
Explained decisions can be evaluated for any potential biases, and this in
turn can be used to iteratively improve the fairness of systems.

The final attribute to consider for RAIs in autonomous driving is privacy.
Large datasets are essential for ensuring that AI models for autonomous
driving perform well, however many companies would be unwilling to share
proprietary data. Swarm learning may be more acceptable to companies, as they
could share local models in a peer-to-peer structure with only companies they
choose. Another aspect of privacy is user privacy; users will have varying
preferences regarding the sharing of sensitive information such as location.
Fortunately, this can largely be addressed by implementing differential
privacy techniques. XAI again supports privacy by ensuring that all other
attributes of RAI continue to be met when privacy-preserving techniques are
implemented.

Overall, the need for RAI to drive AVs is clear. AVs are making continuous and
rapid decisions, and many of their decisions can mean life or death for
passengers and bystanders. The transparency provided by XAI is essential for
investigating decisions that AVs make to ensure that they operate robustly,
safely, and fairly. This also enables AVs to be held accountable for their
actions should accidents occur. XAI also responsible AVs by enabling privacy-
preserving techniques to be implemented without compromising on transparency
and accountability. In this case study, we further illustrate that XAI and RAI
are inseparable concepts. When AI is behind the wheel, it is essential that
XAI techniques are in the passenger seat.

##  6\. Opportunities for Future Work

This review of the literature has clearly demonstrated that XAI is
foundational to the development of RAI systems. However, this field remains in
its infancy, with much opportunity for future research. In this section, we
outline several key directions for future research in this critical domain.

Quantifying responsibility: One notable direction for future research is in
quantifying responsibility to enable comparison between different AI systems.
Some early works have proposed metrics for assessing attributes such as
fairness (Pradhan et al., 2022) and robustness (Sharma et al., 2020), however
these have not been widely validated. Similarly, some works have sought to
assess transparency by surveying users of XAI models (Singla et al., 2023;
Khodabandehloo et al., 2021; Aechtner et al., 2022; van der Waa et al., 2020),
however each of these works used different surveys. A significant gap remains
in terms of developing clear frameworks, guidelines, or metrics for assessing
transparency. Similarly, metrics or methods for assessing accountability,
privacy, and safety remain open research topics. Development of clear metrics
or methods for quantifying responsibility would be a valuable addition to the
literature. Validated strategies for quantifying responsibility could then be
utilised to assess different XAI techniques to determine which are the most
responsible, and subsequently develop new XAI techniques that enhance
responsibility further.

Improving explanations: The field of XAI has been rapidly growing, however
relatively few studies have sought to analyse whether the explanations
provided by their proposed techniques are understandable and acceptable to
humans users. These characteristics are essential in ensuring that XAI tools
are contributing to responsibility. There remains a significant gap in the
literature in determining how humans interpret data, and subsequently
developing XAI systems that are in line with human understanding. The
development of databases and methods for capturing human attention to data
would greatly support research in this area.

Considering all pillars of responsibility: In this review, it is clear that
most works on XAI for RAI focus on only one pillar of responsibility. Future
research would benefit from utilising XAI techniques to improve responsibility
in more than one area. The case studies presented have highlighted that this
is feasible, particularly given certain XAI techniques and approaches can
address many pillars of RAI simultaneously. While there certainly remains a
need for ongoing research that seeks to improve responsibility with respect to
certain pillars, developers of AI systems should seek to meet all pillars of
RAI using XAI tools, as AI cannot be truly responsible if it addresses some
pillars of RAI at the expense of others.

Responsible GAI: In terms of domain-specific research, our review indicates
that explainability and responsibility of generative AI is under-explored.
Given the prevalence and controversy surrounding generative AI systems such as
ChatGPT in the literature and popular media, there is a significant research
opportunity in implementing and continuing to develop XAI techniques that will
lay the foundation for responsible GAI models that incorporate all pillars of
RAI.

Overall, the field of XAI for RAI offers significant opportunities to future
researchers. Research to date has illustrated that XAI is foundational to RAI,
however there remains much research opportunity in areas such as quantifying
responsibility, enhancing explanations to be more responsible, developing RAI
models that address all pillars of responsibility, and finally applying XAI
techniques to support development of RAI in rapidly evolving and exciting
fields such as generative AI.

##  7\. Conclusion

In this work, we conducted a scoping review of current XAI and RAI literature,
and presented an evidence-based argument that XAI is foundational to RAI
across all key pillars of responsibility. Our review began with XAI, exploring
methods for explainability by design and post-hoc explainability. We
identified approaches for feature-based, image-based, and text-based
explanations at both local and global levels, with illustrative examples
provided for each prevalent method.

We then examined the literature on RAI, considering sources from academic
literature, government and political bodies, and large technology companies.
Based on our findings, we identified six key pillars of responsibility:
fairness, robustness, transparency, accountability, privacy, and safety.
Interestingly, it was also identified that governments are leading academia
and industry on the push for responsible AI, with diverse government and
political bodies from across the globe having substantial guidelines in place
for the development of responsible AI.

With the fields of XAI and RAI established, our review then explored the
relationship between XAI and RAI. Based on the evidence found, we identified
that XAI is foundational to all pillars of responsibility. XAI supports AI
systems to be more transparent and accountable without compromising privacy,
which in turn supports the principles of fairness, robustness, and safety. It
is therefore clear that XAI is the essential foundation for truly responsible
AI.

To further illustrate the importance of XAI for RAI in practical contexts, we
present several timely use cases. We first showed how XAI is essential for
developing responsible GAI models that can explain their understanding of a
prompt and how they produced their outputs. Next, we turned to the medical
domain and highlighted how XAI can be utilised to develop RAI diagnostics
models that overcome historical biases and better serve humanity. Finally, we
explored the use of XAI to ensure that autonomous vehicles act responsibly,
highlighting how they can use XAI to communicate their decisions with
passengers, manufacturers, and legal authorities.

Based on our thorough review of cutting-edge research on XAI as the
foundational tool of RAI, we next presented a series of future research
directions informed by our lessons learned. It was identified that this
emerging field has much research potential, with opportunities present in
developing methods and metrics to quantify responsibility, further
improvements to XAI for RAI, development of systems that are responsible
across all key pillars of RAI, and lastly in the application of XAI techniques
to novel and exciting domains, including generative AI.

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