# AI and Identity

Sri Yash Tadimalla, Mary Lou Maher 1

###### Abstract

AI-empowered technologies’ impact on the world is undeniable, reshaping
industries, revolutionizing how humans interact with technology, transforming
educational paradigms, and redefining social codes. However, this rapid growth
is accompanied by two notable challenges: a lack of diversity within the AI
field and a widening AI divide. In this context, This paper examines the
intersection of AI and identity as a pathway to understand biases,
inequalities, and ethical considerations in AI development and deployment. We
present a multifaceted definition of AI identity, which encompasses its
creators, applications, and their broader impacts. Understanding AI’s identity
involves understanding the associations between the individuals involved in
AI’s development, the technologies produced, and the social, ethical, and
psychological implications. After exploring the AI identity ecosystem and its
societal dynamics, We propose a framework that highlights the need for
diversity in AI across three dimensions: Creators, Creations, and Consequences
through the lens of identity. This paper proposes the need for a comprehensive
approach to fostering a more inclusive and responsible AI ecosystem through
the lens of identity.

## Introduction

With the rise of AI, there’s an emergence of complex questions about the very
fabric of its existence. Whether it is politicians mulling over policy
implications (Coeckelbergh 2022), civilians navigating the implications of AI
in daily life (Liu et al. 2023), Educational institution’s decisions on AI
usage in learning and teaching (Mouta, Pinto-Llorente, and Torrecilla-Sánchez
2023), Tech organizations using AI to innovate and create business
applications (Bessen et al. 2023) or military organizations strategizing
defense (De Spiegeleire, Maas, and Sweijs 2017), each group views AI through
its unique lens (Calo 2017). The current discourse on Artificial Intelligence
(AI) reflects a complex interplay of optimism and concern regarding AI’s
impact on global equity (Crawford 2021). This narrative is increasingly
focused on the ”AI Divide,” a term that encapsulates the challenges AI poses
in perpetuating disparities between those who have access and those who don’t
(Carter, Liu, and Cantrell 2020).

The global population is currently around 8.1 billion. As of 2023, it’s
estimated that there were roughly 3.4 billion people employed worldwide, with
2 billion people working within the informal economy (Dyvik 2023). The ICT
sector was projected to employ 55.3 million people full-time by 2020,
according to estimates made before the COVID-19 pandemic (Sherif 2023). Within
this sector, a smaller number of workers are actively involved in developing
or working with AI tools and systems. Looking forward, it’s expected that by
2025, up to 97 million individuals will be working in the AI field.
Furthermore, the US AI market is anticipated to reach 299.64 billion dollars
by 2026. AI tools and systems are projected to impact nearly 40 percent of
jobs globally, with this figure rising to about 60 percent in advanced
economies (Howarth 2024). These changes will lead to a combination of job
replacement and augmentation with the influencing factor being access to or
lack of access to the internet and digital infrastructure.

This scenario underscores a significant power imbalance, where a relatively
small portion of the global population has a profound influence on the lives
and livelihoods of the majority as visualized in Figure 1. In this rapidly
evolving landscape and widening AI divide, a notable challenge that has
emerged is the significant lack of diversity among the creators, researchers,
and educators in the AI field. This homogeneity within the AI workforce, if it
persists into the predicted 96 million AI workforce, represents more than an
issue of fairness or representation; it fundamentally affects the design,
implementation, and impact of AI technologies across different populations and
societies. (Stathoulopoulos and Mateos-Garcia 2019).

The concern is that while AI has immense potential for progress in areas like
healthcare, agriculture, and education, its development is predominantly
driven by the private sector (Abboud, Arya, and Pandi 2020) (Chui et al.
2023), raising critical questions about its applicability and fairness
(Dwivedi et al. 2021). Renowned AI safety groups and industry leaders have
raised alarms not just about existential risks akin to pandemics and nuclear
war but also about the social inequities AI might exacerbate (Hinton et al.
2023) (Buolamwini and Gebru 2018). Scholars have highlighted the immediate
dangers of AI, especially its impact on marginalized and vulnerable
populations (Birhane et al. 2022). This is evidenced by the shortcomings of
existing AI models, including facial recognition inaccuracies and the limited
effectiveness of Large Language Models (LLMs) in addressing the diverse needs
of global populations (Coulter 2023). Congressional testimony on the need for
AI regulation and the collective call by AI researchers for a pause in AI
advancements emphasize the urgency of addressing these issues (Pause Giant
2023). Moreover, the public’s divided perception of AI, akin to debates in
cybersecurity (Kovačević, Putnik, and Tošković 2020), points to a significant
gap between the aspirational goals of various AI frameworks and principles,
and the reality of their implementation, especially in diverse global
contexts(Hagerty and Rubinov 2019).

Figure 1:  Representation of global workforce and AI industry impact

This evolving narrative and the need to solve the AI diversity gap underscores
the need for a nuanced, socio-technical approach to understanding the
multifaceted identity of AI (Lee and Qiufan 2021). Through an exploration of
how AI is created, applied, and perceived (Kim et al. 2023) across various
societal strata, as presented in the AI identity ecosystem (Figure 2), we can
answer questions like, Who creates AI? Whose values does it reflect? How is it
perceived across different cultures and demographics? And, how do all these
factors influence the technology’s trajectory?(Bingley et al. 2023) It is
intricately woven with the myriad societal dynamics that shape identity
formation and influence perceptions. The answers to these questions are
pivotal as they influence not only the technological trajectories of AI but
also its societal implications as presented in the AI identity Framework
(Figure 2). It is critical to underscore that the identity of AI isn’t simply
a reflection of its perception but a pathway to bridging the AI divide and
improving inclusivity efforts.

