Equitable and Inclusive Digital Systems in Society and Institutions Track

As artificial intelligence systems increasingly mediate access to employment, credit, healthcare, education, and information, concerns about algorithmic bias and digital discrimination have moved from theoretical debate to urgent societal challenge. This minitrack focuses on research involving understanding, identifying, and mitigating discrimination that arises in the design, development, deployment, and governance of systems that incorporate artificial intelligence.

A technology is biased if it unfairly or systematically discriminates against certain individuals by denying them an opportunity or assigning them a different and undesirable outcome. As we delegate more and more decision-making tasks to computer autonomous systems and algorithms, such as using artificial intelligence for employee hiring and loan approval, digital discrimination is becoming a serious problem. In her New York Times best-seller book “Weapons of math destruction: How big data increases inequality and threatens democracy,” Cathy O’Neil refers to those math-powered applications as “Weapons of Math Destruction” and provides examples to show how such mathematical models encoded human prejudice, misunderstanding, and bias into the software systems that increasingly managed and harmed our lives.

Artificial Intelligence (AI) decision making can cause discriminatory harm to many vulnerable groups. In a decision-making context, digital discrimination can emerge from inherited prejudices of prior decision makers, designers, engineers or reflect widespread societal biases. One approach to addressing digital discrimination is to increase transparency of AI systems. However, we need to be mindful of the user populations that transparency is being implemented for. In this regard, research has called for collaborations with disadvantaged groups whose viewpoints may lead to new insights into fairness and discrimination.

Addressing the problem of digital discrimination in AI requires a cross-disciplinary effort. For example, researchers have outlined social, organizational, legal, and ethical perspectives of digital discrimination in AI . In particular, prior research has called for our attention to research the three key aspects: how discrimination arises in AI systems; how design in AI systems can mitigate such discrimination; and whether our existing laws are adequate to address discrimination in AI.

This minitrack welcomes papers in all formats, including empirical studies, design research, theoretical framework, case studies, etc. from scholars across disciplines, such as information systems, computer science, library science, sociology, law, etc. Potential topics include, but are not limited to:

• Agentic AI, Decision Making, and Governance
• AI-based Assistants: Opportunities and Threats
• AI Auditing and Algorithmic Accountability
• AI Explainability and Digital Discrimination
• AI Governance and Regulatory Compliance
• AI Literacy of Users
• AI Systems Design and Digital Discrimination
• AI Use Experience of Disadvantaged / Marginalized Groups
• Biases in AI Development and Use
• Digital Discrimination in Online Marketplaces
• Digital Discrimination and the Sharing Economy
• Digital Discrimination with Various AI Systems (LLM based AI, AI assistants, etc.)
• Effects of Digital Discrimination in AI Contexts
• Ethical Use/ Challenges/ Considerations and Applications of AI systems
• Erosion of Human Agency and Generative AI Dependency
• Generative AI (e.g., ChatGPT) Use and Ethical Implications
• Organizational Perspective of Digital Discrimination
• Power Dynamics in Human-AI Collaboration
• Responsible AI Practices to Minimize Digital Discrimination
• Responsible AI Use Guideline and Policy
• Societal Values and Needs in AI Development and Use
• Sensitive Data and AI Algorithms
• Social Perspective of Digital Discrimination
• Trusted AI Applications and Digital Discrimination
• User Experience and Digital Discrimination

Minitrack Co-Chairs:

Sara Moussawi (Primary Contact)
Carnegie Mellon University
smoussaw@andrew.cmu.edu

Jason Kuruzovich
Rensselaer Polytechnic Institute
kuruzj@rpi.edu

Minoo Modaresnezhad
Minoo Modaresnezhad University of Maryland
mmodares@umd.edu

Artificial intelligence (AI) is increasingly embedded in the social, organizational, and informational infrastructures through which individuals plan, decide, learn, and sustain participation across the life course. Population aging has rendered later life a consequential yet insufficiently theorized stage of digital transformation. This period is characterized by a series of structurally and psychologically significant transitions, including retirement, role reconfiguration, changing learning trajectories, and evolving sources of expertise. These transitions raise fundamental questions regarding how digital technologies shape 2 autonomy, continuity, and meaningful engagement in later life, positioning graceful aging as a critical concern for Information Systems (IS) research.

