# Survey on AI Ethics: A Socio-technical Perspective

Dave Mbiazi  Polytechnique MontréalCanada<EMAIL_ADDRESS>,  Meghana Bhange  ÉTS MontréalCanada meghana-
<EMAIL_ADDRESS>,  Maryam Babaei  ÉTS MontréalCanada
<EMAIL_ADDRESS>,  Ivaxi Sheth  CISPA-Helmholtz Center for
Information SecurityGermany<EMAIL_ADDRESS>and  Patrik Joslin Kenfack
Mila, ÉTS MontréalCanada<EMAIL_ADDRESS>
(2018)

###### Abstract.

The past decade has observed a great advancement in AI with deep learning-
based models being deployed in diverse scenarios including safety-critical
applications. As these AI systems become deeply embedded in our societal
infrastructure, the repercussions of their decisions and actions have
significant consequences, making the ethical implications of AI deployment
highly relevant and important. The ethical concerns associated with AI are
multifaceted, including challenging issues of fairness, privacy and data
protection, responsibility and accountability, safety and robustness,
transparency and explainability, and environmental impact. These principles
together form the foundations of ethical AI considerations that concern every
stakeholder in the AI system lifecycle. In light of the present ethical and
future x-risk concerns, governments have shown increasing interest in
establishing guidelines for the ethical deployment of AI. This work unifies
the current and future ethical concerns of deploying AI into society. While we
acknowledge and appreciate the technical surveys for each of the ethical
principles concerned, in this paper, we aim to provide a comprehensive
overview that not only addresses each principle from a technical point of view
but also discusses them from a social perspective.

AI Ethics, Trustworthiness, Responsibility, Fairness, Accountability,
Interpretability, Privacy, Security

*All authors contributed equally

††copyright: acmlicensed††journalyear: 2018††doi: XXXXXXX.XXXXXXX††isbn:
978-1-4503-XXXX-X/18/06††ccs: Security and privacy Human and societal aspects
of security and privacy††ccs: Social and professional topics Computing /
technology policy††ccs: Computing methodologies Philosophical/theoretical
foundations of artificial intelligence††ccs: Computing methodologies
Artificial intelligence††ccs: Computing methodologies Machine learning

##  1\. Introduction

As AI becomes ubiquitous in our lives moving forward in this decade, focusing
on the ethical implication of AI is not just essential but extremely pressing.
Several ethical guidelines and principles have been released around the world
by governments (Smuha, 2019), organizations (Gianni et al., 2022), and
companies (Jobin et al., 2019). Among these ethical considerations, there are
common principles that should be promoted in the development of AI systems
(Lewis et al., 1997; Floridi et al., 2021; Hagendorff, 2020). These principles
form an initial consensus of features or components that should be embedded in
AI systems in order to make their use more socially acceptable. Figure 1
showcases the most common principles found in existing ethical guidelines
(Jobin et al., 2019). These principles include privacy and data protection, to
ensure privacy preservation of sensitive information about individuals
(Section 2); Safety and robustness, to promote the robustness and reliability
of AI systems in different real-world scenarios (Section 2); Transparency and
explainability: to uncover information about how the system works and explain
the decisions made (Section 3); fairness, which promotes bias-free and non-
discrimination in AI systems when used to make decisions in high-stake
scenarios (section 4); Responsibility and Accountability, to promote processes
and rules to enforce ethical considerations throughout the entire lifecycle of
AI systems to limit unintended outcomes (Section 5); Environmental impact, to
study the impact the growing deployment and use of AI systems might have in
the environment along with how these systems can be leveraged for
environmental protection (Section 6).

All these principles play a key role in fostering trust among all stakeholders
in the AI system lifecycle. In this paper, we walk through these principles
and present recent efforts, solutions, and regulations for implementing each
ethical guideline in AI systems.

Figure 1. Ethical principles of responsible AI

As this report focuses on a sociotechnical issue, we covered each guideline
from both social and technical perspectives. We cover the social impact and
value of each guideline and discuss technical contributions from the
literature to address them.

##  2\. Privacy and Data Protection

Ensuring the security and privacy of machine learning models has become a
crucial issue as they are widely used in various fields. In order to provide
trustworthy AI, it is important to include safety, privacy, and security in
their lifecycle. Based on some definitions, safety means reducing the
probability of expected and unexpected harm (Varshney, 2019).

Several studies (Chakraborty et al., 2018; Tramèr et al., 2016; Papernot et
al., 2017; Fredrikson et al., 2015; Hitaj et al., 2017) have indicated that
machine learning models are susceptible to attacks on their privacy and
security at different stages of their lifecycles. Since these models are
trained on vast amounts of data, model training is sometimes outsourced, or
pre-trained models are obtained from untrusted sources, which makes them
vulnerable to attacks during the training phase. Additionally, machine
learning models provided as a service contain valuable information about their
training data and hyperparameters, making them attractive targets for attacks
during the test and deployment phases (Xue et al., 2020). Attacks on privacy
commonly aim to compromise the confidentiality of various machine learning
model components, while security attacks target the model’s integrity and
availability.

###  2.1. Privacy and security attacks and defenses overview

Different attacks are possible depending on the model architecture and
attackers’ capabilities. In terms of security, attackers may aim to gain
access to the model, steal its information, or disrupt its normal functioning.
For example, an attacker may target a spam detector to make it unable to
classify spam correctly by poisoning training data (Barreno et al., 2010).

The attacks can be categorized into three main categories: black-box, partial
white-box, and white-box attacks, according to the attacker’s knowledge of the
machine learning model (Pitropakis et al., 2019). In black-box attacks, the
attacker has no information about the training dataset or model’s
architecture, and the only access is through querying the model; in white-box
attacks, the attacker has full access to the model and all information about
it, including the training dataset, model parameters, model architecture,
prediction vectors, etc. Partial white-box(gray-box) attacks stand between
these extremes, meaning the attacker has some information about the model’s
architecture and training data distribution. The most common security and
privacy attacks against machine learning models are described in this section.

Membership Inference. The membership inference is an attack in which an
attacker attempts to determine if a particular data sample $x$, is part of a
model $M$’s training dataset (Hu et al., 2022; Rigaki and Garcia, 2020; Baluta
et al., 2022). This attack is often carried out using black-box techniques to
query the model. Different querying techniques are used to optimize the attack
to gain more information about the membership of individual records in the
training dataset. One of the first Membership inference attacks, implemented
by (Shokri et al., 2017) which could achieve high accuracy in their inference,
was performed on Google and Amazon’s APIs that provide machine learning as a
service (MLaaS). (Quan et al., 2022) showed that having additional knowledge
about the model or training dataset distribution can improve the attack’s
success rate.

Model Inversion. In a model inversion attack, the adversary tries to get
information from the target model to reconstruct some representation of its
input dataset. The first category aims to generate an actual data
reconstruction (He et al., 2019). In contrast, the second group of attacks
tries to create class representatives or probable values of sensitive features
that may not belong to the training dataset (Fredrikson et al., 2014). Several
attacks have been implemented based on different assumptions. Some attacks
assumed to have information about data and sensitive features, and some had
query access to the model to get a prediction for an input $X$. Two main
categories of this attack are performed.

Property Inference. A property inference attack attempts to deduce information
about the characteristics of a training dataset that are not explicitly
represented in the features. Revealing such properties can result in privacy
breaches since they may be considered confidential. Property inference attacks
are designed to identify dataset-wide properties (Ateniese et al., 2015) or
detect common characteristics among a subset of the training data (Melis et
al., 2019). For instance, a classification model may be trained to distinguish
between criminals and non-criminals. An attacker can estimate the proportion
of men and women in the dataset by conducting a property inference attack.
This information was not meant to be disclosed and was learned by the model
unintentionally.

Model Extraction. In a model extraction attack (Tramèr et al., 2016), the
attacker aims to develop a model replicating the target model’s behavior. This
is typically done when the attacker lacks information about the target model’s
architecture and training dataset. To achieve this, the attacker generates a
training set for the attack model by sending queries to the target model and
uses the predictions generated by the target model as labels of its data
points. A successful model extraction attack, as shown by (Tramèr et al.,
2016), allows the attacker to produce a model that can be used for inference
or to extract information about the training dataset. However, if the attacker
has some knowledge about the training set’s distribution, the attack’s success
rate can be improved. Selecting the data to query the target model is a
critical aspect of the attack methodology, which significantly impacts the
accuracy and fidelity of the attack model.