Figure 2: The AI Identity Research Framework

## Background

The process of developing the construct of ”AI identity” begins with
understanding the drivers and landscape of AI development (Christian 2020)
which can be understood through the AI identity ecosystem (Figure 2). The AI
ecosystem is a dynamic and interwoven network comprising a myriad of essential
components that collectively drive the evolution and application of artificial
intelligence (Jacobides, Brusoni, and Candelon 2021) (Yoo 2020). We will try
to explore the various components of the AI ecosystem in this paper. Firstly,
the building blocks of the system lay in the technologies and tools, such as
machine learning algorithms and frameworks like TensorFlow and PyTorch (Paszke
et al. 2019). Complementing these are specialized hardware solutions like GPUs
and TPUs, designed to cater to the computational demands of AI (Batra et al.
2019). Data, the lifeblood of AI, flows in from diverse sources, including IoT
devices and online interactions, and is stored and processed using advanced
tools (Aragon et al. 2022).

However, in understanding AI’s evolution it is essential to understand the
confluence of various human endeavors that drive its development as mentioned
above. In this section particularly we focus more on the identity of the
creators as it has cascading effect on the other layers. These human
contributors do more than just define AI’s trajectory; they embed societal
norms, values, and biases into the technology, shaping the future of our
automated world (Cave and Dihal 2020). The role of research cannot be
overstated, as institutions constantly push the boundaries of what’s possible,
often dictating the future direction of AI (Basole and Accenture 2021).
Simultaneously, both tech behemoths and startups shape the AI landscape with
their innovative products and platforms. Open-source communities bolster this
growth, fostering collaboration and ensuring that AI tools remain accessible
to all (Quan and Sanderson 2018).

As the influence of AI permeates society, regulatory and ethical bodies are
stepping in to ensure its deployment is responsible and aligned with societal
values (Minkkinen, Zimmer, and Mäntymäki 2021). End-users and consumers, both
individuals and businesses, are the final and important piece of the puzzle,
adopting AI solutions and applications and influencing their development
through feedback. Integral to all these components and often the ones that
shape the direction of development is the financial pillar of investors and
funding entities that fuel the growth of AI across sectors (Khanna 2023).
Lastly, the AI ecosystem is enriched by educational institutions and platforms
that impart knowledge, nurturing the current and future generations of AI
enthusiasts and professionals through AI literacy (Long and Magerko 2020) and
AI education (Schiff 2022).

The identity of AI continues to extend beyond just its creators and the
diversity within the field. It encompasses a range of social considerations
that shape its existence and impact(Cole 1991). Ethical concerns surrounding
AI, such as bias in algorithms, privacy issues, and the societal implications
of AI, require careful attention and mitigation (Amershi 2020). Human-AI
collaboration is evolving to where AI can augment human capabilities and
automate tasks, necessitating the exploration of inclusive ways for humans and
AI to collaborate seamlessly (Amershi et al. 2019). The tendency for humans to
personify or anthropomorphize AI systems raises psychological and sociological
questions about the implications of interacting with AI, especially in
education (Maher, Tadimalla, and Dhamani 2023) and work environments
(Mirbabaie et al. 2022). Moreover, public sentiment around AI is often
polarized based on users’ backgrounds, leading to inaccurate narratives about
AI in society(Prahl and Goh 2021). These narratives and biases, combined with
the digital divide that exists in society and computer science exclude some
and inhibit many’s access to AI careers, especially for certain groups of
underrepresented minorities, and women (Shams, Zowghi, and Bano 2023).

By examining the roles, interactions, and contributions of these diverse
stakeholders as presented in the AI Ecosystem (Figure 3) we can gain a
comprehensive understanding of how AI systems are created, operate, and
evolve. By foregrounding these concepts, we aim to dissect how they influence
AI creators and their creations. Through the depiction of layers and various
pipelines (connections) across the layers that lead to various consequences or
issues in the AI landscape, Figures 1 and 2 together show the myriad ways AI
technologies can either reinforce existing societal disparities or potentially
pave the way for a more inclusive and equitable future.

By dissecting these relationships, we can better comprehend and address the
disproportionate consequences and impacts that AI can have on various segments
of society. The construct of human identity is influenced as depicted in the
image is present across the layers, present in the identities, data,
perceptions, and experiences of end users in the AI ecosystem. Here Human
Identity, as defined within intersectionality (Crenshaw 2013) and identity
theory(Jenkins 2014), encompasses the multifaceted and interconnected aspects
of an individual’s social categories, including race, gender (Scheuerman et
al. 2020), class (Inaba and Togawa 2021), sexuality (Keyes, Hitzig, and Blell
2021), and disability (Trewin et al. 2019), nationality and age (Pollack 2005)
etc, recognizing that these overlapping identities shape unique experiences of
privilege and oppression (Benjamin 2019).

