# Should ChatGPT be Biased?
Challenges and Risks of Bias in Large Language Models

Emilio Ferrara

###### Abstract

As generative language models, exemplified by ChatGPT, continue to advance in
their capabilities, the spotlight on biases inherent in these models
intensifies. This article delves into the distinctive challenges and risks
associated with biases specifically in large-scale language models. We explore
the origins of biases, stemming from factors such as training data, model
specifications, algorithmic constraints, product design, and policy decisions.
Our examination extends to the ethical implications arising from the
unintended consequences of biased model outputs. In addition, we analyze the
intricacies of mitigating biases, acknowledging the inevitable persistence of
some biases, and consider the consequences of deploying these models across
diverse applications, including virtual assistants, content generation, and
chatbots. Finally, we provide an overview of current approaches for
identifying, quantifying, and mitigating biases in language models,
underscoring the need for a collaborative, multidisciplinary effort to craft
AI systems that embody equity, transparency, and responsibility. This article
aims to catalyze a thoughtful discourse within the AI community, prompting
researchers and developers to consider the unique role of biases in the domain
of generative language models and the ongoing quest for ethical AI.

###### keywords:

Artificial Intelligence , Generative AI , Bias , Large Language Models ,
ChatGPT , GPT-4

††journal: First Monday

[inst1]organization=Thomas Lord Department of Computer
Science,addressline=University of Southern California, city=Los Angeles,
postcode=90007, state=CA, country=USA [inst2]organization=USC Information
Sciences Institute,addressline=4676 Admiralty Way #1001, city=Marina del Rey,
postcode=90292, state=CA, country=USA, email=
E-mail<EMAIL_ADDRESS>
##  1 Introduction

In recent years, there has been a remarkable surge in the realm of artificial
intelligence (AI), marked by the emergence of transformative technologies like
ChatGPT and other generative language models ([1, 2, 3, 4, 5, 6, 7, 8, 9]).
These AI systems represent a class of sophisticated models designed to excel
in generating human-like text and comprehending natural language. Using deep
learning techniques and vast datasets, they have the ability to discern
intricate patterns, make contextual inferences, and generate coherent and
contextually relevant responses to a diverse range of inputs ([10, 11, 12, 1,
2]). By mirroring human language capabilities, these models have unveiled a
multitude of applications, ranging from chatbots and virtual assistants to
translation services and content generation tools ([13]).

Language models have ushered in a transformative era, underpinning the
development of chatbots that emulate human interaction in conversations
([14]). These chatbots have become vital tools, simplifying customer service,
technical support, and information queries with human-like interaction.
Furthermore, the integration of language models into virtual assistants has
endowed them with the ability to provide precise and contextually appropriate
responses to user inquiries ([15]). Virtual assistants, thus enhanced, become
indispensable aides, capable of managing tasks ranging from appointment
scheduling to web searches and smart home device control ([16]).

In the sphere of translation, harnessing the prowess of large language models
facilitates markedly improved and fluent translations that span multiple
languages, including those with limited resources ([17, 18, 19, 20, 21]). Such
capabilities not only foster enhanced cross-linguistic communication but can
also enable timely solutions during emergencies and crises, especially in
regions where low-resource languages or indigenous dialects are spoken ([22]).

Moreover, the aptitude of language models to generate coherent and
contextually pertinent text has rendered them invaluable in the realm of
content creation. Acknowledged for their proficiency in producing various
types of content, spanning articles, social media posts, and marketing
materials, they have established a profound impact ([23, 24]).

These applications, among many others, underscore the transformative prowess
of generative language models across an array of industries and sectors.
However, as their adoption proliferates and their influence extends into ever-
diverse domains ([25, 26]), it is imperative to confront the distinctive
challenges posed by the potential biases that may be established within these
models. These biases can have profound implications for users and society at
large, highlighting the urgent need for comprehensive examination and
mitigation of these issues [27].

Contributing Factor | Description | References
---|---|---
Training Data |  Biases in the source material or the selection process for training data can be absorbed by the model and reflected in its behavior. |  [28, 29, 30, 31]
Algorithms |  Biases can be introduced or amplified through algorithms that place more importance on certain features or data points. |  [32, 30, 33]
Labeling and Annotation |  In (semi)supervised learning scenarios, biases may emerge from subjective judgments of human annotators providing labels or annotations for the training data. |  [34, 35, 36]
Product Design Decisions |  Biases can arise from prioritizing certain use cases or designing user interfaces for specific demographics or industries, inadvertently reinforcing existing biases and excluding different perspectives. |  [37, 38]
Policy Decisions |  Developers might implement policies that prevent (or encourage) a given model behavior. For example, guardrails that modulate the behavior of ChatGPT and Bing-AI were designed to mitigate unintended toxic model behaviors or prevent malicious abuse. |  [39, 40, 41, 42]
Table 1: Factors Contributing to Bias in AI Models

##  2 Defining bias in generative language models

###  2.1 Factors contributing to bias in Large Language Models

Bias, in the context of large language models such as GPT-4 ([9, 43, 44]) and
predecessors ([3, 4, 5]), or other state-of-the-art alternatives ([45, 46];
including multimodal variants, [47]), can be defined as the presence of
systematic misrepresentations, attribution errors, or factual distortions that
result in favoring certain groups or ideas, perpetuating stereotypes, or
making incorrect assumptions based on learned patterns. Biases in such models
can arise due to several factors (cf., Table 1).

One factor is the training data. If the data used to train a language model
contain biases, either from the source material or through the selection
process, these biases can be absorbed by the model and subsequently reflected
in its behavior ([28, 29, 30, 31]). Biases can also be introduced through the
algorithms used to process and learn from the data. For example, if an
algorithm places more importance on certain features or data points, it may
unintentionally introduce or amplify biases present in the data ([32, 30,
33]). In (semi)supervised learning scenarios, where human annotators provide
labels or annotations for the training data, biases may emerge from the
subjective judgments of the annotators themselves, influencing the model’s
understanding of the data ([34, 35, 36]).

The choice of which use cases to prioritize or the design of user interfaces
can also contribute to biases in large language models. For example, if a
language model is primarily designed to generate content for a certain
demographic or industry, it may inadvertently reinforce existing biases and
exclude different perspectives ([37, 38]). Lastly, policy decisions can play a
role in the manifestation of biases in language models. The developers of both
commercial and openly available language models might implement policies that
prevent (or encourage) a given model behavior. For example, both OpenAI and
Microsoft have deliberate guardrails that modulate the behavior of ChatGPT and
Bing-AI to mitigate unintended toxic model behaviors or prevent malicious
abuse ([39, 40, 41, 42]).

###  2.2 Types of biases in Large Language Models

Large language models, which are commonly trained from vast amounts of text
data present on the Internet, inevitably absorb the biases present in such
data sources. These biases can take various forms (cf., Table 2).

Demographic biases arise when the training data over-represents or under-
represents certain demographic groups, leading the model to exhibit biased
behavior towards specific genders, races, ethnicities, or other social groups
([34, 28, 29, 35, 31, 48]). Cultural biases occur when large language models
learn and perpetuate cultural stereotypes or biases, as they are often present
in the data used for training. This can result in the model producing outputs
that reinforce or exacerbate existing cultural prejudices ([49, 50, 30]).
Linguistic biases emerge since the majority of the internet’s content is in
English or a few other dominant languages, making large language models more
proficient in these languages. This can lead to biased performance and a lack
of support for low-resource languages or minority dialects ([51, 52, 53, 54,
31]). Temporal biases appear as the training data for these models are
typically restricted to limited time periods or have temporal cutoffs. This
may cause the model to be biased when reporting on current events, trends, and
opinions. Similarly, the model’s understanding of historical contexts or
outdated information may be limited due to a lack of temporally representative
data ([3, 55, 56, 57]). Confirmation biases in the training data may result
from individuals seeking out information that aligns with their pre-existing
beliefs. Consequently, large language models may inadvertently reinforce these
biases by providing outputs that confirm or support specific viewpoints ([28,
29, 2, 58]). Lastly, ideological and political biases can be learned and
propagated by large language models due to the presence of such biases in
their training data. This can lead to the model generating outputs that favor
certain political perspectives or ideologies, thereby amplifying existing
biases ([59, 60, 56, 61]).

This paper aims to explore the question of whether language models like GPT-4
([9, 43, 44]), its prior versions ([3, 4, 5]), or other commercial or open-
source alternatives ([45, 46, 62]) that power applications like ChatGPT ([8])
(or similar) should be biased or unbiased, taking into account the
implications and risks of both perspectives. By examining the ethical,
practical, and societal consequences of each viewpoint, we hope to contribute
to the ongoing discussion surrounding responsible language model development
and use. Through this exploration, our goal is to provide insights that can
help guide the future evolution of GPT-style and other generative language
models toward more ethical, fair, and beneficial outcomes while minimizing
potential harm.