Poisoning. In certain situations, a machine learning model designer may not
generate or thoroughly examine the data used to train the model. Generally,
this may occur when data generation is outsourced to third parties, or pre-
trained models are fine-tuned for a specific task. In such cases, the model
becomes vulnerable to poisoning attacks (Dalvi et al., 2004) that target the
training dataset. In a sample poisoning attack, the attacker injects malicious
data into the training dataset, causing the model to learn patterns unrelated
to the classification task specified for the model. These patterns can be
exploited during the inference phase as a backdoor, resulting in incorrect
decision-making by the model when provided with data containing these
malicious patterns (Biggio et al., 2012; Zhang et al., 2021).

Evasion. Evasion attacks (Huang et al., 2011) occurs when an adversary
attempts to cause a machine learning model to misclassify a data sample during
the inference phase. This attack typically occurs after the model is trained
and deployed. The attacker aims to generate a data sample similar to the
original ones but misclassified by maximizing a loss function based on their
attack objectives. There are two main categories of evasion attacks: targeted
and untargeted. While in a targeted attack, the adversary wants the
manipulated sample to be classified as a specific class, in an untargeted
attack, the attacker is not concerned with which class the manipulated input
is classified in. The manipulated data samples created in evasion attacks are
known as adversarial examples in the literature. Several techniques, such as
projected gradient descent, have been developed to generate adversarial
examples (Deng and Karam, 2020; Biggio et al., 2013; Xu et al., 2020a).

Manipulation. Manipulation attacks occur when the attacker tries to explain
the model decisions based on some criteria that are not made. Here,
explanations help users understand complex models’ behavior (explanations are
described in section 3). But to gain the users’ trust, it is not enough to
have an explanation; it should be robust too. Manipulation attacks are the
type of attacks that exploit the fragility of the explanations. It means that
while the model makes decisions based on some criteria if manipulated,
explanations can pretend the model is using other, more reasonable measures
(Ghorbani et al., 2019). This type of attack is performed to achieve several
goals. One of its main objectives is fairwashing (Aïvodji et al., 2019; Anders
et al., 2020; Fukuchi et al., 2020), which means the model designer has
manipulated the explanation methods to hide the unfairness of their model’s
decisions (Fairness is described in detail in section 4).

###  2.2. An Overview of Defense Techniques

Based on the definition of safety for a machine-learning model (Varshney,
2019), a machine-learning model should be trained and released to be robust
against different kinds of uncertainty; in other words, in case of an
accident, the model should be able to continue its expected normal behavior.
To prevent safety problems, first, the designer should be able to specify the
correct objective function and have a method to evaluate it; second,
sufficient data, time, and infrastructure should be available to train and
evaluate the model (Amodei et al., 2016). One example of the main attributes
of AI safety is robustness to distributional shift (Amodei et al., 2016). If
one of these attributes is lacking, the model will be vulnerable to different
attacks against their privacy and security.

Researchers have proposed some defense strategies in response to security and
privacy attacks. Some of the most common defense techniques are described here
in summary. $k$-anonymity (Sweeney, 2002) is a technique usually used to
prevent privacy attacks. It guarantees that each data instance is
indistinguishable among at least $k-1$ other data points. However, it may not
be sufficient to prevent identification when additional information or
external knowledge is available. Differential privacy is one of the most
popular defenses against privacy attacks on machine learning models (Dwork et
al., 2014). Differential privacy provides a structural approach to quantify
the level of privacy risk added to the data upon using it in model training.
Differential privacy, based on (Dwork et al., 2014), is formalized as follows:

###### Definition 2.1.

A randomized function, denoted as $K$, provides $\epsilon$-differential
privacy if for all data sets, $D_{1}$ and $D_{2}$, differing by at most one
element, and for all subsets, $S\subseteq Range(K)$, the following condition
holds:

$Pr[K(D_{1})\in S]\leq exp(\epsilon)\times Pr[K(D_{2})\in S]$

Based on this definition, any single record of the dataset, if removed, will
not add substantial influence on the output of the function $k$, which has
been trained on that dataset. Parameter $\epsilon$ shows the degree of privacy
protection; The smaller the $\epsilon$ is, the function provides the higher
degree of privacy protection, and if $\epsilon$ approaches zero, the function
provides absolute confidentiality.

Differential privacy can be applied in different phases of the machine
learning lifecycle. It is possible to use it to make training data private
(Zhang et al., 2012), or during the training phase, using differentially
private training techniques like PATE (Papernot et al., 2018) and
DPSGD111Differentially private stochastic gradient descent (Abadi et al.,
2016). Furthermore, model owners can apply differential privacy techniques
after training the model in the inference phase in order to make the model
respond to the queries without privacy leakage (McSherry and Talwar, 2007).

In addition to defenses against privacy attacks, some techniques have been
suggested to protect the model against security attacks. As a countermeasure
to the evasion attacks, adversarial training (Goodfellow et al., 2014b) has
been suggested to make the model robust by exposing it to some adversarial
examples in the training phase, allowing it to learn correct behavior against
them.

Applying each of the above-mentioned defense techniques is a critical decision
that has to be made based on the model owners, data owners, and model users’
priorities. Increasing the privacy and robustness of the models comes with the
cost of reducing their accuracy. Here is where the stakeholders should decide
to what degree they are going to make their models immune against privacy and
security attacks and, at the same time, how much accuracy loss they can afford
to achieve this degree of immunity.

##  3\. Transparency and Explainability

As mentioned in section 2, it is necessary for AI users to understand how the
model works and makes its decisions. Explainability, a crucial aspect of
machine learning, bridges the gap between predictive accuracy and human
understanding. It has emerged as a critical aspect of machine learning models,
addressing the need to understand the reasoning behind their decisions. While
predictive accuracy has long been the primary focus, the growing demand for
transparency in models has motivated academic research in interpreting black-
box neural networks (Rudin, 2019a). Explanations can potentially allow the
deployment of machine learning models while maintaining high ethical standards
(McDermid et al., 2021a).

In various areas of AI applications, the need to have explanations for AI
model decisions has raised and played an important role in motivating
researchers to focus on making complicated models explainable.

For example, in medical applications, using an explainable model for patient
screening not only identifies high-risk individuals but also helps understand
disease causes like cancer (Binder et al., 2021; Kobylińska et al., 2022).
This model could additionally provide insights into predictive factors,
relationships between risk elements, and significant contributors to a
diagnosis, such as biomarkers, genetic predispositions, lifestyle factors, or
environmental exposures (Idrees and Sohail, 2022; Adeoye et al., 2023; Sobhan
and Mondal, 2022; Sheth and Kahou, 2023b). Interpretability in AI proves
crucial in the finance sector (Bracke et al., 2019; Hoepner et al., 2021),
aiding in understanding loan rejections, credit score calculations, and fraud
detection. This helps the stakeholders to identify biases, errors, and
discriminatory practices, ensuring transparency and accountability (Li, 2021;
Rizinski et al., 2022). Compliance and regulatory bodies also benefit from
interpretability, as financial institutions must elucidate AI-driven
decisions. Furthermore, explainability may enhance AI model accuracy and
bolster customer trust (Espinosa Zarlenga et al., 2022). Therefore,
interpretability is not merely desirable but necessary in financial AI
applications. Further, explainability enhances understanding of customer
behavior, enabling personalized offerings and improved customer experience
(Ameen et al., 2021; Xu et al., 2020b). It fosters customer satisfaction and
loyalty by tailoring products and services to meet customer needs. Transparent
explanations educate customers on AI-driven decisions, alleviating concerns
and fostering understanding, thereby building trust and strengthening customer
relationships (Sahal et al., 2022; Maree and Omlin, 2022). The case of
AlphaGo’s (Silver et al., 2016) ”move 37” exemplifies AI’s potential to
surpass human intuition. This neural network model, trained to play Go, made a
move that initially perplexed experts due to its deviation from traditional
strategies. However, as the game unfolded, it became clear that AlphaGo
foresaw the potential of this unconventional move, which proved pivotal in its
ultimate victory. This case underscores the need for techniques to explain AI
decision-making processes.