## AI Identity

Sociology Literature provides a comprehensive exploration of how individuals’
identities are formed (Stets and Burke 2000), negotiated (Jenkins 2014), and
transformed (Collins and Bilge 2020) within the context of social structures
and interactions, offering insightful analysis on the complexity of identity
as both a personal and social construct (Albert 1998). This concept also
extends to objects, organizations, and technology (Wood and Smith 2004),
illustrating how identity formation transcends individual experiences to
encompass broader societal interactions and constructs (Hirsch 1992). Often
discussions and models involving identity in the AI landscape and ecosystem
are heavily centered on the technological and economic aspects (Mitchell
2020). With efforts focused on defining and explaining what AI is (Wang 2019)
and how to understand its usage (Devedzic 2022) (Touretzky, Gardner-McCune,
and Seehorn 2023), how AI perceives the human identity (Scheuerman et al.
2020) (Schlesinger, O’Hara, and Taylor 2018) (Tian et al. 2017), how humans
perceive AI (Ragot, Martin, and Cojean 2020) (Lima et al. 2020) (Shinners et
al. 2022), how humans interact with the AI (Keyes, Hitzig, and Blell 2021)
(Ashktorab et al. 2020) and how AI influences the human identity in various
scenarios (Cao et al. 2023). This includes discussions on algorithms (Noble
2018) (Pasquale 2015), data(Aragon et al. 2016) (Aragon et al. 2022), hardware
infrastructure (Batra et al. 2019), application areas (Huang and Rust 2021),
market growth, company roles (Alahmad and Robert 2020), and investment trends
(Mir, Kar, and Gupta 2022). While these aspects are undeniably crucial, they
often overshadow the deeper, more intricate layers of AI’s perception and
relationship with the concept of identity.

We define ”AI Identity” in two dimensions: internal and external.

Internally, AI Identity includes the collective characteristics, values, and
ethical considerations embodied in the creation of AI technologies.
Externally, AI identity is shaped by individual perception, societal impact,
and cultural norms.

These dimensions form a comprehensive view of AI identity, highlighting the
interplay between the creation of technology itself and its broader
interaction with society. This means understanding the place of AI in society,
its development, interactions with individuals (Gutoreva 2024), and the
nuances of its impact on various facets of human life. The identity of AI is
intricately linked to various ethical dilemmas, including responsibility,
accountability, fairness, transparency, and trust (Benjamin 2019). These
issues are central to the ongoing discussions surrounding the regulation and
governance of AI, as well as its cultural and social impacts(Arora et al.
2023). Furthermore, it is vital to recognize the role of media representations
of AI in popular culture, as they significantly shape public attitudes and
beliefs about this technology. In this context, the emergence of Human-
Centered AI (HCAI) (Shneiderman 2021), with its emphasis on considering human
values and agency, represents a pivotal shift in the AI landscape.
International organizations like the European Union and research and education
institutions are advocating for HCAI, promoting a humanistic and ethical
approach that enhances human capabilities while addressing the multifaceted
challenges associated with AI identity and its broader societal implications
(Capel and Brereton 2023). These dynamic changes in the AI landscape and the
important role that education plays in creating opportunities for minorities
to participate in AI (Salas-Pilco, Xiao, and Oshima 2022), led the authors to
focus on this topic. In the next three sections of the paper, we look deeper
into the layers of identity in AI: the creators, the creations, and the
consequences that emerge from the identity ecosystem. Figure 4 summarizes our
exploration. By examining the intersection of AI with identity, we aim to shed
light on the complex dimensions of AI identity and its implications for
individuals and society as a whole.

Figure 3: The AI Identity Ecosystem

##  The Creators of AI

The development of AI is influenced by its creators, whose diverse backgrounds
and identities are crucial in shaping the technology’s direction (Schiff et
al. 2020). Despite significant advancements, AI suffers from a notable lack of
diversity among its developers, echoing broader inclusivity challenges within
the tech industry (Inaba and Togawa 2021). Diversity is not just about
fairness but is essential for creating AI systems that are unbiased, ethical,
and beneficial for society (Shams, Zowghi, and Bano 2023). Thus, enhancing the
diversity of AI creators is imperative to ensure that the technology reflects
a broad spectrum of human experiences and values, contributing positively to
societal progress(Chubb, Reed, and Cowling 2022). It is important to note that
this layer of the ecosystem is closely connected with the internal dimension
of AI identity. In the list below, we highlight some of the key groups
(individual and organizational) that influence who the creators are,
illustrating the nature of AI’s development landscape. The goals of AI are
often defined by this layer of AI ecosystem and when we look at these key
groups, this position paper acknowledges the need to increase diversity across
all of the groups across sectors and disciplines.

  * •

Researchers, Scientists, and Academics: The development of AI is driven by a
community of researchers, scientists, and academics from various disciplines
(Hartmann and Henkel 2020). Experts in computer science, mathematics, biology,
linguistics, cognitive science, and engineering collaborate to advance AI
technologies. They conduct research, publish academic papers, and contribute
to the theoretical foundations of AI(Stathoulopoulos and Mateos-Garcia 2019).
Higher education institutions also play a big role in shaping the perception
and standards (Mouta, Pinto-Llorente, and Torrecilla-Sánchez 2023)that these
students carry into their professional careers. With HCI, interaction design,
and UX communities contributing to AI work on addressing the gap in discourse
about AI’s relationship to design practice (Loi et al. 2019), higher education
institutions and private research institutions are important actors in shaping
the diversity in the field of AI (Ahmed and Wahed 2020) (Whittaker et al.
2018).