Types of Bias | Description | References
---|---|---
Demographic Biases |  These biases arise when the training data over-represents or under-represents certain demographic groups, leading the model to exhibit biased behavior towards specific genders, races, ethnicities, or other social groups. |  [34, 28, 29, 35, 31, 48]
Cultural Biases |  Large language models may learn and perpetuate cultural stereotypes or biases, as they are often present in the data used for training. This can result in the model producing outputs that reinforce or exacerbate existing cultural prejudices. |  [49, 50, 30]
Linguistic Biases |  Since the majority of the internet’s content is in English or a few other dominant languages, large language models tend to be more proficient in these languages. This can lead to biased performance and a lack of support for low-resource languages or minority dialects. |  [51, 52, 53, 54, 31]
Temporal Biases |  The training data for these models are typically restricted to limited time periods, or have temporal cutoffs, which may cause the model to be biased when reporting on current events, trends, and opinions. Similarly, the model’s understanding of historical contexts or outdated information may be limited for lack of temporally representative data. |  [3, 55, 56, 57]
Confirmation Biases |  The training data may contain biases that result from individuals seeking out information that aligns with their pre-existing beliefs. Consequently, large language models may inadvertently reinforce these biases by providing outputs that confirm or support specific viewpoints. |  [28, 29, 2, 58]
Ideological & Political Biases |  Large language models can also learn and propagate the political and ideological biases present in their training data. This can lead to the model generating outputs that favor certain political perspectives or ideologies, thereby amplifying existing biases. |  [59, 60, 56, 61]
Table 2: Types of Biases in Large Language Models

##  3 Why are generative language models prone to bias?

###  3.1 Biases from the data

ChatGPT and other applications based on large language models are trained
using a process that primarily relies on unsupervised learning, a machine
learning technique that enables models to learn patterns and structures from
vast amounts of unlabelled data ([63, 64]). In most cases with these language
models, the data consists of extensive text corpora available on the internet,
which includes websites, articles, books, and other forms of written content
([2, 45, 46, 62]).

ChatGPT in particular is trained on a diverse range of internet text datasets
that encompass various domains, genres, and languages. While the specifics of
the dataset used for GPT-4 are proprietary ([9, 43]), the data sources
utilized for training its predecessor, GPT-3, likely share similarities. For
GPT-3 and predecessors, the primary dataset used was WebText ([3]), which is
an ever-growing large-scale collection of web pages ([4, 5]). WebText was
created by crawling the internet and gathering text from web pages. The
sources of data include, but are not limited to:

  * 1.

Websites: Text is extracted from a wide array of websites, covering topics
such as news, blogs, forums, and informational websites like Wikipedia. This
enables the model to learn from diverse sources and gain knowledge on various
subjects.

  * 2.

Books: Text from books available online, including both fiction and non-
fiction, contributes to the training data. This helps the model to learn
different writing styles, narrative structures, and a wealth of knowledge from
various fields.

  * 3.

Social media platforms: Content from social media platforms, like Twitter,
Facebook, and Reddit, is incorporated to expose the model to colloquial
language, slang, and contemporary topics of discussion.

  * 4.

Conversational data: To improve the model’s conversational abilities, text
from chat logs, comment sections, and other conversational sources are also
included in the training dataset.

The developers of ChatGPT note that the WebText data is preprocessed and
filtered to remove low-quality content, explicit material, web and social spam
[65, 66], and other undesirable text before being fed into the model ([4, 5]).
However, due to the vast scale of the data and the limitations of current
filtering techniques, some undesirable or biased content may still seep into
the training dataset, affecting the behavior of the resulting model. In
addition to WebText, GPT-3 was further trained using a filtered version of the
Common Crawl dataset (https://commoncrawl.org), a publicly available, massive
web-crawled dataset that contains raw web page data, extracted metadata, and
text content from billions of web pages in multiple languages ([5]).

Another commonly-used dataset for language model training is _The Pile_ ([67,
68]) an extensive and diverse collection of 22 smaller datasets, combining
various sources of scientific articles, books, and web content. It is designed
for training large-scale language models, particularly in the domain of
scientific research and understanding.

###  3.2 Biases from the models

During the training process, generative language models are exposed to
billions of sentences and phrases, allowing them to learn the intricate
relationships between words, grammar, context, and meaning ([63, 64]). As they
process the text data, they gradually acquire natural language generation
capabilities, enabling them to produce coherent and contextually relevant
responses to various inputs. However, some capabilities of these models can
lead to bias (cf., Table 3):

#### Generalization

One crucial aspect of these models is their ability to generalize, which
allows them to apply the knowledge gained from their training data to new and
previously unseen inputs, providing contextually relevant responses and
predictions even in unfamiliar situations. However, this ability also raises
concerns about potential biases, as models may inadvertently learn and
perpetuate biases present in their training data, even if the data has been
filtered and cleaned to the extent possible ([69, 29]).

#### Propagation

As these models learn from the patterns and structures present in their
training data, they may inadvertently absorb and propagate biases they
encounter, such as adopting stereotypes, favoring certain groups or ideas, or
making assumptions based on learned patterns that do not accurately represent
the full spectrum of human experience. This propagation of biases during
training poses significant challenges to the development of fair and equitable
AI systems, as biased models can lead to unfair treatment, reinforce
stereotypes, and marginalize certain groups ([28, 70, 71]).

#### Emergence

In large language models, the phenomenon of emergence, which refers to the
spontaneous appearance of unanticipated capabilities despite these
functionalities not being explicitly encoded within the model’s architecture
or training data, can also result in unexpected biases due to the intricate
interplay between model parameters and biased training data ([72, 73]). The
high-dimensional representations and non-linear interactions in these models
make it difficult to predict or control these emergent biases, which may
manifest in various ways, such as stereotyping, offensive language, or
misinformation. To address this challenge, researchers are exploring bias
mitigation strategies during training, fine-tuning with curated datasets, and
post-hoc emergent bias analyses ([74, 75]).

#### Non-linearity

The non-linear relationships between biases in the system or data and their
real-world impact imply that small biases may have massive negative effects,
and large biases might not result in significant consequences. This
disproportionality arises due to the complex interdependencies between the
model parameters and the high-dimensional representations learned during
training [71]. Randomized controlled trials could be used to draw causal
relationships between the extent of each bias and their effects. In the
absence of that, due to ethical reasons, multifaceted approaches involving in-
depth analysis of model behavior, rigorous evaluation with diverse benchmarks,
and the application of mitigation techniques that account for the nonlinear
nature of emergent biases are needed [76].

#### Alignment

To address these issues, a strategy known as Reinforcement Learning with Human
Feedback (RLHF) ([7]) was developed to fine-tune large language models like
ChatGPT to reduce their biases and align them with human values. This approach
involves collecting a dataset of human demonstrations, comparisons, and
preferences to create a reward model that guides the fine-tuning process [77].
InstructGPT (ChatGPT’s default model) ([8]) is trained using RLHF and then
fine-tuned using Proximal Policy Optimization (PPO), a policy optimization
algorithm ([6]). It is paramount to understand if the same principles could be
exploited to deliberately misalign a model.

Source | Description | References
---|---|---
Generalization |  Models generalize knowledge from training data to new inputs, potentially leading to biased behavior if the data contains biases. This raises concerns about perpetuating biases, even if training data has been cleaned and filtered. |  [69, 29]
Propagation |  Models may absorb and propagate biases in training data, adopting stereotypes and favoring certain groups or ideas, or making assumptions on on non-representative learned patterns. |  [28, 70, 71]
Emergence |  Unanticipated capabilities and biases may emerge in large language models due to complex interactions between model parameters and biased training data. It has been proven difficult to predict or control these emergent biases. |  [72, 73, 74, 75]
Non-linearity |  Biases in AI systems may have non-linear real-world impact, making it difficult to predict their consequences: small model biases may have massive negative effects, whereas large model biases might not cause significant consequences. |  [76, 71]
Alignment |  Reinforcement Learning with Human Feedback (RLHF) fine-tunes large language models to reduce biases and align them with human values. The same principles might be abused to lead to unfair model behaviors. |  [7, 8, 6]
Table 3: Sources of model bias in large language models and their
descriptions.

###  3.3 Can bias be mitigated with human-in-the-loop approaches?

Bias in generative language models can be mitigated to some extent with human-
in-the-loop (HITL) approaches. These approaches involve incorporating human
input, feedback, or oversight throughout the development and deployment of the
language model, which can help address issues related to biases and other
limitations. Here are some ways to integrate human-in-the-loop approaches to
mitigate bias:

  * 1.

Training data curation: Humans can be involved in curating and annotating
high-quality and diverse training data. This may include identifying and
correcting biases, ensuring a balance of perspectives, and reducing the
influence of controversial or offensive content ([78, 31, 33]).

  * 2.

Model fine-tuning: Subject matter experts can guide the model fine-tuning
process by providing feedback on the model’s outputs, helping the model
generalize better and avoid biased or incorrect responses ([79]).

  * 3.

Evaluation and feedback: Human reviewers can evaluate the model’s performance
and provide feedback to developers, who can then iteratively improve the
model. This feedback loop is essential for identifying and addressing bias-
related issues. ([58]).

  * 4.

Real-time moderation: Human moderators can monitor and review the model’s
outputs in real-time, intervening when necessary to correct biased or
inappropriate responses. This approach can be especially useful in high-stakes
or sensitive applications ([80]).

  * 5.

Customization and control: Users can be provided with options to customize the
model’s behavior, adjusting the output according to their preferences or
requirements. This approach can help users mitigate bias in the model’s
responses by tailoring it to specific contexts or domains ([3, 81]).

While human-in-the-loop approaches can help mitigate bias, it is essential to
recognize that they may not be able to eliminate it entirely. Bias can stem
from various sources, such as the training data, fine-tuning process, or even
the human reviewers themselves. However, combining machine learning techniques
with human expertise can be a promising way to address some of the challenges
posed by biases in generative language models.

##  4 The inevitability of some forms of bias

###  4.1 Are some biases inevitable?

Completely eliminating bias from large language models is a complex and
challenging task due to the inherent nature of language and cultural norms.
Since these models learn from vast amounts of text data available on the
internet, they are exposed to the biases present within human language and
culture. Addressing bias in these models involves tackling several key
challenges (cf., Table 4).

First, human language is a reflection of society and as such, it contains
various biases, stereotypes, and assumptions. Separating useful patterns from
these biases can be challenging, as they are often deeply ingrained in the way
people express themselves and the structures of language itself ([82, 83, 84,
85, 86, 87]). Second, cultural norms and values can vary significantly across
different communities and regions. What is considered acceptable or
appropriate in one context may be seen as biased or harmful in another.
Determining which norms should be encoded in AI models and which should be
filtered out is a complex task that requires careful consideration and a
nuanced understanding of diverse cultural perspectives ([88, 89, 90, 91]).