Regulators and policymakers recognize the need for mechanisms to shed light on
AI models’ inner workings, enabling stakeholders to understand and assess the
justifiability of AI-driven decisions. Regulatory bodies across industries
emphasize transparency, fairness, and non-discrimination in AI systems.
Explanations play a pivotal role in meeting these regulatory requirements,
elucidating how AI models arrive at their predictions or decisions. Legal and
ethical concerns surrounding AI technologies necessitate the integration of
explanations into AI systems.

###  3.1. Stakeholders in XAI

Most experts agree that explainability and/or interpretability are crucial for
artificial intelligence (AI) and machine learning systems. However, there
isn’t a universal understanding of what explainable and interpretable mean. As
a result, analyzing the opinion of stakeholder communities surrounding
explainable AI is of great importance. The majority of stakeholder related
debates merely distinguish between end users and system developers. This can
be seen in (McDermid et al., 2021b) through the following points

  * •

Prediction-recipients who are directly impacted by the ML-based system’s
predictions despite not using it themselves since the prediction is typically
mediated by an expert user.

  * •

End users who utilize the ML-based system directly, and are consequently
directly impacted by it.

  * •

Expert users who directly use the ML-based system though they are not
immediately impacted by its forecasts. Since they could be held responsible
(both legally and ethically) for the results of predictions put into action,
they are indirectly impacted.

  * •

Attorneys and the courts are interested in determining who is responsible for
damage caused by an ML-based system.

  * •

The Financial Services Authority, the Vehicle Certification Agency, and the
Medical and Healthcare Products Regulation Agency are regulatory bodies that
are not the system’s immediate users nor its direct beneficiaries. They,
however, safeguard the interests of prediction recipients and end users
(Burton et al., 2020).

A compressed version of the latter was suggested by (Hoffman et al., 2021),
where the authors show a Users Chart of stakeholder groups as developed by the
Defense Advanced Research Projects Agency (DARPA). They summarized some
Sensemaking Needs and Explanation Requirements for some Stakeholder Groups
(such as Policy Makers and Regulators, Developers: AI Experts, test Operators,
and lastly Operations: Military, Legal, and Transportation).

###  3.2. Properties of Explanations

Explanations, in order to be able to respond to their stakeholders’ needs,
should involve several properties:

Clarity refers to the quality of understanding the explanation. It involves
presenting explanations in a clear, concise, and interpretable manner. A clear
explanation avoids unnecessary jargon or technical complexity, making it
accessible to the intended stakeholders. It should be structured and organized
to facilitate understanding and promote effective communication of underlying
concepts or factors.

Fidelity is a crucial property of explanations, emphasizing the importance of
providing correct and truthful information (Mamalakis et al., 2022; Papenmeier
et al., 2019). An accurate explanation aligns with the underlying data or
model, ensuring that the explanation reflects the actual reasons and factors
influencing a decision. Explanations must not misrepresent or distort the
information but rather provide a faithful representation of the relevant
aspects of the data.

Completeness relates to the extent to which an explanation provides a
comprehensive account of the relevant factors or features contributing to a
decision or result (Munoz et al., 2023). A complete explanation includes all
the necessary information, leaving no significant gaps or missing components.
It should cover both the primary factors and any secondary or indirect factors
that may have influenced the outcome. A complete explanation helps to avoid
ambiguity or misunderstanding by providing a holistic view of the situation.

Consistency emphasizes an explanation’s coherence and logical consistency
(Pillai and Pirsiavash, 2021). A consistent explanation should not contain
contradictory statements or conflicting information. It should maintain a
coherent narrative that aligns with the underlying data or model. Consistency
ensures that the explanation is internally coherent and does not introduce
confusion or ambiguity in the interpretation of the provided information.

Causality explores the causal relationships between variables or factors. A
causal explanation seeks to identify and explain the cause-effect
relationships that lead to a particular outcome or prediction (Carloni et al.,
2023). It provides insights into the mechanisms and processes that drive the
observed phenomena. Causal explanations help to uncover the underlying reasons
behind a decision or result, shedding light on the factors that directly or
indirectly influence the outcome.

Transparent relates to the openness and accessibility of an explanation. A
transparent explanation is understandable, interpretable, and accessible to
the intended audience. It avoids unnecessary complexity or obfuscation and
allows individuals to examine and verify the reasoning behind a decision or
outcome. Transparency promotes trust, accountability, and scrutiny, enabling
individuals to assess the reliability and fairness of the provided
explanation.

Contextuality considers an explanation’s relevance and contextual
appropriateness (Anjomshoae et al., 2019). A contextual explanation takes into
account the specific circumstances, background knowledge, and contextual
factors that may impact the interpretation and understanding of the
explanation. It adapts the level of detail, language, or content to suit the
specific context or audience, ensuring that the explanation is meaningful and
relevant within the given context.

Granular explanations can vary in their level of granularity. They can range
from high-level summaries to detailed explanations at the feature or instance
level. The granularity of explanations should align with the users’ needs and
their level of understanding, striking a balance between simplicity and depth.

User-Centric (Liao and Varshney, 2021) emphasizes the importance of tailoring
explanations to the needs, preferences, and cognitive abilities of the
intended users. A user-centric explanation is designed to effectively
communicate information to the target audience, taking into account their
background knowledge, expertise, and information processing capabilities. It
considers the user’s perspective and provides explanations adapted to their
specific requirements and level of understanding. User-centric explanations
enhance the usability and utility of the provided information.

###  3.3. Explainability Techniques

####  3.3.1. Classical methods

A subset of algorithms that inherently produce interpretable models is a
straightforward approach to achieving interpretability.

Logistic regression(Hastie et al., 2009; Došilović et al., 2018; Yang and Wu,
2021; Modarres et al., 2018), an extension of linear regression, models binary
outcomes based on input variables. Extensions like ridge, lasso, and elastic
net regression improve performance and interpretability. Decision trees
(Holte, 1993; Odense and d’Avila Garcez, 2017; Zhou et al., 2003) are
intuitive models that split data based on input features, and they capture
non-linear relationships. Decision rules provide transparent decision-making
representations. The RuleFit algorithm (Friedman and Popescu, 2008) combines
decision trees and linear regression, capturing complex interactions and
incorporating interpretable linear components. However, these models’
simplicity may limit the capturing complex relationships and handling of high-
dimensional data. The choice of model depends on the problem, data
characteristics, and desired interpretability level.

While these models offer interpretability, it is important to note that their
simplicity and transparency come at the cost of potential limitations in
capturing complex relationships and handling high-dimensional data.
Additionally, the choice of model depends on the specific problem, data
characteristics, and the desired level of interpretability.

####  3.3.2. Post Hoc methods

With the emergence of deep learning and the need for highly accurate large
neural networks, local explanations post-hoc explanations have emerged to be
highly useful. Instead of explaining the entire model, post-hoc methods
explain a particular decision.

Individual Conditional Expectation (Goldstein et al., 2015) curves provide a
fine-grained view of how changing a specific feature affects the model’s
prediction for an individual instance. Unlike PDP, which shows the average
effect, ICE curves show the predicted outcome for each instance as the feature
value varies. Feature importance based explanations (Simonyan et al., 2014;
Carter et al., 2019; Sundararajan and Najmi, 2020; Lundberg and Lee, 2017)
such as Local Interpretable Model-agnostic Explanations (LIME) (Ribeiro et
al., 2016) explains individual predictions by approximating the complex model
locally with a simpler, interpretable model. It generates a surrogate model
that is more easily explainable and uses it to understand the reasoning behind
the prediction for a specific instance. Similarly, Shapley values
(Sundararajan and Najmi, 2020) are an attribution method that fairly allocates
the prediction value to individual features. SHAP (Lundberg and Lee, 2017) is
a computation method for Shapley values that combines the individual feature
contributions to explain the model’s predictions. It not only provides
insights at the feature level but also proposes global interpretation methods
by considering combinations of Shapley values across the data. Scoped rules,
also known as anchors (Ribeiro et al., 2018), are rule-based explanations that
identify specific feature values that anchor or lock a prediction in place.
LORE (LOcal Rule-based Explanations) (Guidotti et al., 2018) creates
interpretable rules by using two types of perturbations, in a genetic
algorithm, to find the minimal changes that would alter the prediction.
Counterfactual explanations (Mothilal et al., 2020; Sokol and Flach, 2019;
Verma et al., 2021) explore what changes in the feature values would be
required to achieve a desired prediction outcome. Counterfactual explanations
help understand the model’s decision boundaries and provide insights into how
different features impact the predicted outcome by identifying the necessary
modifications to the features. Concept attribution attributes the final
prediction of a model to align with high-level concept of the input (Kim et
al., 2018; Zhou et al., 2018; Yeh et al., 2020).