  * •

Students and Higher Education Institutions: Students pursuing higher
education, such as undergraduate and graduate programs, play a vital role in
the creation and advancement of AI. They learn the fundamental concepts and
skills necessary for AI development and contribute fresh perspectives and
ideas (Touretzky et al. 2019). Many universities and educational institutions
offer specialized AI programs and research opportunities (Luckin and Holmes
2016), nurturing the next generation of AI creators beginning from K-12 or
high school (Adejoro et al. 2023). It is important to not here the
disciplinary differences across AI perception and adoption across various
disciplines (Zawacki-Richter et al. 2019)

  * •

Corporate and Private Organizations: Numerous companies and organizations are
actively involved in the development and deployment of AI technologies
(Jacobides, Brusoni, and Candelon 2021). Major tech companies like Google,
Microsoft, and IBM invest significant resources into AI research and
development (de Laat 2021). Startups and research organizations focused on AI
innovation also contribute to the creation of novel AI applications and
technologies (Färber and Tampakis 2024). Many historically consumer or
service-oriented organizations and companies are also building in-house AI
solutions across sectors (Simon and Isaza-Ibarra 2023).

  * •

Engineers and Developers: The creation of AI systems requires the expertise of
engineers and developers who design and build the underlying software and
hardware infrastructure. They develop algorithms, models, and frameworks that
power AI applications. These professionals work on coding, testing, and
optimizing AI systems to enhance their performance and capabilities (Griffin,
Green, and Welie 2023).

  * •

Collaborative Communities and Open-Source Projects: Collaboration and open-
source initiatives play a crucial role in AI creation. Online communities,
forums, and platforms facilitate knowledge sharing, collaboration, and the
exchange of ideas among AI enthusiasts and professionals (Bostrom 2018)
(Hibbard 2008). Open-source projects like TensorFlow and PyTorch provide
accessible frameworks and tools that empower developers to create AI solutions
(Paszke et al. 2019). Non-profits and professional societies are also getting
involved with building standards and expectations around AI integration from
the lenses of ethics and standards.

  * •

Government and Policy Institutions: Governments and policy institutions
contribute to the creation of AI by establishing frameworks, regulations, and
policies that guide its development and deployment (Bareis and Katzenbach
2022) (Luo and Van Assche 2023). The government also invests in the
development of AI through its military/defense wing to be up to date with
private system developments. Mulitaleral Global organizations in the ICT space
are trying to address ethical considerations, and privacy concerns, and ensure
responsible and accountable AI practices (Vacarelu 2022) (Floridi et al.
2021). Government-funded National research initiatives and grants also support
AI research and innovation (Donlon 2024).

## The Creation of AI

To understand the definition of AI and its application in various scenarios it
is important to understand the very building blocks of AI, and how they work
and interact with the physical world (Raghavan 2021). This layer of the
ecosystem acts as the mechanism or conduit that translate the goals reflected
by the identity of the creators of AI into the consequences and considerations
layer of AI Identity ecosystem, and therefore is key to understanding both the
external and internal aspects of AI Identity. this should be explained in the
beginning of this section and then summarized at the end of this section. In
this paper, we will adopt a comprehensive approach to understanding the
workings of artificial intelligence (AI) by analyzing it from both technical
and socio-technical perspectives. It is simultaneously important to understand
the intricate connection between the infrastructure layer and the growth of
AI’s impact on society through these two lenses.

The technical approach involves understanding AI Models and Applications,
where we explore the technical foundations and applications of AI, including
chatbots, virtual assistants, language processing, recommendation systems,
domain-specific applications, autonomous systems, robotics, machine learning
models, computer vision, and generative models. This layer represents the nuts
and bolts of AI, where one learns what these technologies are, how they
operate, and the specific tasks they are designed to perform. By understanding
the mechanics and functionalities of these AI applications, one can appreciate
the technical goals and innovations driving AI development. It is important to
note here that the development of these goals have historically been oriented
around efficiency and performance not about diversity and inclusion.

Conversely, the socio-technical approach centers on AI Frameworks and
Principles, focusing on the overarching frameworks and principles guiding the
ethical, responsible, and equitable development and deployment of AI
technologies. This layer encompasses Trustworthy AI, Responsible AI, Ethical
AI, AI for Good, Human-Centered AI, and Equitable AI. Here, we assess how
these guiding principles influence the design, purpose, and impact of AI
technologies on society. This approach allows us to understand the broader
implications of AI, including ethical considerations, societal impacts, and
the role of AI in promoting social good and addressing global challenges.

This infrastructure layer is not only a precursor to the technical layer’s
growth but also a fundamental aspect that interweaves with the socio-technical
layer, affecting how AI integrates and resonates within human society. Through
its capacity to support and scale AI development, It not only provides the
technical means for AI’s existence but also influences the objectives, ethics,
and inclusivity of AI systems by virtue of access. By enabling or limiting the
development of certain AI applications, the infrastructure layer shapes the AI
identity, reflecting the priorities and values of its creators and users.
Furthermore, by offering metrics for capacity building, the infrastructure
layer facilitates the assessment of global AI development efforts, guiding
policy, investment, and education towards a more equitable and inclusive AI
future.

### The Infrastructure Layer

The infrastructure underpinning artificial intelligence (AI) is the bedrock
upon which the technical evolution and societal integration of AI is built.
This infrastructure encompasses the essential tools, hardware, and frameworks
necessary for the development, training, and deployment of AI systems,
including data processing capabilities, computational power, and cloud
services. These components are indispensable for the processing of vast
datasets, supporting complex model training, and ensuring the scalable
deployment of AI applications. As such, the infrastructure directly influences
the pace and direction of AI innovation. The disparities in AI capabilities
across different regions highlight the need for global cooperation and
investment in AI infrastructure. Consequently, infrastructure availability
becomes a crucial metric for gauging capacity-building efforts, underscoring
the importance of democratizing AI benefits to mitigate regional disparities.
As AI technologies become increasingly embedded in everyday life, the
infrastructure that supports these technologies plays a vital role in
determining how they are received, understood, and integrated into various
cultural contexts. This interaction between AI and society influences the
design, functionality, and adoption of AI systems, necessitating a thoughtful
approach to infrastructure development that considers and reflects diverse
human values and societal needs.