Furthermore, fairness is a subjective concept that can be interpreted in
various ways. Completely eliminating bias from AI models would require
developers to define what “fair” means in the context of their applications,
which can be a challenging task, given the diverse range of stakeholders and
perspectives involved ([92, 93, 94]). Lastly, language and culture are
constantly evolving, with new expressions, norms, and biases emerging over
time. Keeping AI models up-to-date with these changes and ensuring that they
remain unbiased is an ongoing challenge that requires continuous monitoring
and adaptation ([95, 96, 97]).

Despite these challenges, it is essential for developers, researchers, and
stakeholders to continue working towards reducing bias in large language
models. By developing strategies for identifying and mitigating biases,
collaborating with diverse communities, and engaging in ongoing evaluation and
improvement, we can strive to create AI systems that are more equitable, fair,
and beneficial for all users.

Challenge |  Description |  References
---|---|---
Inherent biases in language |  Human language is a reflection of society, containing various biases, stereotypes, and assumptions. Separating useful patterns from these biases can be challenging as they are deeply ingrained in language structures and expressions. |  [82, 83, 84, 85, 86, 87]
Ambiguity of cultural norms |  Cultural norms and values vary significantly across communities and regions. Determining which norms to encode in AI models is a complex task that requires a nuanced understanding of diverse cultural perspectives. |  [88, 89, 90, 91]
Subjectivity of fairness |  Fairness is a subjective concept with various interpretations. Eliminating bias from AI models requires defining “fair” in the context of applications, which is challenging due to the diverse range of stakeholders and perspectives. |  [92, 93, 94]
Continuously evolving language and culture |  Language and culture constantly evolve, with new expressions, norms, and biases emerging over time. Keeping AI models up-to-date with these changes and ensuring they remain unbiased requires continuous monitoring and adaptation. |  [95, 96, 97]
Table 4: Challenges in addressing biases in Large Language Models

###  4.2 Utility despite bias?

Biased AI models can still be useful in certain contexts or applications, as
long as users are aware of their limitations and take them into account when
making decisions. In some cases, the biases present in these models may even
be representative of the real-world context in which they are being used,
providing valuable insights in surfacing societal inequalities that need to be
tackled at their root.

The key to leveraging biased AI models responsibly is to ensure that users
have a clear understanding of the potential biases and limitations associated
with these models, so they can make informed decisions about whether and how
to use them in different contexts. Some strategies for addressing this issue
include:

  * 1.

Transparency: Developers should be transparent about the methodologies, data
sources, and potential biases of their AI models, providing users with the
necessary information to understand the factors that may influence the model’s
predictions and decisions. Best practices about the documentation of models
and data have been advanced by the AI community ([58, 98]).

  * 2.

Education and awareness: Providing resources, training, and support to help
users better understand the potential biases in AI models and how to account
for them when making decisions. This may involve creating guidelines, best
practices, or other educational materials that explain the implications of
bias in AI and how to navigate it responsibly.

  * 3.

Context-specific applications: In some limited cases, biased AI models may be
viable for specific applications or contexts where their biases align with the
relevant factors or considerations. Experts should be employed to carefully
evaluate the appropriateness of using biased models in these situations,
taking into account the potential risks and benefits associated with their
use, and actionable plans to recognize, quantify, and mitigate biases.

  * 4.

Continuous monitoring and evaluation: Regularly assessing the performance of
AI models in real-world contexts, monitoring their impact on users and
affected communities, and making adjustments as needed to address any biases
or unintended consequences that emerge over time ([31]).

By acknowledging that biased models can still be useful in limited contexts
and taking steps to ensure that users are aware of their limitations and
qualified to recognize and mitigate them, we can promote the responsible use
of AI technologies and harness their potential benefits while minimizing the
risks associated with bias.

##  5 The broader risks of generative AI bias

###  5.1 Pillars of responsible generative AI development

Ethical considerations of fairness and equality play a crucial role in the
development and deployment of generative AI applications. As these models
integrate increasingly into various aspects of our lives, their potential
impact on individuals and society as a whole becomes a matter of significant
concern. The responsibility lies with developers, researchers, and
stakeholders to ensure that AI models treat all users and groups equitably,
avoiding the perpetuation of existing biases or the creation of new ones (cf.,
Table 5).

One key ethical consideration is representation. It is essential to ensure
that the training data used to develop generative AI models are representative
of the diverse range of perspectives, experiences, and backgrounds that exist
within society ([99, 35, 94, 58, 100]). This helps to reduce the risk that
biases are absorbed and propagated by models, leading to more equitable
outcomes. Transparency is another important aspect. Developers should be
transparent about the methodologies, data sources, and potential limitations
of their generative AI models ([101, 102]). This enables users to better
understand the factors that may influence the model’s predictions and
decisions. Accountability is also crucial for responsible generative AI
development. Developers and stakeholders must establish a clear framework for
accountability, which may include monitoring the performance of AI models,
addressing biases and errors, and responding to users and communities affected
([103, 104, 105]). An unique aspect of generative AI, compared to traditional
machine learning, is its ability to possibly replace human artistic expression
or to plagiarize the style and uniqueness of human work: as such, preservation
of intellectual property, copyright protection and prevention of plagiarism
are paramount [27]. Inclusivity is another key ethical consideration.
Generative AI applications should be designed to be inclusive and accessible
to all users, taking into account factors such as language, culture, and
accessibility needs ([106, 107]). This ensures that the benefits of AI are
shared equitably across society.

Lastly, continuous improvement is vital to achieve fairness and equality in
generative AI applications. Developers must commit to an ongoing process of
evaluating, refining, and improving their AI models to address biases and
ensure fairness over time ([108, 109, 58, 110]). This may involve
collaborating with researchers, policymakers, and affected communities to gain
insight and feedback that can help guide the development of more equitable AI
systems.

By prioritizing ethical considerations of fairness and equality, AI developers
can create applications that not only harness the power of advanced
technologies such as large language models, but also promote a more just and
inclusive society, where the benefits and opportunities of AI are accessible
to all.

Pillar |  Description |  References
---|---|---
Representation |  Ensuring that the training data used to develop AI models is representative of the diverse range of perspectives, experiences, and backgrounds that exist within society. This helps to reduce the risk of biases being absorbed and propagated by the models, leading to more equitable outcomes |  [99, 35, 94, 58, 100]
Transparency |  Developers should be transparent about the methodologies, data sources, and potential limitations of their AI models, enabling users to better understand the factors that may influence the model’s predictions and decisions |  [101, 102]
Accountability |  It is essential for developers and stakeholders to establish a clear framework for accountability, which may include monitoring the performance of AI models, addressing biases and errors, and responding to the concerns of users and affected communities |  [103, 104, 105]
Inclusivity |  AI applications should be designed to be inclusive and accessible to all users, taking into account factors such as language, culture, and accessibility needs, to ensure that the benefits of AI are shared equitably across society |  [106, 107]
Protection of IP, human work, and human artistic expression |  Generative AI models have the remarkable capability to create human-like text, artwork, music, and more. This creative aspect presents unique challenges, including issues related to intellectual property and the protection of human-generated copyright work to avoid AI plagiarism |  [27]
Continuous improvement |  Developers must commit to an ongoing process of evaluating, refining, and improving their AI models to address biases and ensure fairness over time. This may involve working with researchers, policy makers, and affected communities to gain information and feedback that can help guide the development of more equitable AI systems |  [108, 109, 58, 110]
Table 5: Pillars of responsible generative AI development

###  5.2 The risks of exacerbating existing societal biases

Bias in widely-adopted AI models, including ChatGPT and other generative
language models, can have far-reaching consequences that extend beyond the
immediate context of their applications. When these models absorb and
propagate biases, including those present in their training data, they may
inadvertently reinforce stereotypes, marginalize certain groups, and lead to
unfair treatment across various domains. Some examples of how biased AI models
can adversely impact different areas include:

  * 1.

Hiring: AI-driven hiring tools that use biased models may exhibit unfair
treatment towards applicants from underrepresented groups or those with non-
traditional backgrounds. This could lead to the perpetuation of existing
inequalities in the job market, limiting opportunities for affected
individuals and reducing diversity in the workforce ([111, 112]). Large
language models can be used to automate the screening of job applicants, such
as by analyzing resumes and cover letters. Since these models are trained on
vast amounts of text data, they may have internalized biases present in the
data, such as gender or racial biases. As a result, they could unintentionally
favor certain applicants or disqualify others based on factors unrelated to
their qualifications, reinforcing existing inequalities in the job market.

  * 2.

Lending: Financial institutions increasingly rely on AI models for credit
scoring and lending decisions. Biased models may unfairly penalize certain
groups, such as minority communities or individuals with lower socio-economic
status, by assigning them lower credit scores or denying them access to loans
and financial services based on biased assumptions ([113, 114, 115]). In
lending, large language models can be used to assess creditworthiness or
predict loan default risk, e.g., based on automated analysis of application or
support documents. If the data used to train these models contain historical
biases or discriminatory lending practices, the models may learn to replicate
these patterns. Consequently, they could deny loans to certain demographics or
offer unfavorable terms based on factors like race, gender, or socioeconomic
status, perpetuating financial inequality ([116]).

  * 3.

Content moderation: AI-powered content moderation systems help manage and
filter user-generated content on social media platforms and other online
communities. If these systems are trained on biased data, they may
disproportionately censor or suppress the voices of certain groups, while
allowing harmful content or misinformation from other sources to proliferate
([117, 118, 119, 120]). Language models can be employed to automatically
moderate and filter content on social media platforms or online forums.
However, these models may struggle to understand the nuances of language,
context, and cultural differences. They might over-moderate or under-moderate
certain types of content, disproportionately affecting certain groups or
topics. This could lead to censorship or the amplification of harmful content,
perpetuating biases and misinformation ([121, 122, 123, 124]).