####  3.3.3. Ante-hoc methods

(Rudin, 2019b) highlights the main challenges of explainable models, stating
the importance of learning explainable features during model training itself.
Ante-hoc explainability methods involve learning of concepts during the
training phase. Early concept-based models that involved the prediction of
concepts prior to the classifier were widely used in few-shot learning
settings (Kumar et al., 2009; Lampert et al., 2009). Unsupervised concept
learning methods (Alvarez-Melis and Jaakkola, 2018; Sarkar et al., 2022) use a
concept encoder to extract the concepts and relevance network for final
predictions. Although these methods are useful when pre-defined concepts are
absent, they do not enable effective interventions. Concept whitening (Chen et
al., 2020) was introduced as a method to plug an intermediate layer in place
of the batch normalization layer of a CNN to assist the model in concept
extraction. (Koh et al., 2020; Espinosa Zarlenga et al., 2022; Sheth and
Kahou, 2023a) extend the idea by decomposing the task into two stages: concept
prediction through a neural network from inputs, and then target prediction
from the concepts. Such concept-based models have been further utilized to
facilitate human-model interaction (Sheth et al., 2022), a useful feature for
model editing.

###  3.4. Large Language Models and Explainability

In a rising number of different tasks, large language models (LLMs) such as
LLaMA-2, BERT, GPT-3 and GPT-4 (Touvron et al., 2023; Kenton and Toutanova,
2019; Brown et al., 2020; Bubeck et al., 2023) have shown outstanding
performance. However, these models have become known as black boxes due to
their inability to be understood clearly. Due to their opacity, they are no
longer useful in high-risk fields like medicine and policymaking.
Explainability is essential for LLMs since it enables users to comprehend how
the model generates its predictions (Singh et al., 2023; Peng et al., 2023).

In this line, (Cífka and Liutkus, 2023) developed a model-agnostic explanation
technique based on tracking the model’s predictions as a function of the
number of context tokens available for causal (autoregressive) language
models. Each time a new token is introduced, there is an increment in context
length, and the authors proposed a metric, _Differential Importance Score_ to
quantify this change. These scores appear to have the ability to find long-
range dependencies (LRDs), which is particularly intriguing because they are
intended to highlight information not already covered by shorter contexts,
unlike attention maps, for instance.

Deep neural networks are trained to recognize very particular structural and
perceptual attributes of inputs. Techniques for locating neurons that react to
certain idea categories, such as textures, are readily available in computer
vision. Nevertheless, the scope of these methods is constrained, labeling only
a tiny portion of the neurons and behaviors in every network. To solve this,
(Hernandez et al., 2021) proposed MILAN (mutual-information-guided linguistic
annotation of neurons) which generates descriptions (that capture categorical,
relational, and logical structure in learned features) of neuron behavior in
vision models using patch-level information about visual characteristics.

Another approach to black-box language models is through text modules. (Singh
et al., 2023) introduced the Summarize and Score (SASC) approach that takes a
text module as input and outputs a natural language explanation of the
module’s selectivity coupled with a reliability score. They show better
interpretability for LLMs may be attained by the SASC, which can enhance
automated analysis of LLM submodules such as attention heads. (Bills et al.,
2023) offer a SASC-like technique for explaining individual neurons in an LLM
by forecasting token-level neuron activations. They used an automated approach
to solve the issue of scaling an interpretability approach to each neuron in
an LLM which is expected to assess the trustworthiness of the models before
the deployment. The method clarifies how textual patterns trigger neuron
activation through; Explaining neuron’s activation using GPT-4, Simulating
activations conditioned on the explanation, and Scoring the explanation (Singh
et al., 2022; Zhong et al., 2022).

Nevertheless, (Zhao et al., 2023) provide a classification of explainability
methods and a systematic summary of strategies for elucidating Transformer-
based language models. These techniques are categorized according to the LLM
training approaches: the traditional fine-tuning method and the prompting-
based method. They also delve into metrics for assessing the quality of
generated explanations and explore how these explanations aid in
troubleshooting to enhancing model performance.

##  4\. Fairness and Equity

As discussed in sections 2 and 3, adding transparency to the AI systems, makes
different stakeholders equipped to find the system’s deviations from desired
behavior. Fairness is one of the main requirements in which many AI systems
fail. Fairness can be defined as the absence of any prejudice or favoritism
towards an individual or a group of individuals based on their inherent
acquired characteristics such as race, gender, religion, etc.

There are numerous examples of AI applications that exhibit unwanted
discriminatory behaviors. For instance, the Compas system: a software used in
the US to assess the recidivism risk of defendants. Julia Angwin investigated
(Angwin et al., 2016) the software and showed that compared to white
defendants, black defendants are predicted as twice as likely to re-offend
although they do no subsequent offenses; Another example is the AI-based
hiring system (Jeffrey, 2018) used by Amazon for assessing the resumes of job
applicants. It was observed that the evaluations assigned to applicants’
resumes exhibited gender bias. These examples of AI bias have triggered the
need for developing more inclusive AI models, and to take action to audit and
overcome the potential bias that might be embedded in AI systems. A biased AI
system does not only have a negative impact on the end-users but the
organization deploying the system can suffer reputation damage, user distrust,
and juridical liability. Therefore, developing and deploying bias-free systems
significantly contributes to making their use more socially acceptable.
Initial important steps to mitigate these issues focused on mathematically
defining and quantifying bias AI systems.

###  4.1. Definitions of Bias and Fainess

The concept of fairness has a variety of definitions depending on the domain
considered (Shaheen, 2021; Creamer et al., 2021; Laurim et al., 2021). It can
be defined according to _political philosophy, areas of education, in the
legal domain and according to the general public’s perception_ (Kenfack et
al., 2021; Mehrabi et al., 2022). Unfairness or discrimination is divided into
two categories:

  * •

Disparate treatment: Which is defined as intentionally treating individuals
differently based on their membership in a demographic group (direct
discrimination) (Pessach and Shmueli, 2022)

  * •

Disparate impact: which is negatively affecting members of a demographic group
more than others even if by a seemingly neutral policy (indirect
discrimination) (Pessach and Shmueli, 2022)

In fact, AI systems can exhibit disparate treatment if the model heavily
relies on sensitive attributes to make predictions. However, discriminatory
outcomes from AI systems are generally not intended, but due to different
sources of bias, the system will provide disparate outcomes over different
demographic groups considered.

###  4.2. Source of bias and Fairness notions in AI

Unfairness in machine learning originates from three main sources of biases:
biases due to the data, those from the algorithm, and user interaction.
Machine learning systems and artificial intelligent systems are data-driven
since they rely on data in order to be trained, making them an integral part
of the system. As a result, if the algorithm is trained on a biased dataset,
then these biases are likely to be portrayed in the model’s outcome.

There are a wide variety of sources of bias in data and some important ones
are highlighted here as reported by (Mehrabi et al., 2022).

Omitted-Variable Bias (OVB). OVB occurs when the dataset fails to incorporate
one or more relevant variables/features. The authors in (Wilms et al., 2021)
showed that in a model which explains the relationship between dependent and
independent variables, omitting a relevant variable leads to biased estimates.
They also showed that OVB leads to statistical relationships that can be
indicated as larger, smaller, or opposite to their actual value, which
inflates error rates.

Measurement Bias. Also called Reporting or Recall bias, which is a result of
how important features are measured (Mehrabi et al., 2022).

Aggregation Bias. It occurs when it is erroneously believed that individual
data points follow the trends found in aggregated data. Aggregation bias
frequently happens in research because it is sometimes assumed incorrectly
that patterns that exist at an aggregate level must also appear at an
individual level. Sadly, as the preceding illustration showed, this is not
always true. A study’s results may derive incorrect conclusions due to
aggregate bias, which is misleading. This kind of bias is especially damaging
when it comes to the correlations between different variables.