IS scholarship has long examined how digital technologies support decision-making, knowledge creation, and participation. Much of this literature, however, has been grounded in implicit assumptions of relatively stable user roles, cognitive capacities, and learning orientations. Recent advances in AI—particularly generative, adaptive, and data-driven systems—challenge these assumptions by actively mediating how individuals access information, develop competencies, and make sense of complex environments. In later life, such AI-enabled mediation intersects with age-related changes in experience, cognition, motivation, and identity, thereby reshaping the conditions under which individuals adapt, contribute, and exercise agency over time.

Research on aging and digital technologies has predominantly emphasized technology acceptance, health-oriented interventions, or aggregated well-being outcomes. While these streams have generated important insights, they often conceptualize aging as a static contextual attribute rather than as an unfolding process of transition and adaptation. A graceful aging perspective foregrounds aging as a dynamic and heterogeneous life-course process, emphasizing continuity, psychological resilience, social connectedness, dignity, adaptive growth, and a sense of purpose and autonomy. Within this perspective, AI functions not merely as an assistive artifact, but as a socio-technical arrangement that reconfigures learning processes, forms of contribution, and the distribution of expertise and responsibility across laterlife transitions.

This minitrack advances IS research by positioning AI-enabled graceful aging as a central analytical lens. It invites studies that examine how AI shapes later-life transitions, how learning and unlearning processes unfold in AI-mediated environments, and how digital systems can be designed and governed to support autonomy, role continuity, and sustained participation in later life. By treating aging and later life as core theoretical concerns rather than peripheral user characteristics, this minitrack seeks to extend IS theories of digital transformation, learning, and agency into an increasingly salient stage of the human life course. The minitrack welcomes theoretical, empirical, experimental, and design-oriented studies employing qualitative, quantitative, or mixed methods. Examples of possible topics of interest include, but are not limited to:

• AI-Enabled Graceful Aging as a Life-Course Process
  1. Examining how AI supports autonomy, resilience, dignity, companionship, subjective well-being, and selfagency (e.g. perceived control) as core dimensions of graceful aging
  2. Investigating how AI contributes to continuity of self, identity coherence, and sustained purpose across later life
• Later-Life Transitions and AI-Enabled Adaptation
  1. Exploring how AI supports adaptation during major later-life transitions, such as retirement, role exit, and social reconfiguration
  2. Examining how AI assists older adults in navigating uncertainty, reorientation, and sensemaking across life stages
• AI-Supported Lifelong Learning and Cognitive Engagement
  1. Investigating how AI enables learning, reskilling, and intellectual engagement in later adulthood
  2. Examining the role of AI in sustaining curiosity, cognitive vitality, and adaptive learning across aging transitions
• Meaning-Making and Psychological Growth in Later Life
  1. Exploring AI-enabled reflection, life review, and narrative reconstruction in later life
  2. Examining how AI supports meaning-making, wisdom development, and emotional maturation across the aging process
• Social Participation and Relational Dimensions of Aging
  1. Investigating how AI enables social participation, belonging, and community engagement in later life
  2. Examining AI-supported pathways to maintaining or reconfiguring social roles and intergenerational connections
• Socio-Technical Arrangements Supporting Older Adults’ Collaboration with AI
  1. Investigating organizational, community, or platform-level arrangements that enable older adults to collaborate effectively with AI systems
  2. Examining coordination among older adults, AI systems, families, organizations, and institutions across later-life transitions
• Resistance, Boundary Work, and Reinterpretation of AI in Later Life
  1. Examining how older adults resist, reinterpret, or strategically appropriate AI systems to maintain control and autonomy
  2. Investigating boundary work between human agency and machine authority in later-life human – AI collaboration