  * 4.

Healthcare: AI models are increasingly used to support medical decision-making
and resource allocation. Biased models may result in unfair treatment for
certain patient groups, leading to disparities in healthcare access and
quality, and potentially exacerbating existing health inequalities ([125, 126,
110, 105]). Large language models can be employed for tasks such as diagnosing
diseases, recommending treatments, or analyzing patient data ([127, 128]). If
the training data includes biased or unrepresentative information, the models
may produce biased outcomes. Data used to train these models might be
predominantly collected from specific populations, leading to less accurate
predictions or recommendations for underrepresented groups. This can result in
misdiagnoses, inadequate treatment plans, or unequal access to care. Models
might unintentionally learn to associate certain diseases or conditions with
specific demographic factors, perpetuating stereotypes and potentially
influencing healthcare professionals’ decision-making. Finally, biases in
healthcare data could lead to models that prioritize certain types of
treatments or interventions over others, disproportionately benefiting certain
groups and disadvantaging others ([129, 130]).

  * 5.

Education: AI-driven educational tools and platforms can help personalize
learning experiences and improve educational outcomes. However, biased models
may perpetuate disparities by favoring certain learning styles or cultural
backgrounds, disadvantaging students from underrepresented or marginalized
communities ([131, 132]). Large language models can be used in education for
tasks such as personalized learning, grading, or content creation. If the
models are trained on biased data, they may exacerbate existing biases in
educational settings. Furthermore, if used for grading or assessing student
work, language models might internalize biases in historical grading
practices, leading to unfair evaluation of students based on factors like
race, gender, or socioeconomic status. Finally, access in itself to ChatGPT or
other AI tools for education can exacerbate preexisting inequalities.

###  5.3 Paths to AI transparency

Transparency and trust are essential components in the development and
deployment of AI systems. As these models become more integrated into various
aspects of our lives, it is increasingly important for users and regulators to
understand how they make decisions and predictions, ensuring that they operate
fairly, ethically, and responsibly.

Emphasizing transparency in AI systems can provide several benefits:

  * 1.

Informed decision-making: When users and regulators have a clear understanding
of how AI models make decisions and predictions, they can make more informed
choices about whether to use or rely on these systems in different contexts.
Transparency can empower users to evaluate the potential risks and benefits of
AI systems and make decisions that align with their values and priorities
([133, 134]).

  * 2.

Public trust: Fostering transparency can help build public trust in AI
systems, as it demonstrates a commitment to ethical development and
responsible deployment. When users and regulators can see that developers are
taking steps to ensure the fairness and equity of their models, they may be
more likely to trust and adopt these technologies ([101, 102]).

  * 3.

Ethical compliance: By promoting transparency, developers can demonstrate
their compliance with ethical guidelines and regulations, showcasing their
commitment to the responsible development of AI systems. This can help to
establish a strong reputation and foster positive relationships with users,
regulators, and other stakeholders ([135, 116]).

  * 4.

Collaborative improvement: Transparency can facilitate collaboration between
developers, researchers, policymakers, and affected communities, enabling them
to share insights and feedback that can help guide the development of more
equitable and ethical AI systems ([108, 109, 58, 110]).

In summary, emphasizing transparency and trust in AI systems, including
generative language models, is crucial for ensuring that users and regulators
have a clear understanding of how these models make decisions and predictions.
By promoting transparency, developers can demonstrate their commitment to
ethical development and responsible deployment, fostering public trust and
paving the way for more equitable and beneficial AI applications.

###  5.4 Regulatory efforts, industry standards, and ethical guidelines

As concerns about bias in AI systems continue to grow, several ongoing
regulatory efforts and industry standards have emerged to address these
challenges. AI ethics guidelines and fairness frameworks aim to provide
guidance and best practices for developers, organizations, and policymakers to
reduce bias and ensure responsible development and deployment of AI systems
([135, 136]).

Some notable efforts are summarized in Table 6. These ongoing regulatory
efforts and industry standards represent important steps in addressing bias
and promoting the responsible development of AI systems. By adhering to these
guidelines and frameworks, developers can contribute to the creation of AI
technologies that are more equitable, fair, and beneficial for all users and
communities.

Table 6: Ongoing Regulatory Efforts and Industry Standards to Address Bias in AI Effort |  Description
---|---
European Union’s AI Ethics Guidelines |  The EU’s High-Level Expert Group on Artificial Intelligence has developed a set of AI Ethics Guidelines that outline key ethical principles and requirements for trustworthy AI, including fairness, transparency, accountability, and human agency ([137]).
IEEE’s Ethically Aligned Design |  The Institute of Electrical and Electronics Engineers (IEEE) has published a comprehensive document, ”Ethically Aligned Design,” which provides recommendations and guidelines for the ethical development of AI and autonomous systems, with a focus on human rights, data agency, and technical robustness ([138]).
Partnership on AI |  A coalition of tech companies, research institutes, and civil society organizations, the Partnership on AI aims to promote the responsible development and use of AI technologies. They work on various initiatives, including fairness, transparency, and accountability, to ensure that AI benefits all of humanity ([139]).
AI Fairness 360 (AIF360) |  Developed by IBM Research, AIF360 is an open-source toolkit that provides a comprehensive suite of metrics and algorithms to help developers detect and mitigate bias in their AI models. It assists developers in understanding and addressing fairness concerns in their AI applications ([140]).
Google’s AI Principles |  Google has outlined a set of AI Principles that guide the ethical development and use of AI technologies within the company. These principles emphasize fairness, transparency, accountability, and the importance of avoiding harmful or unjust impacts ([141]).
Algorithmic Impact Assessment (AIA) |  Developed by the AI Now Institute, the AIA is a framework designed to help organizations evaluate and mitigate the potential risks and harms of AI systems. The AIA guides organizations through a structured process of identifying and addressing potential biases, discrimination, and other negative consequences of AI deployment ([142]).
OECD Recommendation of the Council on Artificial Intelligence |  The OECD Recommendation of the Council on Artificial Intelligence is a set of guidelines that provides a framework for the responsible development and deployment of AI, with five principles focused on inclusive growth, human-centered values, transparency and explainability, robustness, security and safety, and accountability ([143, 144]).

##  6 The role of human oversight and intervention

###  6.1 How to identify and mitigate bias?

Identifying and mitigating bias in AI models is essential for ensuring their
responsible and equitable use. Various methods can be employed to address bias
in AI systems:

  * 1.

Regular audits: Conducting regular audits of AI models can help identify
potential biases, errors, or unintended consequences in their outputs. These
audits involve evaluating the model’s performance against a set of predefined
metrics and criteria, which may include fairness, accuracy, and
representativeness. By monitoring AI models on an ongoing basis, developers
can detect and address biases before they become problematic ([104]).

  * 2.

Retraining with curated data: Retraining AI models with curated data can help
reduce biases in their predictions and decisions. By carefully selecting and
preparing training data that is more diverse, balanced, and representative of
different perspectives, developers can ensure that AI models learn from a
broader range of inputs and experiences, which may help mitigate the influence
of biases present in the original training data ([79, 8]).

  * 3.

Applying fairness metrics: Fairness metrics can be used to evaluate the
performance of AI models with respect to different user groups or populations
([145, 146]). By analyzing AI model outputs based on these metrics, developers
can identify potential disparities or biases in the model’s treatment of
different users and take steps to address them. Examples of fairness metrics
include demographic parity, equalized odds, and equal opportunity ([147]).

  * 4.

Algorithmic debiasing techniques: Various algorithmic techniques have been
developed to mitigate bias in AI models during training or post-processing.
Some of these techniques include adversarial training, re-sampling, and re-
weighting, which aim to minimize the influence of biased patterns and features
on the model’s predictions and decisions ([28, 148, 36, 70, 49, 100, 114,
112]).

  * 5.

Inclusion of diverse perspectives: Ensuring that AI development teams are
diverse and inclusive can help bring a wide range of perspectives and
experiences to the table, which can contribute to the identification and
mitigation of biases in AI models. By involving individuals from different
backgrounds, cultures, and disciplines, developers can create more robust,
fair, and representative AI systems ([67, 68]).

  * 6.

Human-in-the-loop approaches: Incorporating human experts into the AI model
development and decision-making processes can help provide valuable contextual
understanding and ethical judgment that AI models may lack. Humans can serve
as an additional layer of quality control, identifying biases, errors, or
unintended consequences in AI model outputs and providing feedback to improve
the model’s performance and fairness.

Next, we dive deeper into the role that human experts can play in the
responsible design and development of AI systems, including large language
models, and their continuous oversight.

###  6.2 The importance of humans in the AI loop

Emphasizing the importance of involving human experts in AI system
development, monitoring, and decision-making is crucial to ensuring the
responsible and ethical deployment of AI technologies. Humans possess the
ability to provide context and ethical judgment that AI models may lack, and
their involvement can help address potential biases, errors, and unintended
consequences that may arise from the use of these systems. Some key benefits
of involving human experts in AI system development include:

  * 1.

Contextual understanding: Human experts can provide valuable insights into the
cultural, social, and historical contexts that shape language and
communication, helping to guide AI models in generating more appropriate and
sensitive responses ([131, 149, 150]).

  * 2.

Ethical judgment: Human experts possess the moral and ethical reasoning skills
needed to evaluate the potential impacts of AI systems on users and affected
communities. By involving human experts in decision-making, we can ensure that
AI models align with ethical principles and values, such as fairness,
transparency, and accountability ([138, 135, 151, 137, 116]).

  * 3.

Bias identification and mitigation: Human experts can help identify and
address biases in AI models, working alongside developers to implement
strategies for mitigating or eliminating harmful biases and promoting more
equitable and representative AI systems ([36, 100, 112]).