Representation Bias. It occurs when certain segments of the target population
are underrepresented in the training data and, consequently, don’t generalize
well. Data representation bias may be due to biases introduced after the data
was obtained, either historically, cognitively, or statistically, or it may
result from how (and where) the data was initially collected (Shahbazi et al.,
2023; Kenfack et al., 2022). Selection bias can cause representation bias,
which occurs when just a small percentage of the population is sampled, the
population of interest has changed, or the population of interest differs from
the population used to train the model. For instance, if a poll measuring the
illegal drug use of teenagers only includes high school students and leaves
out homeschooled children or dropouts, it may be biased (Suresh and Guttag,
2021). The skewness of the underlying distribution is another possible
explanation for representation bias. Let’s say that adults aged 18 to 60 years
old are the target demographic for a specific medical dataset. Within this
community, there are minority groups; for instance, pregnant women may
constitute only 5% of the target population. Because the model has fewer data
points to learn from for the group of pregnant people, it is susceptible to
being less robust even with perfect sampling and an identical population
(Suresh and Guttag, 2021).

Algorithmic Bias. Another common source of bias can be the algorithm itself.
The machine learning model, trained on a biased dataset, can reproduce and
amplify the biases in the model’s output. Even if trained using an unbiased
dataset, machine learning algorithms throughout their architecture have the
ability to demonstrate biased behavior (Mehrabi et al., 2022). It arises
solely as a result of the design characteristics and model architecture, such
as the choice of the regularizer, and loss functions.

Having identified the origins of bias in the AI lifecycle, auditing AI systems
for bias assessment requires metrics to quantify them and to evaluate the
efficiency of intervention methods. In this regard, various fairness metrics
(definitions) have been defined to capture different aspects of fairness.

###  4.3. Fairness definitions.

Fairness notions can be categorized into group, individual, and subgroup types
(Alves et al., 2023). Group fairness suggests that different groups are
treated equally. In its widest sense, group fairness splits a population into
groups defined by _protected attributes/features_ (such as gender, religion,
caste) and desires some statistical quantities to be equal across different
groups. (Mehrabi et al., 2022; Weerts et al., 2023) discuss a wide variety of
group fairness metrics and the following are the most significant ones:

Demographic Parity. This metric seeks to guarantee that a model’s predictions
are not related to one’s belonging to a vulnerable group. Demographic parity
refers to equal selection rates for each group in the binary classification
scenario (Pagano et al., 2023; Alves et al., 2023). Equal selection, for
instance, in the context of a resume screening approach, would imply that the
proportion of candidates chosen for a job interview should be the same across
groups.

Equalized Odds. This metric aims to guarantee that a machine learning model
works equally effectively for all groups. It is more stringent than
demographic parity because it demands that groups have the same true positive
and false positive rates as well as independent predictions from the machine
learning model regardless of membership in sensitive groups (Pagano et al.,
2023; Birzhandi and Cho, 2023). This distinction is crucial because even if a
model achieves demographic parity (i.e., its predictions are not dependent on
a subject’s membership in a sensitive group), it may nevertheless provide more
false positive predictions for a particular group.

Equal Opportunity. This can be understood as requiring that both protected and
unprotected group members have an equal chance of being allocated to a
positive result if they belong to a positive class (Mehrabi et al., 2022). In
other words, the equal opportunity definition states that the true positive
rates for protected and unprotected groups should be equal.

Disparate Impact (DI). This notion can also be viewed as the ratio between the
two groups’ rates of accurate predictions and so a high value of the ratio
guarantees that the percentage of accurate predictions is consistent across
groups. Nevertheless, one of the major drawbacks of _disparate impact_ and
_demographic parity_ is that a perfectly accurate classifier may be viewed as
being unfair when the proportion of real positive outcomes of the various
groups are noticeably different (Pessach and Shmueli, 2022).

Individual Fairness. In contrast to group fairness, individual fairness is
focused on how each individual is treated (Li et al., 2021). This notion
requires similar individuals to receive similar outcomes from the model (Dwork
et al., 2012). Individual fairness is beneficial because it is a highly
specific way of defining fairness and also because people tend to care more
about individuals than large groups (Barocas et al., 2019). The main challenge
in applying this notion is the difficulty of defining the distance metric over
individuals with respect to the task at hand. (Dwork et al., 2012) assumed
such distance metric could be provided by domain experts, which could be hard
to define in practice.

Fairness Through Unawareness. This notion states that a model is fair as long
it is not trained using the sensitive attributes (Mehrabi et al., 2022).
However, a significant weakness of this notion is its failure to consider non-
sensitive features that may correlate with sensitive ones. When the model uses
these non-sensitive features as proxies for the unused sensitive features, it
can result in discriminatory outcomes(Corbett-Davies and Goel, 2018).

Counterfactual fairness. It concerns the root causes of differences. A
sensitive trait would be replaced in practice, affecting everything that
occurred due to that sensitive feature down the line (Kusner et al., 2017). In
the hiring scenario, one would alter a sensitive attribute, such as race, if
counterfactual fairness were applied. As a result, subsequent outcomes should
not be altered. Based on the counterfactual, the decision of a classifier
whether to hire the candidate should remain the same.

Rawlsian Min-Max fairness. This notion promotes maximizing the utility of the
worst-case groups. This fairness notion is derived from John Rawls’s (Rawls,
1971) notion of distributive justice, which defines the right (fair) decision
as the one that is of the greatest advantage to the least disadvantaged
members of society. In AI systems, this notion is enforced by designing models
that maximize the performances (e.g., accuracy) of the demographic group with
the worst performance (Lahoti et al., 2020). This metric however inherits the
critiques of Rawlsian distributive justice, in particular the difficulty of
defining the least advantaged subgroups when they are not provided.

###  4.4. Fairness enhancing methods

Fairness-enhancing methods are grouped into three main categories based on the
stage of the pipeline where the fairness constraint is enforced, i.e., at the
data level before training the model(pre-processing techniques), during the
model training (in-processing techniques), or after training the model (post-
processing techniques).

Pre-processing techniques. A model that relies on sensitive attributes (e.g.,
gender, race, nationality) to make predictions can lead to discrimination or
unfair results. Pre-processing techniques are used to remove the influence of
sensitive attributes from the data before training the model. The main
advantage of these techniques is that they are model-agnostic. The transformed
dataset or representation learned can then be used in downstream tasks
(classification, regression, etc) without any change to provide ”fairer”
outcomes. We group approaches to mitigate biases at the data level into three
main categories:

  * •

Fair representation learning: learn a fair representation of the data that
obfuscates information about the sensitive attributes (Zemel et al., 2013;
Lowd and Meek, 2005; Kenfack et al., 2023b; Madras et al., 2018; Song et al.,
2019; Beutel et al., 2017).

  * •

Dataset transformation: Modify the training data by relabeling or reweighing
data points (Kamiran and Calders, 2012) or apply data augmentation by
interpolating samples from different group (Chuang and Mroueh, 2021).

  * •

Sampling: find a distribution close to the empirical distribution of the
dataset subject to fairness constraints (Kamiran and Calders, 2012).

In-processing techniques. These techniques are used when we have access to the
model training, and it is not costly to retrain an existing model. In a
nutshell, the loss function is transformed to add a loss/regularization term
that penalizes the model’s disparities across groups. Therefore, the model is
forced to optimize for accuracy and fairness. The classification problem
becomes a constrained optimization problem where the goal is to minimize the
classification error (maximize the accuracy) while satisfying a given fairness
constraint. However, this optimization problem is nonconvex and difficult to
enforce. Therefore, existing in-processing techniques are reformulated in
different ways or dual problems are solved. They can be grouped as follows:

##### Reduction approach

The Exponentiated Gradient (Agarwal et al., 2018) or AdaFair (Iosifidis and
Ntoutsi, 2019) approaches for fairness transforms any binary classification
problem into a cost-sensitive classification problem, that can yield a
randomized classifier having the lowest error while satisfying fairness
constraints.