  * •

Large Language Models (LLMs): Large Language Models, such as GPT (Generative
Pretrained Transformer) and BERT (Bidirectional Encoder Representations from
Transformers), represent significant advancements in natural language
processing and understanding. These models, which learn from vast amounts of
text data, have revolutionized how machines understand and generate human-like
text, enabling a wide range of applications from automated writing assistants
to advanced chatbots. The development and refinement of LLMs require
substantial computational resources and sophisticated algorithms, showcasing
the importance of robust infrastructure in AI innovation (Devlin et al. 2018)
(Brown et al. 2020).

  * •

Data:  Data serves as the cornerstone for training AI models, providing the
raw material from which machines learn and make inferences. The quality,
diversity, and volume of data directly impact the performance and bias in AI
systems. Effective data management and processing infrastructure are crucial
for handling the increasing scale of data, necessitating advanced storage
solutions and data processing frameworks (Hartmann and Henkel 2020). The
ethical considerations in data collection and use are also paramount,
emphasizing the need for transparency and fairness in AI (Aragon et al. 2022).

  * •

Curriculum: The AI curriculum encompasses the educational resources and
programs designed to equip individuals with the knowledge and skills required
to develop and manage AI technologies. This includes courses on machine
learning, ethics in AI, data science, and specialized AI applications. The
development of a comprehensive AI curriculum that is accessible and inclusive
is essential for fostering a diverse and skilled workforce capable of
advancing AI technology while considering its societal impacts (Maher and
Tadimalla 2024) (Long and Magerko 2020) (Schiff 2022) (Song et al. 2024).

  * •

Standards and Policy: Standards and policies play a critical role in guiding
the ethical development, deployment, and governance of AI technologies. These
frameworks help ensure that AI systems are developed responsibly, promoting
transparency, accountability, and fairness. International organizations and
regulatory bodies are increasingly focusing on developing AI standards and
policies that balance innovation with ethical considerations and human rights
(Jobin, Ienca, and Vayena 2019) (Long et al. 2023)

  * •

Hardware: The hardware infrastructure, including GPUs (Graphics Processing
Units) and TPUs (Tensor Processing Units), is fundamental for training and
running AI models (Jouppi et al. 2017). These specialized processors can
handle the parallel processing of large datasets and complex algorithms,
making them indispensable for deep learning applications. The development of
more efficient and powerful hardware is crucial for advancing AI capabilities
and making AI more accessible and sustainable (Merritt 2024).

  * •

Cloud and Digital Tools: Cloud computing and digital tools offer scalable and
flexible resources for AI development and deployment. Cloud services provide
access to computational power, storage, and AI development tools on-demand,
facilitating the rapid prototyping and scaling of AI applications. This
infrastructure supports the collaborative development of AI, enabling access
to cutting-edge technology without significant upfront investment (Armbrust et
al. 2010).

In essence, the infrastructure supporting AI is not just a foundation for its
technical growth but also a mediator of its interaction with society,
influencing both the capabilities of AI technologies and their alignment with
human values.

### The Technical Layer: AI Models and Applications

Understanding the foundation models of AI is essential to recognize how data
influences AI’s identity and its diverse applications, from enhancing daily
life to revolutionizing industries. While AI moves beyond the imagery of
futuristic robots to practical applications like chatbots, self-driving cars,
and medical diagnosis, concerns about inclusivity and a human-centric focus
persist. The distinction between Symbolic AI and Connectionist AI, such as
deep learning, highlights different approaches to AI development, emphasizing
the need for AI to be both transparent and grounded in human values.

As AI’s applications extend from machine learning models in image recognition
to personalized recommendation systems and beyond, the adaptability and
potential of AI are evident. However, aligning AI with societal needs and
ethical considerations remains a challenge. The exploration of AI’s
capabilities across various domains underscores the importance of developing
AI technologies that are inclusive, ethical, and beneficial for all, ensuring
a positive contribution to society’s global challenges. Some of the concepts
and applications that one needs to familiarize themselves with to get a
comprehensive understanding of foundational AI blocks:

  * •

Machine Learning Models: Often categorized into supervised learning, where
models learn from labeled data; unsupervised learning, identifying patterns in
unlabeled data; and reinforcement learning, where models optimize behavior
through rewards from trial and error actions. AI employs machine learning
models, including deep neural networks, for tasks like image recognition and
natural language processing (Goodfellow, Bengio, and Courville 2016). However,
while these models excel at pattern recognition and decision-making based on
data, they sometimes lack a human-centered focus, which can result in outputs
that might not always align with human values or societal nuances (Raghavan
2021).

  * •

Chatbots, Virtual Assistants, and Language Processing: AI-powered chatbots,
virtual assistants (e.g., Siri, Alexa), and language processing systems
leverage natural language understanding and generation to interact with
humans, provide information, and perform tasks (Khurana et al. 2023) (Kasneci
et al. 2023).

  * •

Autonomous Systems and Robotics: AI enables the development of autonomous
systems, including self-driving cars (Grigorescu et al. 2020), drones, and
robots, which can make decisions and perform tasks without direct human
intervention (Thrun 2000).