  * 4.

Quality assurance and validation: Human experts can serve as a vital layer of
quality control, evaluating AI model outputs for coherence, relevance, and
potential biases, and providing feedback to improve the model’s performance,
accuracy, regulatory compliance, and trustworthiness ([152]).

  * 5.

Human override: Incorporating human experts into AI system workflows can help
strike a balance between automation and human judgment, allowing humans to
intervene and override AI model decisions when necessary to ensure fairness,
accountability, and ethical compliance ([153]).

By involving human experts in the development, monitoring, and decision-making
processes of AI systems, we can leverage their contextual understanding and
ethical judgment to complement the capabilities of AI models. This
collaborative approach can help us create AI systems that are more
responsible, equitable, and beneficial for all users, while also addressing
the potential risks and challenges associated with bias in AI.

Strategy |  Description |  References
---|---|---
Engaging with affected communities |  Involving affected communities in the development and evaluation of AI models can lead to the creation of generative AI systems that are more culturally sensitive, contextually relevant, and fair to all users |  [154, 155, 156, 157, 158, 159, 160]
Multidisciplinary collaboration |  Bringing together experts from different fields, such as computer science, social sciences, humanities, and ethics, can help to develop more robust strategies for addressing and mitigating bias in generative AI systems |  [109, 41, 156]
User feedback and evaluation |  Encouraging users to provide feedback on AI model outputs and performance can contribute to the ongoing improvement and refinement of generative AI models, ensuring that they remain fair, accurate, and relevant to users’ needs |  [161, 162, 163, 164]
Openness and transparency |  Sharing information about the methodologies, data sources, and potential biases of generative AI models can enable stakeholders to make more informed decisions about whether and how to use these technologies in different contexts |  [156, 165, 143, 135]
Establishing partnerships |  Forming partnerships between AI developers, research institutions, non-profit organizations, and other stakeholders can facilitate the sharing of knowledge, resources, and best practices, leading to the development of more equitable and responsible AI technologies |  [135]
Table 7: Strategies for addressing bias in generative AI systems

###  6.3 Possible strategies and best practices to address bias in generative
AI

Addressing and mitigating potential biases in generative AI models requires a
collaborative effort between AI developers, users, and affected communities.
Fostering a more inclusive and fair AI ecosystem involves engaging various
stakeholders in the development, evaluation, and deployment of AI
technologies. This collaboration can oversee that AI models are designed to be
more equitable, representative, and beneficial to all users.

Some key aspects of fostering collaboration in the AI ecosystem are shown in
Table 7. By fostering collaboration between AI developers, users, and affected
communities, we can work towards creating a more inclusive and fair AI
ecosystem that respects and values diverse perspectives and experiences. This
collaborative approach can help ensure that AI technologies are developed and
deployed in a way that is equitable, responsible, and beneficial for all
users, while also addressing the potential risks and challenges associated
with bias in AI.

Table 8: Future avenues for Large Language Models and generative AI research. Research Area |  Description
---|---
Fairness, Bias, and Ethics |  Addressing and minimizing biases in language models, as well as understanding their ethical implications, is a critical area of research; developing methods to detect, mitigate and prevent biases in AI models is essential.
Interpretability and Explainability |  Understanding the internal workings of large language models is a significant challenge; researchers are developing methods to make models more interpretable and explain their predictions.
Auditability and Accountability |  Large language models increasingly impact various sectors of society, influencing decision-making and shaping public discourse; ensuring that models are transparent, their actions can be traced, and those responsible for their development and deployment can be held accountable for the consequences of the AI’s actions is vital for fostering trust and maintaining ethical standards in the AI community and beyond.
Controllable and Safe AI |  Ensuring that AI models can generate outputs that align with human intentions and values is an important research question; developing methods to control AI behavior, reduce harmful outputs and improve safety measures is vital.
Societal Effects |  The societal effects and implications of the deployment of AI systems encompass a wide range of concerns, including labor markets, privacy, bias, access to technology, public discourse, security, ethics, and regulation; Observing, characterizing, quantifying and understanding the broader effects that the deployment of AI systems has on society warrant careful consideration and continued research as these technologies continue to proliferate.

##  7 Conclusions

This paper highlights the challenges and risks associated with biases in
generative language models like ChatGPT, emphasizing the need for a multi-
disciplinary, collaborative effort to develop more equitable, transparent, and
responsible AI systems that enhance a wide array of applications while
minimizing unintended consequences.

Various methods for identifying and mitigating bias in AI models were
presented, including regular audits, retraining with curated data, applying
fairness metrics, and incorporating human experts in AI system development,
monitoring, and decision-making. To achieve this goal, the development and
deployment of AI technologies should prioritize ethical principles, such as
fairness and equality, ensuring that all users and groups are treated
equitably. Human oversight plays a vital role in providing context and ethical
judgment that AI models may lack, helping to identify and address potential
biases, errors, or unintended consequences. Collaboration between AI
developers, users, and affected communities is essential for fostering a more
inclusive and fair AI ecosystem, ensuring that diverse perspectives and
experiences are considered and valued.

Continued research into methods for identifying, addressing, and mitigating
biases in AI models will be critical to advancing the state of the art and
promoting more equitable and inclusive AI systems. By bringing together
experts from various disciplines, including computer science, social sciences,
humanities, and ethics, we can foster a more comprehensive understanding of
the potential biases and ethical challenges associated with AI applications.

Fostering an open and ongoing dialogue between stakeholders is crucial for
sharing knowledge, best practices, and lessons learned from the development
and use of AI applications. This dialogue can help to raise awareness of the
potential risks and challenges associated with biases in AI models and promote
the development of strategies and guidelines for mitigating their negative
impacts.

#### Future research avenues

As the development of large language models continues, several essential
aspects of research are necessary to advance their understanding and ensure
their responsible deployment (cf., Table 8). Among these are understanding
their inner workings, addressing ethical concerns, ensuring controllability
and safety, and developing more robust evaluation methods.

One critical area of research is fairness, bias, and ethics, which involves
detecting, mitigating, and preventing biases in AI models to minimize their
impact on various sectors of society. Another important research question is
interpretability and explainability, as understanding the internal workings of
large language models remains a significant challenge. Researchers are working
to make models more interpretable and explain their predictions. Additionally,
large language models can influence decision-making and shape public
discourse, making auditability and accountability essential for fostering
trust and maintaining ethical standards in the AI community and beyond.

Controllable and safe AI is also important to ensure that AI models can
generate outputs that align with human intentions and values. Finally,
observing, characterizing, quantifying, and understanding the broader effects
that deploying AI systems has on society is critical for addressing societal
effects, which encompass a wide range of concerns, including labor markets,
privacy, bias, access to technology, public discourse, security, ethics, and
regulation. The need for a multi-disciplinary, collaborative effort to develop
more equitable, transparent, and responsible AI systems is clear.

This paper emphasized the challenges and risks associated with biases in
generative language models like ChatGPT and highlights the importance of
continued research to develop responsible AI systems that enhance a wide array
of applications while minimizing unintended consequences.

## Acknowledgements

The author is grateful to all current and past members of his lab at USC, and
the numerous colleagues and students at USC Viterbi and Annenberg, who engaged
in stimulating discussions and provided invaluable feedback about this study.