##### Adversarial-based approach

Adversarial network is a method that involves two competitive neural networks,
commonly used in generative models like GANs (Generative Adversarial Networks)
(Goodfellow et al., 2014a). This method is also applied to mitigate bias. A
popular application is Adversarial debiasing (Zhang et al., 2018). It involves
an adversary network that tries to predict the sensitive attribute while the
classifier tries to defeat the adversary, thus enforcing the independence of
the outcome and sensitive attribute.

##### Regularization-based approach

Regularization is generally used in ML to prevent overfitting by penalizing
the model’s weights using $L_{1}$ or $L_{2}$ norms (Tian and Zhang, 2022). A
similar technique can be employed to add regularization terms to the loss
function to penalize the model for disparities over demographic groups
(Kamishima et al., 2011; Bechavod and Ligett, 2017a, b; Zafar et al., 2017;
Woodworth et al., 2017; Calders and Verwer, 2010; Berk et al., 2017). For
instance, (Kamishima et al., 2011) introduced prejudice remover regularizer, a
regularization term for fairness that minimizes the mutual information between
the model’s output and the sensitive attributes.

##### Sampling-based approach

Bias can be mitigated using oversampling (Yan et al., 2020; Rančić et al.,
2021) or subsampling (Roh et al., 2020; Celis et al., 2016). For instance,
FairBatch (Roh et al., 2020) is a batch selection process that enforces a
given fairness metric by sampling mini-batches in a way to transform the
Empirical Risk Minimization (EMR) problem into a weighted EMR to incorporate
fairness constraints.

Post-processing techniques. This group of methods treats the model as a black
box and enforces fairness constraints over the model’s output. Most existing
post-processing methods consist of post hoc modification of the model’s
outputs in order to satisfy a given fairness metric (Hardt et al., 2016;
Corbett-Davies et al., 2017; Menon and Williamson, 2018; Dwork et al., 2018).
In particular, (Hardt et al., 2016) formalized an optimization problem over
the model’s output to derive a classifier that satisfies the fairness
constraint while minimizing the classification loss. The derived classifier
depends on four parameters that measure the probability of positive outcomes
given the current classifier output and the sensitive attribute. The
optimization problem is thus defined as a constrained linear optimization
problem. When the model output is continuous (a score function) the derived
classifier is based on a threshold of each demographic group such that it
maximizes the classification loss while satisfying fairness constraints, i.e.,
equal opportunity and equalized odds. Similar methods are proposed in the
literature, and they differ mainly in the way the optimization problem is
defined.

Pre-In-Post process: where should we enforce fairness? Each group of fairness-
enhancing methods has its pros and cons. There are different settings where
they can be applied and settings where their use is more challenging or not
possible. We summarize the advantages and disadvantages of each group of
methods as follows:

  * •

Pre-processing methods can work with any type of model and machine learning
tasks. As the fairness intervention is done at the data level, the downstream
task can be of any type, however, it becomes difficult to control the
tradeoffs, and the algorithmic bias that might arise in the downstream task is
not controlled.

  * •

In-processing methods allow control over the fairness-accuracy tradeoff that
the model can achieve. Having access to the optimization problem with fairness
constraints provides more flexibility in the tradeoffs, however, there is
little flexibility over the type of models used, i.e., the constraint
optimization is model-specific.

  * •

Similarly to pre-processing techniques, post-processing methods can be applied
to any type of model (classifier), which is treated like a black box. The
output of the model is modified to satisfy a given fairness metric. However,
changing the model’s output comes at a significant cost of accuracy. Moreover,
these methods can yield unfair outcomes against certain individuals as the
model output is changed to satisfy a certain fairness metric.

Overall, as shown by Friedler et al. (2019) there is no consensus in the
literature about which group of methods performs best. None of the methods
consistently outperforms others, and their performances depend on the fairness
metric and datasets.

##  5\. Responsibility, Accountability, and Regulations

As it was demonstrated in previous sections, AI systems are generally complex
”black-boxes” sociotechnical systems that can sometimes produce unintended
outcomes, which have raised issues of accountability establishment when
something goes wrong. Moreover, understanding how new technologies intertwine
with the upcoming AI regulations and how they reflect on AI ethics could go a
long way. In this section, we delve into the ethical considerations of AI and
the current landscape of AI regulation.

###  5.1. Existential Risks of AI

The existential risks of AI are becoming increasingly concerning as
advancements in the field continue to evolve (Hendrycks and Mazeika, 2022;
Hendrycks et al., 2023). The common approach with new technologies is to
implement them first, then address any significant issues that arise, making
adjustments as necessary. However, this approach may not be suitable for
advanced AI, as early missteps in directing these systems could prevent later
adjustments, potentially leading to catastrophic outcomes (Torres, 2019).
Examining history, technological advancement during the Industrial Revolution
completely transformed people’s lives and brought about profound changes for
humanity. This also yielded sociotechnical problems that needed regulations.
For instance, the invention of the first gas-powered automobile lacked the
rules and technologies we now take for granted, like traffic signs and
automated lights. Pedestrians and drivers had to watch out for themselves for
safety. During this transformative period, legislators responded reactively by
instituting rules to govern personal car usage. Few events during the
Industrial Revolution have the same transformative impact as advancements in
AI (Ammanath, 2022).

These advancements in AI could potentially amplify existing catastrophic
risks, such as bioterrorism, the spread of disinformation leading to
institutional dysfunction, misuse of centralized power, nuclear and
conventional warfare, other coordination failures, and unforeseen risks
(Hendrycks et al., 2023). This necessitates a proactive approach where
potential problems are anticipated and resolved well in advance, preserving
our ability to make corrections and avoid irreversible consequences.

Shortly after the development of advanced AI, we are likely to encounter AI
systems that significantly outperform humans in most cognitively demanding
tasks (Hadfield-Menell and Hadfield, 2019). These include tasks that have a
substantial impact on the world, such as technological development,
social/political persuasion, and cyber operations (Hendrycks et al., 2023). If
there is a conflict of goals between advanced AI systems and humans, it is
likely that the AI will outperform or outmaneuver humans to prioritize their
objectives. Misdirected advanced AI could limit humanity’s ability to make
corrections. They could determine that their current goals would not be met if
humans redirected them towards other objectives or deactivated them.
Consequently, they would take steps to prevent such interventions.

###  5.2. Accountability

We have seen in previous sections that AI systems can sometimes go wrong, for
example, gender bias in the Amazon hiring system, or adversarial environments
that mislead a deep neural network model. In general, when something goes
wrong in an organization or a company, someone is held accountable for it.
However, AI systems cannot solely be responsible for unpredicted outcomes. A
question that naturally arises when AI systems provide outcomes that have
adverse effects or harm individuals, is who is accountable for the issues.
Accountability is a well-studied notion in different fields such as politics
and law (Espeland and Vannebo, 2007; Harlow, 2014). It provides moral
principles that guide the ethical conduct of people or organizations as they
bear the responsibility for their actions. Accountability is, therefore, a key
component of trust in society, organizations, and the professional milieu. In
the context of AI, accountability is a meta-component of trustworthiness that
ensures that ethical principles are promoted and enforced throughout the
lifecycle of AI projects (Ammanath, 2022; Doshi-Velez et al., 2017; Kim et
al., 2020; Miguel et al., 2021).

Accountable for what? According to Virginia Dignum accountability means the
decisions of the AI system are explainable and derivable from the decision-
making mechanisms used (Dignum, 2019) and is a set of components guided by
moral values that are part of a large socio-technical system. (Millar et al.,
2018) see accountability as ”answerability” for decisions, actions, products,
and policies. From this perspective, it is required that accountability is a
practice that the management board of a company could enforce by making all
the stakeholders who develop the system understand that they bear the
responsibility for the decisions of the system. Therefore, policymakers, data
scientists, and AI engineers should carefully choose and justify the choices
made during the design, development, and deployment of AI systems (Novelli et
al., 2023).

Accountable to whom? The design, development, and deployment of AI systems
involve multiple stakeholders. While a system in which decisions can be
explained can help to detect the causes or reasons for the incidents, it does
not necessarily provide an answer to who is most responsible for the
unintended outcomes. Establishing causal accountability remains challenging,
and unintended negative outcomes might bear legal liability. Companies or
organizations can acknowledge incidents caused by the use of their systems and
can pay for reparation. However, monetary compensation could not be enough in
society and requires someone to get punished, i.e., to take legal
responsibility for the potential harm. (Ammanath, 2022) says that instead of
looking for people to blame, companies should recognize the legal
responsibility of sociotechnical systems around AI and all stakeholders should
use it as an ”additional motivation to own their individual responsibility”.