  * •

Generative Models and Creative Applications: AI generative models (Devlin et
al. 2018), like GANs and VAEs, create realistic content such as images, music,
and text, blurring the line between human and machine-generated creations
(Verdoliva 2020). AI is used in creative fields(Du Sautoy 2020) such as art,
music, and literature (Cao et al. 2024).

  * •

Recommendation Systems and Personalization: AI-powered recommendation systems
analyze user behavior and preferences to provide personalized recommendations
for various products, services, and content, enhancing user experiences and
engagement (Lü et al. 2012). Personalized Marketing and Targeted Advertising
AI analyzes user data and behavior to deliver personalized marketing
strategies, including targeted advertisements and recommendations tailored to
individual customers.

  * •

Computer Vision and Image Analysis: AI advancements in computer vision enable
machines to understand and interpret visual information, performing tasks such
as image recognition, object detection, facial recognition, and autonomous
surveillance Iandola et al. (2016) (Voulodimos et al. 2018).

  * •

Domain-Specific Applications: Medical Diagnosis, Healthcare, and Fraud
Detection AI systems aid in medical diagnosis, analyzing patient data, medical
images, and clinical records (Topol 2019). They also contribute to fraud
detection and cybersecurity, identifying patterns and anomalies to protect
against threats.

### The Socio-technical Layer: AI Frameworks and Principles

The frameworks of Trustworthy AI, Responsible AI, Ethical AI, AI for Good,
Human-Centered AI, and Equitable AI originated from the growing awareness of
the profound impacts AI technologies have on society, coupled with a
recognition of the potential risks and ethical dilemmas they present. As AI
began to permeate every aspect of human life, from healthcare and education to
governance and privacy, scholars, policymakers, and technologists acknowledged
the need for guiding principles to ensure the development and deployment of AI
technologies that are beneficial, fair, and aligned with human values (Floridi
et al. 2021).

  * •

Trustworthy AI: This framework focuses on creating AI systems that are safe,
transparent, and reliable, emphasizing user privacy and data security (Li et
al. 2023). It aims to build user trust through transparency, fairness, and
accountability (AI 2019).

  * •

Responsible AI: This framework stresses the ethical development and use of AI,
ensuring societal benefit and minimal harm. It advocates for ethical standards
and the consideration of AI’s broader societal impacts, promoting integrity in
AI deployment (Dignum 2019).

  * •

Ethical AI: These AI principles align AI development with ethical principles,
including human rights and fairness, to prevent bias and discrimination. It is
supported by frameworks like those from the (Jobin, Ienca, and Vayena 2019),
encouraging diverse perspectives in AI development.

  * •

AI for Good: This framework applies AI to tackle global challenges (Rakova and
Dobbe 2023) and achieve the United Nations Sustainable Development Goals
(Coeckelbergh 2021), focusing on healthcare, education, and environmental
protection (Floridi et al. 2021). It highlights AI’s potential for positive
societal impact (Cowls et al. 2019).

  * •

Human-Centered AI (HCAI): These AI principles prioritize human needs and
values in AI development, integrating psychology, sociology, and ethics to
enhance human capabilities without compromising dignity or autonomy. It
advocates for participatory design, making AI accessible and meaningful
(Shneiderman 2021).

  * •

Explainable AI: As AI becomes more complex and powerful, there is a growing
need for trust and explainability. Users and stakeholders want to understand
how AI systems reach their conclusions or recommendations (Arrieta et al.
2020). Research on explainable AI aims to develop methods that can provide
meaningful explanations for AI decisions (Arrieta et al. 2020).

  * •

Equitable AI: These AI principles aim for the fair distribution of AI benefits
and the mitigation of harms across society, addressing inequalities in AI’s
development and deployment. It focuses on diversity, inclusivity, and access,
promoting inclusive design practices (Bennett, Rosner, and Taylor 2020).

The perception of AI and these frameworks dynamically influence each
other(Shin 2021). These frameworks play a crucial role in shaping the
development of AI by providing ethical, social, and technical guidelines that
aim to maximize the benefits of AI while minimizing harm. They serve as a
compass for creators, guiding the design of AI systems that respect human
rights, promote inclusivity, and ensure accountability. They collectively
emphasize ethical standards, societal welfare, and human values, advocating
for transparency, fairness, and accountability (Floridi et al. 2021). However,
their focuses vary slightly; Trustworthy AI prioritizes user trust through
safety and reliability, Responsible and Ethical AI emphasizes adherence to
ethical standards and societal impacts, AI for Good targets the application of
AI for solving global challenges, Human-Centered AI stresses enhancing human
capabilities and well-being, and Equitable AI seeks to ensure fairness and
prevent exacerbation of inequalities.

Together, these frameworks form a comprehensive set of principles guiding the
ethical, inclusive, and beneficial development of AI technologies,
highlighting the multifaceted approach needed to address the complex
implications of AI in society. Public perception impacts the emphasis and
direction of these frameworks, as societal concerns about privacy, job
displacement, and bias in AI systems have led to a greater focus on
trustworthiness, responsibility, and ethics in AI development. Conversely, the
adoption and promotion of these frameworks can positively influence public
perception of AI, building trust and confidence in AI technologies.