## References

  * [1] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, I. Polosukhin, Attention is all you need, Advances in neural information processing systems 30 (2017).
  * [2] J. Devlin, M.-W. Chang, K. Lee, K. Toutanova, Bert: Pre-training of deep bidirectional transformers for language understanding, arXiv preprint arXiv:1810.04805 (2018).
  * [3] A. Radford, K. Narasimhan, T. Salimans, I. Sutskever, et al., Improving language understanding by generative pre-training (2018).
  * [4] A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever, et al., Language models are unsupervised multitask learners, OpenAI blog 1 (8) (2019) 9.
  * [5] T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, et al., Language models are few-shot learners, Advances in neural information processing systems 33 (2020) 1877–1901.
  * [6] J. Schulman, F. Wolski, P. Dhariwal, A. Radford, O. Klimov, Proximal policy optimization algorithms, arXiv preprint arXiv:1707.06347 (2017).
  * [7] D. M. Ziegler, N. Stiennon, J. Wu, T. B. Brown, A. Radford, D. Amodei, P. Christiano, G. Irving, Fine-tuning language models from human preferences, arXiv preprint arXiv:1909.08593 (2019).
  * [8] L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. L. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray, et al., Training language models to follow instructions with human feedback, arXiv preprint arXiv:2203.02155 (2022).
  * [9] OpenAI, Gpt-4 technical report (2023).
  * [10] Y. Bengio, R. Ducharme, P. Vincent, A neural probabilistic language model, Advances in neural information processing systems 13 (2000).
  * [11] T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, J. Dean, Distributed representations of words and phrases and their compositionality, Advances in neural information processing systems 26 (2013).
  * [12] I. Sutskever, O. Vinyals, Q. V. Le, Sequence to sequence learning with neural networks, Advances in neural information processing systems 27 (2014).
  * [13] T. Young, D. Hazarika, S. Poria, E. Cambria, Recent trends in deep learning based natural language processing, IEEE Computational intelligenCe magazine 13 (3) (2018) 55–75.
  * [14] H. Chen, X. Liu, D. Yin, J. Tang, A survey on dialogue systems: Recent advances and new frontiers, Acm SIGKDD Explorations Newsletter 19 (2) (2017) 25–35.
  * [15] S. Zhang, E. Dinan, J. Urbanek, A. Szlam, D. Kiela, J. Weston, Personalizing dialogue agents: I have a dog, do you have pets too?, arXiv preprint arXiv:1801.07243 (2018).
  * [16] T.-H. Wen, D. Vandyke, N. Mrksic, M. Gasic, L. M. Rojas-Barahona, P.-H. Su, S. Ultes, S. Young, A network-based end-to-end trainable task-oriented dialogue system, arXiv preprint arXiv:1604.04562 (2016).
  * [17] Q. Wang, B. Li, T. Xiao, J. Zhu, C. Li, D. F. Wong, L. S. Chao, Learning deep transformer models for machine translation, arXiv preprint arXiv:1906.01787 (2019).
  * [18] A. Karakanta, J. Dehdari, J. van Genabith, Neural machine translation for low-resource languages without parallel corpora, Machine Translation 32 (2018) 167–189.
  * [19] N. Pourdamghani, K. Knight, Neighbors helping the poor: improving low-resource machine translation using related languages, Machine Translation 33 (3) (2019) 239–258.
  * [20] M. R. Costa-jussà, J. Cross, O. Çelebi, M. Elbayad, K. Heafield, K. Heffernan, E. Kalbassi, J. Lam, D. Licht, J. Maillard, et al., No language left behind: Scaling human-centered machine translation, arXiv preprint arXiv:2207.04672 (2022).
  * [21] S. Ranathunga, E.-S. A. Lee, M. Prifti Skenduli, R. Shekhar, M. Alam, R. Kaur, Neural machine translation for low-resource languages: A survey, ACM Computing Surveys 55 (11) (2023) 1–37.
  * [22] C. Christianson, J. Duncan, B. Onyshkevych, Overview of the DARPA LORELEI program, Machine Translation 32 (2018) 3–9.
  * [23] K.-C. Yang, E. Ferrara, F. Menczer, Botometer 101: Social bot practicum for computational social scientists, Journal of Computational Social Science 5 (2) (2022) 1511–1528.
  * [24] E. Ferrara, Social bot detection in the age of ChatGPT: Challenges and opportunities, First Monday 28 (6) (2023).
  * [25] A. Gilson, C. W. Safranek, T. Huang, V. Socrates, L. Chi, R. A. Taylor, D. Chartash, et al., How does ChatGPT perform on the united states medical licensing examination? the implications of large language models for medical education and knowledge assessment, JMIR Medical Education 9 (1) (2023) e45312.
  * [26] J. H. Choi, K. E. Hickman, A. Monahan, D. Schwarcz, ChatGPT goes to law school, Available at SSRN (2023).
  * [27] E. Ferrara, GenAI against humanity: Nefarious applications of generative artificial intelligence and large language models, arXiv preprint arXiv:2310.00737 (2023).
  * [28] T. Bolukbasi, K.-W. Chang, J. Y. Zou, V. Saligrama, A. T. Kalai, Man is to computer programmer as woman is to homemaker? debiasing word embeddings, Advances in neural information processing systems 29 (2016).
  * [29] A. Caliskan, J. J. Bryson, A. Narayanan, Semantics derived automatically from language corpora contain human-like biases, Science 356 (6334) (2017) 183–186.
  * [30] S. L. Blodgett, S. Barocas, H. Daumé III, H. Wallach, Language (technology) is power: A critical survey of “bias” in NLP, arXiv preprint arXiv:2005.14050 (2020).
  * [31] E. M. Bender, T. Gebru, A. McMillan-Major, S. Shmitchell, On the dangers of stochastic parrots: Can language models be too big?, in: Proceedings of the 2021 ACM conference on fairness, accountability, and transparency, 2021, pp. 610–623.
  * [32] I. Solaiman, M. Brundage, J. Clark, A. Askell, A. Herbert-Voss, J. Wu, A. Radford, G. Krueger, J. W. Kim, S. Kreps, et al., Release strategies and the social impacts of language models, arXiv preprint arXiv:1908.09203 (2019).
  * [33] D. Hovy, S. Prabhumoye, Five sources of bias in natural language processing, Language and Linguistics Compass 15 (8) (2021) e12432.
  * [34] R. Munro, S. Bethard, V. Kuperman, V. T. Lai, R. Melnick, C. Potts, T. Schnoebelen, H. Tily, Crowdsourcing and language studies: the new generation of linguistic data, in: NAACL Workshop on Creating Speech and Language Data With Amazon’s Mechanical Turk, Association for Computational Linguistics, 2010, pp. 122–130.
  * [35] J. Buolamwini, T. Gebru, Gender shades: Intersectional accuracy disparities in commercial gender classification, in: Conference on fairness, accountability and transparency, PMLR, 2018, pp. 77–91.
  * [36] E. M. Bender, B. Friedman, Data statements for natural language processing: Toward mitigating system bias and enabling better science, Transactions of the Association for Computational Linguistics 6 (2018) 587–604.
  * [37] J. Kleinberg, S. Mullainathan, M. Raghavan, Inherent trade-offs in the fair determination of risk scores, arXiv preprint arXiv:1609.05807 (2016).
  * [38] R. Benjamin, Race after technology: Abolitionist tools for the new jim code (2020).
  * [39] F. Doshi-Velez, B. Kim, Towards a rigorous science of interpretable machine learning, arXiv preprint arXiv:1702.08608 (2017).
  * [40] R. Binns, Fairness in machine learning: Lessons from political philosophy, in: Conference on fairness, accountability and transparency, PMLR, 2018, pp. 149–159.
  * [41] K. Crawford, R. Dobbe, T. Dryer, G. Fried, B. Green, E. Kaziunas, A. Kak, V. Mathur, E. McElroy, A. N. Sánchez, et al., Ai now 2019 report, New York, NY: AI Now Institute (2019).
  * [42] M. O. Prates, P. H. Avelar, L. C. Lamb, Assessing gender bias in machine translation: a case study with Google translate, Neural Computing and Applications 32 (2020) 6363–6381.
  * [43] OpenAI, GPT-4 System Card (2023).
  * [44] S. Bubeck, V. Chandrasekaran, R. Eldan, J. Gehrke, E. Horvitz, E. Kamar, P. Lee, Y. T. Lee, Y. Li, S. Lundberg, et al., Sparks of artificial general intelligence: Early experiments with gpt-4, arXiv preprint arXiv:2303.12712 (2023).
  * [45] C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, P. J. Liu, Exploring the limits of transfer learning with a unified text-to-text transformer, The Journal of Machine Learning Research 21 (1) (2020) 5485–5551.
  * [46] H. Touvron, T. Lavril, G. Izacard, X. Martinet, M.-A. Lachaux, T. Lacroix, B. Rozière, N. Goyal, E. Hambro, F. Azhar, et al., Llama: Open and efficient foundation language models, arXiv preprint arXiv:2302.13971 (2023).
  * [47] C. Wu, S. Yin, W. Qi, X. Wang, Z. Tang, N. Duan, Visual ChatGPT: Talking, drawing and editing with visual foundation models, arXiv preprint arXiv:2303.04671 (2023).
  * [48] H. R. Kirk, Y. Jun, F. Volpin, H. Iqbal, E. Benussi, F. Dreyer, A. Shtedritski, Y. Asano, Bias out-of-the-box: An empirical analysis of intersectional occupational biases in popular generative language models, Advances in neural information processing systems 34 (2021) 2611–2624.
  * [49] S. Bordia, S. R. Bowman, Identifying and reducing gender bias in word-level language models, arXiv preprint arXiv:1904.03035 (2019).
  * [50] M. T. Ribeiro, T. Wu, C. Guestrin, S. Singh, Beyond accuracy: Behavioral testing of NLP models with checklist, arXiv preprint arXiv:2005.04118 (2020).
  * [51] A. Conneau, G. Lample, M. Ranzato, L. Denoyer, H. Jégou, Word translation without parallel data, arXiv preprint arXiv:1710.04087 (2017).
  * [52] M. Johnson, M. Schuster, Q. V. Le, M. Krikun, Y. Wu, Z. Chen, N. Thorat, F. Viégas, M. Wattenberg, G. Corrado, et al., Google’s multilingual neural machine translation system: Enabling zero-shot translation, Transactions of the Association for Computational Linguistics 5 (2017) 339–351.