Promoting accountability throughout the entire lifecycle of AI systems faces
the challenge of developing processes and rules to enforce it while not
hindering innovation. The enforcement of accountability can also be baked by
laws and regulations that define a conformity assessment of the outcome of
sociotechnical systems in order to hinder potential flaws of the system
(Millar et al., 2018; Ammanath, 2022). Accountability, therefore, becomes a
core component of trustworthiness in a socio-technical system. That is, if a
system provides discriminatory outcomes, leaks people’s sensitive data, or is
not robust to different kinds of environments where it is deployed, someone
would bear the responsibility of justifying and mitigating it.

###  5.3. AI Ethics and Regulation

Understanding how new technologies intertwine with the upcoming AI regulations
and how they reflect on AI ethics could go a long way. According to Stanford
University’s 2023 AI Index (Lynch, 2023) in 2022, 127 countries passed around
37 bills with the word “artificial intelligence” which shows a higher interest
in regulating AI. In this section, we delve into the ethical considerations of
AI and the current landscape of AI regulation. There are initiatives around
the world that focus on the development and promotion of trustworthy AI and
the regulations around it. One exciting initiative to look at is the National
AI Initiative, one of the components of the United States’ approach to AI
regulation. Looking at the National AI Initiative Act of 2020 (Congress,
2020), it aims to promote and support research and development in trustworthy
AI in both the public and private sectors. They also aim to develop technical
standards and guidelines that facilitate the evaluation of bias in artificial
intelligence training data and applications. The US governmental agencies
allocated a budget of $1.7 billion to AI R&D which is an increase of 209% from
2018 (Lynch, 2023). Despite many of these efforts, the United States does not
have a comprehensive federal law governing AI. Different states or even
problem statements have taken different approaches to AI regulation, with some
enacting laws related to specific applications of AI, such as autonomous
vehicles, and others focusing on broader issues, such as data privacy and
security. For autonomous vehicle regulation in the United States, the National
Conference of State Legislatures (NCSL) provides a comprehensive database (of
State Legislatures, 2023) that tracks autonomous vehicle bills introduced in
all 50 states and the District of Columbia. And for broader issues such as
data privacy and security, the National Conference of State Legislatures
(NCSL) documents state privacy laws in various areas (of State Legislatures,
2022)

There has also been regulation around Deepfake and online safety of users
(Zhu, 2023). The UK passed the Online Safety Bill, making sharing pornographic
deepfakes without consent a crime, and considering laws for clear labeling of
AI-generated content (McCallum, 2023; Zhu, 2023). Also, China’s Deep Synthesis
Provisions, effective from January 10, 2023, regulate deep synthesis
technology, mandating data protection, transparency, and content management
with other requirements (Interesse, 2023).

The European Union (EU) has taken significant steps to regulate AI and protect
data. General Data Protection Regulation (GDPR) (Union, 2018), enacted in May
2018, is a set of rules designed to give EU citizens more control over their
personal data. Elements of GDPR, like Data Minimisation, can play a huge role
in what kind of data AI models can store and use for training. Additionally,
the European Commission introduced the Artificial Intelligence Act in April
2021 (Commission, 2021) and actively working towards establishing
comprehensive AI regulations. ”The vote sets up the so-called trilogue
negotiations, the final phase of the EU’s process, and paves the way for the
likely adoption of Europe’s — and the world’s — first comprehensive AI
regulatory framework in early 2024.” (Morse, 2023)

###  5.4. Generative AI Ownership

For a long time, creativity has been considered one of the most distinguishing
attributes of humans. But with the advances in computer science and machine
learning, people have tried to add creativity to the machine learning models,
and it is where Generative AI appears. Regarding probabilistic modeling,
generative models are machine learning models that describe generating a
dataset. Sampling from such a model generates new data belonging to the
desired distribution (Foster, 2022).

Who is the author? While current samples of generative models can create high-
quality products like meaningful text (OpenAI, 2023), different types of art,
including paintings (Photoleap, 2023), music (Inc., 2023), and executable
computer codes, the first generative models which caught the news attention
were StyleGAN (Karras et al., 2019) which created hyper-realistic images from
human faces and GPT-2 (Radford et al., 2019) that could finish a paragraph of
meaningful text related to its received opening sentence.
While the number of generative AI users is increasing daily, a critical
question about generative AI ownership still needs to be answered. Consider a
situation in which an employee who is expected to add some lines of code to
their company’s product uses generative AI to generate the code and add it to
their product. The question that arises here is who owns the product? Does the
company lose copyright over its product because a machine learning model
generates one part? What about art generated by generative AI? While an artist
who won Colorado State Fair’s art competition using AI-generated picture
(Harwell, 2022) claimed he had not broken any rules, another artist rejected
the prize he won at the Sony World Photography award, revealing that AI had
generated the winning photo (Grierson, 2023). As a prevention from such
events, the Grammys forbids AI-generated works from participating in their
competitions (King, 2023). Debates about the ownership of Generative AI
products remain unsolved, and people taking each side of this debate have
satisfying reasons. While from one point of view, the owners of the AI-
generated products are people who made the models generate such output by
their prompts and guidance, the other perspective gives the ownership of these
products to model owners who created these generative models or to the models
themselves (Epstein et al., 2023).

Rules and Regulations Although the debate on generative AI ownership has not
concluded, generative AI is being used more and more daily. Thus, it needs
some regulation to decide who is responsible for and who owns the products
generated by AI. Based on Federal Register guideline (Register, 2023b), the
work generated by AI, using only one prompt by the human user, is unprotected.
At the same time, if it is a product of interaction between the human and
machine where a human makes some modification, selection, or arrangement on
the AI product, it is protected by copyright law. Regulations in the EU and UK
take a different side toward AI-generated product ownership: a product can be
subject to copyright if it is “the author’s own intellectual creation.”
According to Copyright Designs and Patents Act 1988 (CDPA) (Register, 2023a),
since computer programs cannot be considered as the owner of their product,
“the person by whom the arrangements necessary for the creation of the work
are undertaken” is known as the author of AI products. As can be seen from
different viewpoints of regulations toward generative AI ownership, there has
yet to be a finalized committed decision. Some consider AI models as an
extension of their human users, and the human owns their products; others take
model owners as the creators of the model’s products, and the last group
considers joint ownership between creators and the model owners. Since the
legal landscape around AI ownership is still evolving, a long way exists to
achieve commitment.
But what are generative AI’s economic and social impacts apart from the
ownership of AI products? Based on AI ACT, when products like images, voice,
art, etc., are generated by AI and published, it should be exposed that it is
AI-generated, especially when they resemble existing persons, places, or
events (of European Union, 2022). Otherwise, AI-generated products can easily
deceive and manipulate people in various political, social, and cultural
domains. Malicious use of AI to defame or create popularity for specific
individuals or groups is another important concern toward using Generative AI.
In this regard, AI Act added an account for general-purpose AI that may lead
to discriminatory outcomes and the exclusion of certain groups.

Product manufacturers or fashion designers can utilize AI-generated content to
promote their products without hiring specialized individuals. In this regard,
another issue that arises is the economic impact of using generative AI.
Despite the significant concerns raised about generative AI’s impact on the
future of jobs by enabling enterprises to use AI instead of their human
forces, the economic consequences of its use and the possibility of further
economic inequality due to AI utilization exist. Since launching and utilizing
generative AI requires high-level hardware capabilities and software
knowledge, equal access to it is impossible for all countries and all
individuals within a society. This can significantly increase social and
economic inequality between developing and developed countries and between
organizations and individuals with financial and informational resources and
those without them. The lack of consistent regulation for generative AI added
to the concerns about the harms unregulated AI can bring to society (Bender et
al., 2021) has resulted in an open letter (for Life, 2023b) initiated by
Future for Life and signed by more than 3000 people in the technology
industry, asking for a six-month pause on giant AI experiments by all
companies until sufficient policies are made and enforced. On the other hand,
since the AI act is forcing more strict regulations for using generative AI,
some European companies claim that it may jeopardize technological sovereignty
and signed an open letter (for Life, 2023a) asking the EU to reconsider its
plans to let European companies participate in the advancement of AI.