High-quality and relevant papers from this mini-track will be selected for fast-tracked development towards Internet Research, an international and refereed journal that is indexed and abstracted in major databases (e.g., SSCI, SCI, ABI/INFORM Global), with an impact factor 6.8 in 2024. Selected papers must expand in content and length in line with the requirements for standard research articles published in the journal. Although the mini-track co-chairs are committed to guiding the selected papers towards final publication, further reviews may be needed before a final publication decision can be made.

Minitrack Co-Chairs:

Christy M.K. Cheung (Primary Contact)
Hong Kong Baptist University
ccheung@hkbu.edu.hk

Matthew K.O. Lee
Hong Kong Metropolitan University
mkolee@hkmu.edu.hk

Lawrence X. Liu
Hong Kong Baptist University
lawrence_xl@hkbu.edu.cn

AI Psychometrics is an emerging field at the intersection of psychology, cognitive science, neuroscience, and artificial intelligence, dedicated to evaluating and interpreting the psychological traits and internal processes of artificial intelligence. While traditional psychometrics and cognitive science have accumulated extensive methodologies to rigorously evaluate human intelligence, personality, and cognitive processes, AI Psychometrics adapts these frameworks to assess large language models and autonomous agents. Researchers in this field not only apply human-behavioral methodologies—such as questionnaires, standardized tests, experiments, and interviews—but also increasingly incorporate neural network-level analyses, including Neuron Activation Pathway Analysis, Probing, and Steering Vectors. By examining the activations and weights within a model’s neural architecture, these methodologies seek to map specific cognitive properties directly to their underlying computational structures. This multi-layered approach provides a holistic understanding of AI behavior, offering the insights necessary to enhance the development, deployment, and ethical management of complex systems by grounding “psychological” findings in “neural” reality.

As AI systems become increasingly integrated into everyday life—from healthcare and education to hiring and criminal justice—the need to ensure these systems are equitable, inclusive, and trustworthy has never been more pressing. Without rigorous psychometric evaluation, biases embedded in AI systems may go undetected, fairness claims may remain unverified, and vulnerable populations may bear disproportionate risks from poorly understood AI behaviors. AI Psychometrics offers a pathway for addressing these concerns by enabling the systematic assessment of AI systems’ cognitive processes, biases, and reasoning patterns through validated psychometric instruments. Furthermore, platforms such as the PsyCogMetrics™ AI Lab aim to democratize access to rigorous AI evaluation, providing these assessment tools to researchers, policymakers, and stakeholders.

This minitrack welcomes researchers from all disciplines, including psychology, cognitive science, computer science, information systems, education, social sciences, and beyond. We invite submissions that employ a variety of methodologies, including but not limited to psychometric testing, cognitive science experiments, design science research, computational modeling, empirical studies, surveys, theoretical analyses, and case studies. Our goal is to cultivate a vibrant dialogue across a wide range of disciplinary and methodological perspectives, thereby advancing understanding and fostering innovation in the evaluation and governance of AI systems. Our scope of interest spans a wide range of topics, which will be detailed in the following sections.

• Personality, Emotional Intelligence, and Theory of Mind Assessment of Large Language Models
• Psychometric Validity, Reliability, and Measurement Quality of AI Evaluations
• Adaptive Testing, Item Response Theory, and Novel Benchmarking Approaches for AI
• Bias Detection, Fairness, and Equity Evaluation Using Psychometric Methods
• Psychometric Framework Development, Evaluation Platforms, and Design Science for AI Assessment
• Neuron Activation Pathway Analysis and Mechanistic Interpretability of AI Systems
• Probing and Representation Analysis of Emotions, Personality, and Cognitive Traits in Neural Networks
• Activation Engineering and Steering Vectors for AI Personality and Behavioral Control
• Sparse Autoencoders and Feature Disentanglement for Psychological Trait Extraction in LLMs
• Circuit-Level Analysis of Reasoning, Decision-making, and Cognition in Large Language Models