## AI consequences

This section explores the second dimension of the AI identity that is external
and introduces a wide range of ethical, societal, and cultural considerations.
Ethical issues, including algorithmic bias, privacy breaches, and the
potential for surveillance, pose significant concerns. The integration of AI
into everyday life highlights the potential for synergy between humans and AI,
facilitating collaborative problem-solving while also raising questions about
anthropomorphism and its implications. The reflection of human biases in AI
systems underscores the urgency for fairness and inclusivity in model
development. As AI systems become more complex, the demand for transparency
and clear explanations of AI decisions grows, alongside the need for stringent
regulations and robust data protection measures. AI’s foray into creative
fields prompts discussions about originality and the nature of human
creativity. Moreover, AI’s impact extends to reshaping job markets and
socioeconomic structures, necessitating strategies to protect workers and
leverage AI for socioeconomic improvement. Media representations of AI
significantly influence public perception, highlighting the need for a
critical analysis of how AI is portrayed and perceived.

The concept of intersectional identity is pivotal when discussing perception,
revealing how AI’s interaction with culture, economics, and ethics intersects
with various human identities, such as race, gender, socioeconomic status, and
disability. Biased AI systems disproportionately affect marginalized
communities, amplifying inequalities. The differential impact of AI on job
markets, based on intersecting identities, calls for equitable AI deployment
and strategies tailored to diverse societal needs. Media representations must
also embrace intersectionality, offering nuanced portrayals that challenge
stereotypes and shape diverse perceptions of AI technologies. Acknowledging
these intersectional dimensions is essential for the development, regulation,
and integration of AI technologies in a manner that ensures fairness,
inclusivity, and equitable outcomes for everyone.

  * •

Ethical Considerations: The development and deployment of AI raise important
ethical considerations. Issues like bias in AI algorithms, privacy concerns,
job displacement, and the impact of AI on society and human values require
careful consideration and mitigation. Beyond its creators, AI’s identity is
shaped by a plethora of ethical dilemmas that have broader societal
implications. The dichotomy of AI applications, as seen in authoritarian
regimes using it for surveillance versus democratic societies grappling with
its ethical deployment, illustrates the complexity of its societal
implications (Jobin, Ienca, and Vayena 2019).

  * •

Personification or Anthropomorphism: Humans often tend to anthropomorphize AI
systems, attributing human-like qualities, intentions, and emotions to them
(Cole 1991). This phenomenon raises important questions about the
psychological and societal implications of interacting with AI. One of the
most vivid examples of humans attributing lifelike qualities to AI systems can
be seen in the applications of voice-activated virtual assistants like Siri,
Alexa, or Google Assistant (Blut et al. 2021). As benign as this may seem, it
brings forth significant questions. How does this humanization of machines
affect our social behaviors, especially among the younger generation? Do these
interactions blur the lines between genuine human relationships and
interactions with coded algorithms? Additionally, as AI systems get more
sophisticated in their responses, the risk of over-trusting or becoming
emotionally reliant on them grows. This phenomenon necessitates a broader
discussion on the implications of AI-human interactions, not just from a
technological standpoint, but from psychological and societal perspectives as
well.

  * •

AI and Human Collaboration: The relationship between AI and humans is
evolving. AI can augment human capabilities, automate repetitive tasks, and
provide valuable insights. Human-AI collaboration is essential for leveraging
the strengths of both to tackle complex problems (Dellermann et al. 2019)but
introducing AI may negatively impact employees if not done intentionally
(Mirbabaie et al. 2022) (Rezwana and Maher 2023).

  * •

Human-Bias Reflection: AI systems can inherit human biases present in the data
they are trained on, leading to biased outcomes and discriminatory behavior
(Schellmann 2024). Recognizing and mitigating these biases is essential to
ensure fairness and inclusivity in AI applications (Buolamwini and Gebru
2018). We have seen the need to scrutinize and rectify any biases in the data
of AI systems become critical especially in areas like criminal justice, to
ensure fairness and prevent perpetuation of historic injustices.

  * •

Regulation and Governance: The rapid advancement of AI has prompted
discussions around the need for regulations and governance frameworks (Hinton
et al. 2023) (Pause Giant 2023). Governments and organizations are exploring
ways to ensure the responsible and ethical use of AI, protect privacy, and
address potential risks associated with its deployment (Madiega 2021).

  * •

Privacy and Data Protection: AI systems rely on vast amounts of data, raising
concerns about privacy and data protection. Experts in computer science and
law collaborate to develop frameworks and regulations that safeguard
individuals’ privacy while allowing for AI innovation (Aragon et al. 2022)
(Arora et al. 2023).

  * •

AI and Creativity: AI is making its mark in creative domains such as art,
music, and literature. This blurs the line between human creativity and
machine-generated output (Karimi et al. 2020), raising questions about
authorship, originality, and the nature of art. Can an AI, devoid of lived
experiences and emotions, genuinely craft a narrative that resonates with
human emotions? And if the AI art/creation wins a competition, who would take
the accolades - the AI, its developers, or the myriad of authors whose works
trained the system? (Franceschelli and Musolesi 2022)

  * •

Cultural and Social Impacts: AI technologies are not developed in a vacuum but
are shaped by cultural and social contexts. Experts in sociology and
anthropology examine how AI systems reflect and reproduce societal norms,
values, and power dynamics (Karizat et al. 2021).

  * •

Socioeconomic Impact: The implications of AI-driven automation have the
potential to reshape job markets and socioeconomic structures through job
displacement and disrupting the human resources landscape (Schellmann 2024).
Thus, exploring strategies to ensure a just transition for workers and
leveraging AI for socioeconomic development becomes crucial across various
sectors like healthcare, education, and governance (Chui et al. 2023).
Countries that rapidly adapt to AI stand to gain significant socioeconomic
advantages, while those lagging behind face an exacerbated digital divide.
This widening gap between AI-ready nations and those without adequate access
or infrastructure could lead to significant disparities in social and economic
growth in the coming decades escpecially for marginalised groups (Chetty et
al. 2014).