  * [53] T. Pires, E. Schlinger, D. Garrette, How multilingual is multilingual BERT?, arXiv preprint arXiv:1906.01502 (2019).
  * [54] S. Ruder, I. Vulić, A. Søgaard, A survey of cross-lingual word embedding models, Journal of Artificial Intelligence Research 65 (2019) 569–631.
  * [55] R. Zellers, A. Holtzman, H. Rashkin, Y. Bisk, A. Farhadi, F. Roesner, Y. Choi, Defending against neural fake news, Advances in neural information processing systems 32 (2019).
  * [56] R. T. McCoy, E. Pavlick, T. Linzen, Right for the wrong reasons: Diagnosing syntactic heuristics in natural language inference, arXiv preprint arXiv:1902.01007 (2019).
  * [57] N. A. Smith, Contextual word representations: putting words into computers, Communications of the ACM 63 (6) (2020) 66–74.
  * [58] M. Mitchell, S. Wu, A. Zaldivar, P. Barnes, L. Vasserman, B. Hutchinson, E. Spitzer, I. D. Raji, T. Gebru, Model cards for model reporting, in: Proceedings of the conference on fairness, accountability, and transparency, 2019, pp. 220–229.
  * [59] N. Garg, L. Schiebinger, D. Jurafsky, J. Zou, Word embeddings quantify 100 years of gender and ethnic stereotypes, Proceedings of the National Academy of Sciences 115 (16) (2018) E3635–E3644.
  * [60] L. Dixon, J. Li, J. Sorensen, N. Thain, L. Vasserman, Measuring and mitigating unintended bias in text classification, in: Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, 2018, pp. 67–73.
  * [61] R. W. McGee, Is ChatGPT biased against conservatives? an empirical study, An Empirical Study (February 15, 2023) (2023).
  * [62] A. Chowdhery, S. Narang, J. Devlin, M. Bosma, G. Mishra, A. Roberts, P. Barham, H. W. Chung, C. Sutton, S. Gehrmann, et al., Palm: Scaling language modeling with pathways, arXiv preprint arXiv:2204.02311 (2022).
  * [63] Z. Jiang, F. F. Xu, J. Araki, G. Neubig, How can we know what language models know?, Transactions of the Association for Computational Linguistics 8 (2020) 423–438.
  * [64] N. Carlini, F. Tramer, E. Wallace, M. Jagielski, A. Herbert-Voss, K. Lee, A. Roberts, T. B. Brown, D. Song, U. Erlingsson, et al., Extracting training data from large language models., in: USENIX Security Symposium, Vol. 6, 2021\.
  * [65] E. Ferrara, The history of digital spam, Communications of the ACM 62 (8) (2019) 82–91.
  * [66] E. Ferrara, Twitter spam and false accounts prevalence, detection, and characterization: A survey, First Monday 27 (12) (2022).
  * [67] L. Gao, S. Biderman, S. Black, L. Golding, T. Hoppe, C. Foster, J. Phang, H. He, A. Thite, N. Nabeshima, et al., The pile: An 800gb dataset of diverse text for language modeling, arXiv preprint arXiv:2101.00027 (2020).
  * [68] S. Biderman, K. Bicheno, L. Gao, Datasheet for the pile, arXiv preprint arXiv:2201.07311 (2022).
  * [69] S. Gururangan, S. Swayamdipta, O. Levy, R. Schwartz, S. R. Bowman, N. A. Smith, Annotation artifacts in natural language inference data, arXiv preprint arXiv:1803.02324 (2018).
  * [70] S. Dev, J. Phillips, Attenuating bias in word vectors, in: The 22nd International Conference on Artificial Intelligence and Statistics, PMLR, 2019, pp. 879–887.
  * [71] E. Ferrara, The butterfly effect in artificial intelligence systems: Implications for ai bias and fairness, arXiv preprint arXiv:2307.05842 (2023).
  * [72] J. Wei, X. Wang, D. Schuurmans, M. Bosma, E. Chi, Q. Le, D. Zhou, Chain of thought prompting elicits reasoning in large language models, arXiv preprint arXiv:2201.11903 (2022).
  * [73] T. Dettmers, M. Lewis, Y. Belkada, L. Zettlemoyer, LLM.int8 (): 8-bit matrix multiplication for transformers at scale, Advances in Neural Information Processing Systems 35 (2022) 30318–30332.
  * [74] A. Srivastava, A. Rastogi, A. Rao, A. A. M. Shoeb, A. Abid, A. Fisch, A. R. Brown, A. Santoro, A. Gupta, A. Garriga-Alonso, et al., Beyond the imitation game: Quantifying and extrapolating the capabilities of language models, arXiv preprint arXiv:2206.04615 (2022).
  * [75] J. Wei, Y. Tay, R. Bommasani, C. Raffel, B. Zoph, S. Borgeaud, D. Yogatama, M. Bosma, D. Zhou, D. Metzler, et al., Emergent abilities of large language models, arXiv preprint arXiv:2206.07682 (2022).
  * [76] S. Chiappa, Path-specific counterfactual fairness, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33, 2019, pp. 7801–7808.
  * [77] V. Kumar, H. Koorehdavoudi, M. Moshtaghi, A. Misra, A. Chadha, E. Ferrara, Controlled text generation with hidden representation transformations, in: Findings of the Association for Computational Linguistics: ACL 2023, Association for Computational Linguistics, Toronto, Canada, 2023, pp. 9440–9455.
  * [78] D. Hovy, S. L. Spruit, The social impact of natural language processing, in: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 2016, pp. 591–598.
  * [79] S. Gururangan, A. Marasović, S. Swayamdipta, K. Lo, I. Beltagy, D. Downey, N. A. Smith, Don’t stop pretraining: Adapt language models to domains and tasks, arXiv preprint arXiv:2004.10964 (2020).
  * [80] J. H. Park, P. Fung, One-step and two-step classification for abusive language detection on twitter, arXiv preprint arXiv:1706.01206 (2017).
  * [81] Y. Bisk, A. Holtzman, J. Thomason, J. Andreas, Y. Bengio, J. Chai, M. Lapata, A. Lazaridou, J. May, A. Nisnevich, et al., Experience grounds language, arXiv preprint arXiv:2004.10151 (2020).
  * [82] P. Bourdieu, Language and symbolic power, Harvard University Press, 1991.
  * [83] N. Fairclough, Language and power, Pearson Education, 2001.
  * [84] G. Lakoff, M. Johnson, Metaphors we live by, University of Chicago press, 2008.
  * [85] J. H. Hill, The everyday language of white racism, John Wiley & Sons, 2009.
  * [86] B. L. Whorf, Language, thought, and reality: Selected writings, MIT press, 2012\.
  * [87] M. Foucault, Archaeology of knowledge, Routledge, 2013.
  * [88] C. Geertz, The interpretation of cultures, Vol. 5043, Basic books, 1973.
  * [89] G. Hofstede, Culture’s consequences: International differences in work-related values, Vol. 5, sage, 1984.
  * [90] R. Inglehart, Christian Welzel Modernization, Cultural Change, and Democracy The Human Development Sequence, Cambridge: Cambridge university press, 2005.
  * [91] H. C. Triandis, Individualism and collectivism, Routledge, 2018.
  * [92] S. A. Friedler, C. Scheidegger, S. Venkatasubramanian, On the (im)possibility of fairness, arXiv preprint arXiv:1609.07236 (2016).
  * [93] M. B. Zafar, I. Valera, M. Gomez Rodriguez, K. P. Gummadi, Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment, in: Proceedings of the 26th international conference on world wide web, 2017, pp. 1171–1180.
  * [94] S. Barocas, M. Hardt, A. Narayanan, Fairness and machine learning: Limitations and opportunities, Fairness and Machine Learning: Limitation and Oppotunities (2019).
  * [95] S. S. Mufwene, The Ecology of Language Evolution. Cambridge Approaches to Language Contact., ERIC, 2001.
  * [96] H. Jenkins, M. Deuze, Convergence culture (2008).
  * [97] M. Castells, The rise of the network society, John wiley & sons, 2011.
  * [98] T. Gebru, J. Morgenstern, B. Vecchione, J. W. Vaughan, H. Wallach, H. D. Iii, K. Crawford, Datasheets for datasets, Communications of the ACM 64 (12) (2021) 86–92.
  * [99] A. Torralba, A. A. Efros, Unbiased look at dataset bias, in: CVPR 2011, IEEE, 2011, pp. 1521–1528.
  * [100] T. Sun, A. Gaut, S. Tang, Y. Huang, M. ElSherief, J. Zhao, D. Mirza, E. Belding, K.-W. Chang, W. Y. Wang, Mitigating gender bias in natural language processing: Literature review, arXiv preprint arXiv:1906.08976 (2019).
  * [101] S. Larsson, F. Heintz, Transparency in artificial intelligence, Internet Policy Review 9 (2) (2020).
  * [102] U. Ehsan, Q. V. Liao, M. Muller, M. O. Riedl, J. D. Weisz, Expanding explainability: Towards social transparency in AI systems, in: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, 2021, pp. 1–19.
  * [103] S. Wachter, B. Mittelstadt, L. Floridi, Transparent, explainable, and accountable AI for robotics, Science robotics 2 (6) (2017) eaan6080.
  * [104] I. D. Raji, A. Smart, R. N. White, M. Mitchell, T. Gebru, B. Hutchinson, J. Smith-Loud, D. Theron, P. Barnes, Closing the AI accountability gap: Defining an end-to-end framework for internal algorithmic auditing, in: Proceedings of the 2020 conference on fairness, accountability, and transparency, 2020, pp. 33–44.
  * [105] H. Smith, Clinical AI: opacity, accountability, responsibility and liability, AI & SOCIETY 36 (2) (2021) 535–545.
  * [106] M. R. Morris, Ai and accessibility, Communications of the ACM 63 (6) (2020) 35–37.
  * [107] R. Schwartz, J. Dodge, N. A. Smith, O. Etzioni, Green AI, Communications of the ACM 63 (12) (2020) 54–63.
  * [108] C. O’neil, Weapons of math destruction: How big data increases inequality and threatens democracy, Crown, 2017.
  * [109] K. Holstein, J. Wortman Vaughan, H. Daumé III, M. Dudik, H. Wallach, Improving fairness in machine learning systems: What do industry practitioners need?, in: Proceedings of the 2019 CHI conference on human factors in computing systems, 2019, pp. 1–16.
  * [110] R. Challen, J. Denny, M. Pitt, L. Gompels, T. Edwards, K. Tsaneva-Atanasova, Artificial intelligence, bias and clinical safety, BMJ Quality & Safety 28 (3) (2019) 231–237.
  * [111] M. Bogen, A. Rieke, Help wanted: An examination of hiring algorithms, equity, and bias (2018).