Minitrack Co-Chairs:

Yibai Li (Primary Contact)
University of Scranton
yibai.li@scranton.edu

Zhiye (Norman) Jin
PsyCogMetrics™ AI Lab
zjin@m.marywood.edu

While large organizations are rapidly advancing toward AI‑enabled operations and practices, small and medium‑sized enterprises (SMEs) often lag significantly behind. More specifically, SMEs face structural, processual and relational constraints that limit their AI adoption, including limited financial capacity, access to advanced technologies, insufficient data infrastructures, and a shortage of digital and analytical skills within their workforce. This disparity risks creating a widening digital divide between large firms and SMEs, which may undermine economic dynamism, digital equity, and long‑term sustainability across sectors, countries and regions.

Given that SMEs form the backbone of most global economies, their difficulty in participating meaningfully in today’s AI-enabled business and technological economy through effective organizational transformation poses challenges not only for firm‑level competitiveness but also for inclusive and equitable digital transformation at the ecosystemic, societal, and institutional levels. Ensuring that SMEs can access, adopt, and benefit from AI-enabled technologies and practices is therefore critical for fostering resilience, innovative, and fair economic systems.

This minitrack provides a forum for discussing and exchanging research ideas, lessons from case studies and best practices in an emergent and disruptive context. We welcome all types of contributions to theoretical, empirical, design‑oriented, technical, and interdisciplinary research addressing the opportunities, challenges, strategies, and outcomes associated with AI-enabled transformation and use in the SME context. The topics include, but are not limited to:

• AI adoption drivers, strategies, and readiness models for SMEs’ digital transformation
• AI barriers, challenges, and failures for SMEs’ digital transformation
• Skill gaps, workforce development, organizational learning and managerial capabilities for AI‑enabled practices in SMEs
• Ethical and societal issues surrounding AI in SMEs and their environments
• Case studies of AI adoption and use in SMEs and their business ecosystems
• AI innovation ecosystems inclusive of SMEs
• Design of SME-suitable AI artefacts
• SME practitioner perspectives on AI
• Implementing human-centered AI for SMEs
• AI-enabled business model innovation pathways in SMEs
• AI governance frameworks for SMEs
• AI-driven shifts in SME strategies for competition, co-opetition, co-creation, and open innovation
• Development, configuration, and management of AI ecosystems for SMEs

This minitrack partners with Electronic Markets, a leading information systems journal ranked in the top quartile (Q1) of Scimago journals and classified as ‘A’ journal in many rankings. Authors of papers that are accepted in this minitrack and align with the journal scope may be invited to submit a substantially extended version of their work to the journal for a fast‑tracked review process.

Minitrack Co-Chairs:

Yao Shi (Primary Contact)
University of North Carolina Wilmington
shiy@uncw.edu

Lukas Fitz
Brandenburg University of Technology Cottbus – Senftenberg
fitz@b-tu.de

Claudia Pelletier
Université du Québec à Trois-Rivières
claudia.pelletier@uqtr.ca

Fanny-Eve Bordeleau
Dalhousie University
fe.bordeleau@dal.ca

This minitrack provides an open and inclusive outlet for high-quality research aligned with the theme of the Equitable and Inclusive Digital Systems in Society and Institutions track that does not fit neatly within other minitracks. We particularly welcome emerging, interdisciplinary, and agenda-setting work that advances understanding of equity, inclusion, justice, and harm in digital systems.