  * •

Media Representations: Media plays a crucial role in shaping public
perceptions and understandings of AI. Experts critically analyze how AI is
portrayed in popular culture, news media, and entertainment, especially the
genres of science fiction and speculative fiction, which have a long history
of shaping the public’s perception of AI. These diverse media representations
significantly influence public sentiment and understanding of AI, thus shaping
the discourse surrounding its development and deployment in real-world
contexts.

  * •

AI Dilemmas: Various combinations of Consequences and considerations in AI
raise numerous dilemmas, such as determining responsibility and accountability
for AI actions, ensuring fairness and transparency in decision-making, and
considering the ethical impact of AI on social dynamics, safety, and privacy
(Caldwell et al. 2022). Highlighting that we must ensure that while AI is a
valuable resource and tool, the final decision must rests with the human
expert, capitalizing on the strengths of both entities to address intricate
challenges while promoting the principles of HCAI.

When AI is seen as being developed and deployed in a manner that is aligned
with ethical principles and societal well-being, it garners broader acceptance
and support (Lucivero et al. 2020). This interplay highlights the importance
of transparent communication and engagement with the public in the ongoing
development and refinement of AI technologies and their guiding frameworks.

Figure 4: The AI Identity Framework

##  AI Identity Framework

The perception of AI is not monolithic, It is influenced by numerous factors,
including societal structures, personal experiences, media representations,
etc. The lens of identity in the field adds a layer of complexity, which leads
to discussions about whether AI application design and creation processes are
informed by a myriad of backgrounds, experiences, and worldviews. Thus by
understanding the context of who creates AI technologies, as well as examining
the frameworks and societal considerations that guide AI’s development and the
way they impact society, we can advocate for diversity and inclusion as
essential to an AI Identity that serves all people fairly. The research and
conceptual frameworks of AI identity ecosystem capture this complexity by
examining the relationship between individual identity factors—such as race,
ethnicity, gender, class, sexual orientation, religion, and disability—and
their impact in technological contexts, specifically through the lens of
creators and consumers roles in the development, access to, data management,
perception, and implementation of AI in applications/solutions(Kivunja 2018).
In the technology development landscape, the creator’s work is influenced by
their experiences, perceptions and identity, which manifests in the data they
select and collect, ultimately shaping the technology they create. Thus
representation and inclusion in the creation process has far-reaching
consequences and considerations, which the framework suggests must be
critically assessed through the lens of identity. Key sociological constructs
such as diversity, fairness, inclusion, and bias are interwoven with a
fundamental sense of belonging, and accountability underscoring the importance
of these concepts in evaluating diversity and inclusion work in the field of
AI. The upward arrow alongside ’Impact of Identity’ suggests that the presence
or lack of diversity and inclusion across the mentioned sociological
constructs get amplified as we move up through the layers. for example, the
impact of bias in the creator’s layers (Mehrabi et al. 2021) snowballs
exponentially into the consequences and considerations layer much akin to the
bio accumulation/magnification process in nature (Bommasani et al. 2022).
Greater emphasis on these aspects can potentially elevate the role and
positive influence of identity in the technological sphere. This sociological
framework serves as a guide for a comprehensive analysis of how identity
shapes technology and, conversely, how technology can reflect and affect
societal values and individual sense of self.

In essence, this position paper underscores the importance of an
interdisciplinary, identity-centered approach when educating future designers
and developers of AI systems. It reinforces the belief that for AI to be truly
beneficial for all of society, the designers of tomorrow must be equipped not
just with technical expertise, but with a profound understanding of their own
identity and understand the ethical, social, and personal ramifications of
AI’s role in our world. Importantly, this approach combined with Human-
Centered principles can also catalyze broadening participation. When AI
technologies are built to prioritize inclusivity and fairness, they can
naturally spark interest and engagement from a broader cross-section of
society. A commitment to center AI creation and development in identity and
the human experience can lead to more people, including those from
underrepresented backgrounds, feeling motivated to participate in the field.
In return, their participation ensures a broader scope of insights, improving
AI’s responsiveness to societal needs.

## Conclusion

In conclusion, the world of AI is ever-changing, with new creators, creations,
and ideas constantly emerging as technology advances. Highlighting the
interplay between the technology itself and its broader interaction with
society, We define AI Identity in two dimensions to form a comprehensive view
of AI identity. AI Identity that includes the collective characteristics,
values, and ethical considerations embodied in the creation of AI technologies
internally, and AI identity that is shaped by individual perception, societal
impact, and cultural norms externally.The discussions within this paper shed
light on the significant impact of diversity and inclusion in shaping public
perceptions and understanding of AI, demonstrating how these narratives
influence the discourse surrounding AI technologies in various societal
contexts. Moreover, by proposing the AI identity framework, which captures the
impact of various social constructs such as diversity, fairness, inclusion,
bias, sense of belonging, and accountability across the creators, creations,
and consequences of AI, we advocate for a more inclusive and responsible AI
ecosystem. This AI identity ecosystem lens highlights the need for the
development of AI technologies that are equitable, accessible, and beneficial
for all segments of society. This paper serves as a call to action, urging the
AI community to ground the development of AI in the human experience. An
approach that creates technology to addresses the needs of diverse
populations, which, in turn, fosters greater inclusivity and engagement in AI
development.

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