##  6\. Environmental Impact

While machine learning algorithms are not inherently bad for the climate and
environment, their implementation can have certain implications that need to
be considered (Zhong et al., 2023; Patterson et al., 2021; Schwartz et al.,
2020). One significant factor is the high energy consumption associated with
training and running machine learning models, particularly when GPUs (Graphics
Processing Units) are used for accelerated computation. From an environmental
viewpoint, the following aspects have to be considered.

Energy Consumption and Carbon Emissions. Machine learning algorithms,
particularly deep learning models, require extensive computational resources
for training (Patterson et al., 2021). This process involves performing
numerous complex calculations on large datasets. Graphics Processing Units
(GPUs) are commonly used for their parallel processing capabilities, which
accelerate the training process. However, GPUs are power-hungry devices that
consume substantial amounts of electricity. The energy consumption of AI
algorithms is a significant concern due to the scale at which these algorithms
are deployed. The study by (Strubell et al., 2019) discovered that the
greenhouse gas emissions produced from some of the NLP algorithms were
comparable to those from 300 flights traveling between New York and San
Francisco. Training state-of-the-art models can take weeks or even months,
consuming a substantial amount of energy during that time. The energy
consumption is directly proportional to the size and complexity of the model
and the amount of data being processed. As machine learning applications
become more prevalent across industries, the collective energy consumption
associated with training and running increases (Sarswatula et al., 2022).

Carbon Emissions. Machine learning algorithms’ energy consumption directly
impacts carbon emissions (Anthony et al., 2020; Lacoste et al., 2019;
Henderson et al., 2020)., as most electricity worldwide still comes from non-
renewable sources like coal, natural gas, and oil. These fossil fuels emit CO2
and other greenhouse gases when burned for energy, contributing to climate
change. Machine learning algorithms, which heavily use GPUs, often draw
electricity from grids powered by fossil fuel-based plants. This results in
significant carbon emissions from both the direct power consumption and the
indirect emissions from fossil fuel extraction, production, and transportation
needed to meet energy demands.

Electronic Waste and Disposal Challenges. The rapid advancement of machine
learning technology necessitates frequent hardware upgrades, including GPUs
(Gao et al., 2020). As newer and more powerful GPUs are introduced to the
market, older models become obsolete. This cycle of hardware replacement
results in electronic waste generation. The electronic waste consists of
discarded electronic devices, including GPUs, that are no longer in use.
Improper handling and disposal of e-waste pose significant environmental
risks. Electronic devices contain hazardous materials, such as lead, mercury,
cadmium, and brominated flame retardants, which can contaminate the
environment if not managed properly. E-waste often ends up in landfills, where
toxic substances can leach into the soil and water, posing threats to
ecosystems and human health. Managing e-waste from machine learning
infrastructure can be particularly challenging due to the rapid obsolescence
of hardware and the need for specialized recycling processes to handle complex
electronic components (Miller, 2022).

###  6.1. Using AI for mitigating Climate Change

Contrary to the potential harms of climate change due to AI, recent research
(Cowls et al., 2021; Leal Filho et al., 2022; Coeckelbergh, 2021; Huntingford
et al., 2019) has gained interest in harnessing AI to combat climate change.
By leveraging AI’s capabilities, climate modeling and prediction, optimization
of resource management and energy efficiency, advancement of renewable energy
solutions, and enhancement of climate change adaptation and resilience can be
improved. The potential of AI to address the pressing issues of climate change
offers a promising path toward sustainability and a greener future.

Improved Climate Modeling and Prediction. AI has revolutionized climate
modeling by providing powerful tools to process vast amounts of data and make
accurate predictions (Schneider et al., 2023; Barnes et al., 2019). Machine
learning algorithms can analyze historical climate data, satellite imagery,
and environmental variables, unveiling complex climate system dynamics. This
enhanced understanding enables us to anticipate extreme weather events, assess
the impacts of human activities on the environment, and predict long-term
climate trends. AI-driven climate modeling empowers policymakers
(Coeckelbergh, 2021) with valuable insights to develop effective mitigation
and adaptation strategies, fostering a more sustainable future.

Optimized Resource Management and Energy Efficiency. AI has the potential to
transform resource management and enhance energy efficiency across various
sectors (Ahmed et al., 2022; Zeng et al., 2021). By employing AI, especially
RL algorithms, smart grids can analyze real-time data on energy consumption,
demand, and weather conditions (Ali and Choi, 2020; Mazhar et al., 2023). This
analysis facilitates efficient energy distribution, minimizes wastage, and
encourages the integration of renewable energy sources. Machine learning
techniques can optimize energy-intensive processes in industries,
transportation systems, and buildings, resulting in significant energy
savings.

Advancements in Renewable Energy Solutions. Renewable energy plays a pivotal
role in mitigating climate change, and AI can accelerate its adoption. AI
algorithms can optimize the integration and operation of renewable energy
(Chen et al., 2021; Ramachandran, 2020; Al-Othman et al., 2022) systems like
solar, wind, and hydroelectric power. By analyzing geographical and climatic
data, AI can identify optimal locations for solar panels and wind turbines,
maximizing energy output. Additionally, AI can aid in developing advanced
materials and technologies for efficient energy storage, addressing one of the
primary challenges in renewable energy adoption (Shin et al., 2021). By
harnessing AI, the shift to a renewable energy-powered world reduces
dependence on fossil fuels and mitigates climate change.

In summary, to ensure the responsible use of machine learning algorithms,
stakeholders must consider the environmental implications and strive to adopt
sustainable practices. This can include optimizing algorithms for efficiency,
using energy-efficient hardware, considering renewable energy sources for
computing infrastructure, and promoting responsible e-waste management. By
balancing technological advancements with environmental considerations, we can
potentially harness the benefits of machine learning while minimizing its
potential negative impacts on the climate and environment.

##  7\. Conclusion

Over the past few years, AI ethics has reaped significant attention as the
ramifications of AI decisions permeate every layer of society. We provide a
review to understand its intricacies and challenges through notions of privacy
and data protection, transparency and explainability, fairness and equity,
responsibility, accountability, and regulations and lastly the environmental
impacts. The notions presented in this paper are key ingredients to building
trustworthy AI systems. Nevertheless, there is not yet a checklist that, when
fulfilled, ensures trustworthy AI. The integration of these principles depends
on the problems and the business needs. Moreover, certain ethical principles
can be conflicting or open to compromise. For example, prioritizing privacy in
a system might lead to diminished performance and fairness (Bagdasaryan et
al., 2019; Uniyal et al., 2021); introducing trade-offs among accuracy,
fairness, and privacy. Determining which ethical aspect should take precedence
in such scenarios remains ambiguous. Regulators are expected to establish a
baseline for fairness and privacy, necessitating compliance audits before AI
system deployment (Falco et al., 2021).

Concurrently, constructing fair models introduces privacy concerns related to
demographic information (Ferry et al., 2023; Lyu and Chen, 2021), or faces
challenges when demographic information is unavailable due to privacy
constraints (Hashimoto et al., 2018; Kenfack et al., 2023a). While
explainability is crucial for instilling trust, disparities in the quality of
explanations (Dai et al., 2022) and the risk of ’fairwashing’ (Aïvodji et al.,
2019) persist. Assessments of the advancement in building models robust to
attacks predominantly focus on accuracy, though other principles such as
fairness are also vulnerable to attacks (Mehrabi et al., 2021).

Conversely, promoting accountability in companies, with clear consequences for
individuals involved in the AI lifecycle when unexpected outcomes arise, could
instill fear of professional repercussions and potentially stifle innovation.
Therefore, it is imperative to formulate policies that strike a balance
between fostering innovation and ensuring accountability (Millar et al., 2018;
Ammanath, 2022). Despite these challenges requiring considerable attention, an
expanding body of work is dedicated to addressing them (Papernot et al., 2021;
Tran et al., 2022; Li et al., 2023; Wang et al., 2021; Xu et al., 2021).

### Acknowledgments

We would like to thank the reviewers of the earlier version of the paper which
was part of the 2023 Kaggle AI Report competition.

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