Minitrack Co-Chairs:

K.D. Joshi (Primary Contact)
University of North Carolina Wilmington
joshik@uncw.edu

Nancy Deng
California State University, Dominguez Hills
ndeng@csudh.edu

Digital ecosystems, including data infrastructures, algorithmic decision‑making tools, artificial intelligence (AI), and platform technologies, are increasingly embedded within justice institutions and shaping how rights, risks, and resources are allocated. Innovations in Information Communication Technologies (ICT) have transformed these ecosystems in ways that can amplify harm, exclusion, and victimization, often at scale. At the same time, ICTs provide new avenues for illicit actors to reach, manipulate, and exploit marginalized groups who are already vulnerable to diverse forms of exploitation.

Law enforcement agencies and governments have begun updating outdated laws and policies in response to these developments, yet regulatory systems often struggle to keep pace with rapid technological change and evolving criminal practices. Justice institutions now depend heavily on digital ecosystems for investigation, adjudication, and governance, raising important questions about transparency, accountability, and equity. In parallel, legitimate organizations increasingly seek ways to deploy ICT to detect and mitigate criminal misuse of their products and services, protect stakeholders, and reduce societal harm.

Criminal groups often act as unexpected drivers of innovation by developing, adapting, or repurposing technologies and organizational practices. These dynamics require governments and law enforcement agencies not only to continually adapt but also to strengthen their capacity for strategic anticipation. In an era of heightened global conflict and geopolitical rivalry, criminal disruptions may be instrumentalized, directly or indirectly, to destabilize political or economic systems. Such disruptions can also reinforce dominant positions within digital ecosystems, blurring the lines between organized crime, influence operations, and power politics, with disproportionate consequences for vulnerable populations.

Digital ecosystems now play a central role in the administration of justice. Emerging ICT tools, particularly AI, are reshaping legal systems with profound implications. On one hand, AI‑driven applications offer promise for improving judicial decision‑making, streamlining legal procedures, and expanding access to justice, especially for underserved and marginalized communities. On the other hand, these technologies introduce risks related to bias, discrimination, surveillance, and privacy violations, which can disproportionately affect vulnerable groups. As AI tools become more deeply embedded in judicial and administrative functions, the need for critical examination of their societal and institutional impacts becomes increasingly urgent.

This minitrack explores criminal justice and the administration of justice as a high‑stakes domain for examining equitable and inclusive digital ecosystems. We especially welcome research addressing:

• The Dual‑Edged Impact of ICT and AI Technologies in Justice: Studies examining how ICT and AI can enhance efficiency, accessibility, and fairness in legal processes while also introducing new risks, including bias, discrimination, and privacy violations.
• Evolving Criminal Behavior: Research on how digital platforms, datafication, and AI systems enable illicit behaviors; how cybercriminals initiate and develop cybercrime “careers”; and how technological platforms are misused to target vulnerable populations.
• Law Enforcement and Policy Responses: Analyses of how justice institutions, law enforcement agencies, NGOs, and businesses use ICT and AI to detect and disrupt illicit networks, and how these systems influence equity, accountability, and rights protections.
• Legal and Institutional Reforms: Work that evaluates the adequacy of current laws and policies amid technological change, focusing on how ICT and AI can safeguard rights and ensure access to justice for vulnerable groups.
• Victim Support and Access to Justice: Investigations into how ICT and AI can support victims of crime and exploitation, particularly marginalized groups requiring legal assistance and protection.
• Criminal Innovation and Crisis‑Induced Destabilization: Research on how criminal groups drive technological and organizational innovations that generate systemic disruptions, expose institutional vulnerabilities, and challenge the resilience of digital justice infrastructures.
• Digital Crime and Strategic Destabilization: Studies on how ICT‑enabled crime contributes to institutional instability, crisis dynamics, and geopolitical or geoeconomic manipulation, blurring boundaries between organized crime, influence operations, and digital governance.