  * [112] M. Raghavan, S. Barocas, J. Kleinberg, K. Levy, Mitigating bias in algorithmic hiring: Evaluating claims and practices, in: Proceedings of the 2020 conference on fairness, accountability, and transparency, 2020, pp. 469–481.
  * [113] D. K. Citron, F. Pasquale, The scored society: Due process for automated predictions, Wash. L. Rev. 89 (2014) 1.
  * [114] N. T. Lee, P. Resnick, G. Barton, Algorithmic bias detection and mitigation: Best practices and policies to reduce consumer harms, Brookings Institute: Washington, DC, USA 2 (2019).
  * [115] B. Ustun, A. Spangher, Y. Liu, Actionable recourse in linear classification, in: Proceedings of the conference on fairness, accountability, and transparency, 2019, pp. 10–19.
  * [116] L. Weidinger, J. Mellor, M. Rauh, C. Griffin, J. Uesato, P.-S. Huang, M. Cheng, M. Glaese, B. Balle, A. Kasirzadeh, et al., Ethical and social risks of harm from language models, arXiv preprint arXiv:2112.04359 (2021).
  * [117] T. Gillespie, Custodians of the Internet: Platforms, content moderation, and the hidden decisions that shape social media, Yale University Press, 2018.
  * [118] S. T. Roberts, Behind the screen, Yale University Press, 2019.
  * [119] E. C. Choi, E. Ferrara, Automated claim matching with large language models: Empowering fact-checkers in the fight against misinformation, arXiv preprint arXiv:2310.09223 (2023).
  * [120] I. Augenstein, T. Baldwin, M. Cha, T. Chakraborty, G. L. Ciampaglia, D. Corney, R. DiResta, E. Ferrara, S. Hale, A. Halevy, et al., Factuality challenges in the era of large language models, arXiv preprint arXiv:2310.05189 (2023).
  * [121] M. Sap, D. Card, S. Gabriel, Y. Choi, N. A. Smith, The risk of racial bias in hate speech detection, in: Proceedings of the 57th annual meeting of the association for computational linguistics, 2019, pp. 1668–1678.
  * [122] T. Davidson, D. Bhattacharya, I. Weber, Racial bias in hate speech and abusive language detection datasets, arXiv preprint arXiv:1905.12516 (2019).
  * [123] I. V. Pasquetto, B. Swire-Thompson, M. A. Amazeen, F. Benevenuto, N. M. Brashier, R. M. Bond, L. C. Bozarth, C. Budak, U. K. Ecker, L. K. Fazio, et al., Tackling misinformation: What researchers could do with social media data, The Harvard Kennedy School Misinformation Review (2020).
  * [124] F. Ezzeddine, O. Ayoub, S. Giordano, G. Nogara, I. Sbeity, E. Ferrara, L. Luceri, Exposing influence campaigns in the age of llms: a behavioral-based ai approach to detecting state-sponsored trolls, EPJ Data Science 12 (1) (2023) 46.
  * [125] M. A. Gianfrancesco, S. Tamang, J. Yazdany, G. Schmajuk, Potential biases in machine learning algorithms using electronic health record data, JAMA internal medicine 178 (11) (2018) 1544–1547.
  * [126] Z. Obermeyer, B. Powers, C. Vogeli, S. Mullainathan, Dissecting racial bias in an algorithm used to manage the health of populations, Science 366 (6464) (2019) 447–453.
  * [127] T. Davenport, R. Kalakota, The potential for artificial intelligence in healthcare, Future healthcare journal 6 (2) (2019) 94.
  * [128] K. Y. Ngiam, W. Khor, Big data and machine learning algorithms for health-care delivery, The Lancet Oncology 20 (5) (2019) e262–e273.
  * [129] J. K. Paulus, D. M. Kent, Predictably unequal: understanding and addressing concerns that algorithmic clinical prediction may increase health disparities, NPJ digital medicine 3 (1) (2020) 99.
  * [130] E. Ferrara, Fairness and bias in artificial intelligence: A brief survey of sources, impacts, and mitigation strategies, arXiv preprint arXiv:2304.07683 (2023).
  * [131] M. C. Elish, D. Boyd, Situating methods in the magic of Big Data and AI, Communication monographs 85 (1) (2018) 57–80.
  * [132] N. Selwyn, Should robots replace teachers?: AI and the future of education, John Wiley & Sons, 2019.
  * [133] B. Goodman, S. Flaxman, European union regulations on algorithmic decision-making and a “right to explanation”, AI magazine 38 (3) (2017) 50–57.
  * [134] R. Guidotti, A. Monreale, S. Ruggieri, F. Turini, F. Giannotti, D. Pedreschi, A survey of methods for explaining black box models, ACM computing surveys (CSUR) 51 (5) (2018) 1–42.
  * [135] A. Jobin, M. Ienca, E. Vayena, The global landscape of AI ethics guidelines, Nature Machine Intelligence 1 (9) (2019) 389–399.
  * [136] L. Floridi, Establishing the rules for building trustworthy AI, Nature Machine Intelligence 1 (6) (2019) 261–262.
  * [137] Independent High-Level Expert Group on Artificial Intelligence, Ethics guidelines for trustworthy AI, Technical report, European Commission (2019).
  * [138] K. Shahriari, M. Shahriari, Ieee standard review—ethically aligned design: A vision for prioritizing human wellbeing with artificial intelligence and autonomous systems, in: 2017 IEEE Canada International Humanitarian Technology Conference (IHTC), IEEE, 2017, pp. 197–201.
  * [139] J. Heer, The partnership on AI, AI Matters 4 (3) (2018) 25–26.
  * [140] R. K. Bellamy, K. Dey, M. Hind, S. C. Hoffman, S. Houde, K. Kannan, P. Lohia, J. Martino, S. Mehta, A. Mojsilović, et al., Ai fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias, IBM Journal of Research and Development 63 (4/5) (2019) 4–1.
  * [141] S. Pichai, Ai at Google: our principles, The Keyword 7 (2018) 1–3.
  * [142] D. Reisman, J. Schultz, K. Crawford, M. Whittaker, Algorithmic impact assessments: A practical framework for public agency, AI Now (2018).
  * [143] C. Cath, S. Wachter, B. Mittelstadt, M. Taddeo, L. Floridi, Artificial intelligence and the ‘good society’: the US, EU, and UK approach, Science and engineering ethics 24 (2018) 505–528.
  * [144] K. Yeung, Recommendation of the council on artificial intelligence (OECD), International legal materials 59 (1) (2020) 27–34.
  * [145] S. Yan, H.-t. Kao, E. Ferrara, Fair class balancing: Enhancing model fairness without observing sensitive attributes, in: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, 2020, pp. 1715–1724.
  * [146] Y. H. Ezzeldin, S. Yan, C. He, E. Ferrara, S. Avestimehr, Fairfed: Enabling group fairness in federated learning, in: Proceedings of the 37th AAAI Conference on Artificial Intelligence, 2023.
  * [147] N. Mehrabi, F. Morstatter, N. Saxena, K. Lerman, A. Galstyan, A survey on bias and fairness in machine learning, ACM Computing Surveys (CSUR) 54 (6) (2021) 1–35.
  * [148] B. H. Zhang, B. Lemoine, M. Mitchell, Mitigating unwanted biases with adversarial learning, in: Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, 2018, pp. 335–340.
  * [149] D. Leslie, Understanding artificial intelligence ethics and safety, arXiv preprint arXiv:1906.05684 (2019).
  * [150] Y. K. Dwivedi, L. Hughes, E. Ismagilova, G. Aarts, C. Coombs, T. Crick, Y. Duan, R. Dwivedi, J. Edwards, A. Eirug, et al., Artificial intelligence (ai): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy, International Journal of Information Management 57 (2021) 101994.
  * [151] N. A. Smuha, The EU approach to ethics guidelines for trustworthy artificial intelligence, Computer Law Review International 20 (4) (2019) 97–106.
  * [152] M. Felderer, R. Ramler, Quality assurance for ai-based systems: Overview and challenges (introduction to interactive session), in: Software Quality: Future Perspectives on Software Engineering Quality: 13th International Conference, SWQD 2021, Vienna, Austria, January 19–21, 2021, Proceedings 13, Springer, 2021, pp. 33–42.
  * [153] A. Etzioni, O. Etzioni, Keeping AI legal, Vand. J. Ent. & Tech. L. 19 (2016) 133\.
  * [154] W. Wallach, C. Allen, I. Smit, Machine morality: bottom-up and top-down approaches for modelling human moral faculties, AI & Society 22 (2008) 565–582.
  * [155] L. Taylor, R. Schroeder, Is bigger better? the emergence of big data as a tool for international development policy, GeoJournal 80 (2015) 503–518.
  * [156] B. D. Mittelstadt, P. Allo, M. Taddeo, S. Wachter, L. Floridi, The ethics of algorithms: Mapping the debate, Big Data & Society 3 (2) (2016) 2053951716679679\.
  * [157] V. Eubanks, Automating inequality: How high-tech tools profile, police, and punish the poor, St. Martin’s Press, 2018.
  * [158] M. L. Gray, S. Suri, Ghost work: How to stop Silicon Valley from building a new global underclass, Eamon Dolan Books, 2019.
  * [159] S. M. West, M. Whittaker, K. Crawford, Discriminating systems, AI Now (2019).
  * [160] S. Costanza-Chock, Design justice: Community-led practices to build the worlds we need, The MIT Press, 2020.
  * [161] H. Cramer, V. Evers, S. Ramlal, M. Van Someren, L. Rutledge, N. Stash, L. Aroyo, B. Wielinga, The effects of transparency on trust in and acceptance of a content-based art recommender, User Modeling and User-adapted interaction 18 (2008) 455–496.
  * [162] M. K. Lee, D. Kusbit, E. Metsky, L. Dabbish, Working with machines: The impact of algorithmic and data-driven management on human workers, in: Proceedings of the 33rd annual ACM conference on human factors in computing systems, 2015, pp. 1603–1612.
  * [163] S. Amershi, D. Weld, M. Vorvoreanu, A. Fourney, B. Nushi, P. Collisson, J. Suh, S. Iqbal, P. N. Bennett, K. Inkpen, et al., Guidelines for human-AI interaction, in: Proceedings of the 2019 chi conference on human factors in computing systems, 2019, pp. 1–13.
  * [164] J. Stoyanovich, B. Howe, H. Jagadish, Responsible data management, Proceedings of the VLDB Endowment 13 (12) (2020).
  * [165] M. Taddeo, L. Floridi, Regulate artificial intelligence to avert cyber arms race (2018).