Criminal justice encompasses the laws, procedures, institutions, and policies operating before, during, and after a crime. Central to this system is the protection of rights for suspects, convicted individuals, and victims. This minitrack highlights how digital ecosystems shape these protections, often unevenly. We invite conceptual, theoretical, empirical, and methodological contributions that deepen understanding of how ICT and AI are transforming justice institutions and how their design, governance, and accountability can promote a more equitable system.

Minitrack Co-Chairs:

Carlos Torres (Primary Contact)
Baylor University
carlos_torres@baylor.edu

Daniel Pienta
University of Tennessee, Knoxville
dpienta@utk.edu

Michael Dinger
Baylor University
Michael_dinger@baylor.edu

Christine Dugoin-Clement
Sorbonne Business School
christine.dugoin-clement@iae.pantheonsorbonne.fr

Digital divide refers to the gap between those who have access to and can effectively use digital technologies and those who cannot. While significant progress has been made globally in the diffusion of information and communication technologies (ICTs), digital inequity remains persistent and multifaceted. Beyond technological infrastructure and connectivity, digital exclusion may also be associated with and manifested in social, economics, educational, and corporeal basis to shape who benefits from digital services and who is excluded or disadvantaged. These disparities disproportionately affect vulnerable populations, including but not limited to youth, older adults, persons with disabilities, low-income communities, rural and indigenous communities, sexual minorities, marginalized castes, migrants and refugees, stateless individuals, and underserved regions in both developing and developed contexts.

Emerging Artificial Intelligent (AI) technologies further highlight the double-edged nature of digitalization in shaping digital divides and digital equity. While AI offers opportunities to enhance inclusion through improved access to education and digital technologies, it may also deepen inequalities due to uneven access to data, infrastructure, and skills, as well as algorithmic bias and the concentration of AI capabilities. Examining this dual role of AI is essential for advancing inclusive and equitable digital futures.

Moreover, the use of AI in the workplace is also creating a new future of work. There is a heightened need to ensure this new digitally-enabled future of work unfolds in a manner that promote digital equity and inclusion, especially for those who are at risk of being digitally disenfranchised. This entails not only the changing demands of digital literacy and skills, but also the transformation of individual identities and social relations in the AI-driven digital society. Given that inequitable distribution of AI impact on different countries and population groups, intersectional or feminist approach to digital equity and inclusion would be welcome. Furthermore, alternative and human-centric imaginaries for the digital future, as well as embodied and affective approaches to digital inclusion are also worth exploring.

As societies and institutions become increasingly digitalized and cities transform into smart cities, the urgency of moving from diagnosing digital divides to enabling digital equity and inclusion has never been greater. This minitrack invites research papers and review articles that elucidates the theory and practice of digital divides and advances digital equity and inclusion. Design science studies on digital social innovation, especially participatory design and community-based approaches that promotes digital equity and inclusion are much welcomed. Submissions may address, but not limited to, the following topics:

• Beyond access: social, economic, educational, and corporeal dimensions of digital exclusion
• Digital equity and inclusion practices, policies and strategies
• Role of AI in digital divide or digital equity and inclusion
• Digital skills, reskilling, and unequal transitions into AI-mediated work
• Datafication of cities and uneven civic participation
• Digital citizenship, surveillance, and differential inclusion/exclusion
• Inclusive digital platforms for health, education, and social care
• Identity, dignity, and subjectivity in AI-enabled workplaces
• Intersectional, feminist, or decolonial perspectives on digital inequity
• Digital inequality as a dynamic, relational, and political process
• From “digital divide” to “digital justice” and “digital rights”
• Technology use, well-being, and psychosocial exclusion
• Lived experiences of “being left behind” in digital societies
• Digital inclusion/exclusion in crisis, conflict, or humanitarian contexts
• Participatory, co-design, and community-based digital innovation
• Ethical, political, and philosophical foundations of inclusive digital societies

Minitrack Co-Chairs:

Calvin M.L. Chan (Primary Contact)
Singapore University of Social Sciences
calvinchanml@suss.edu.sg

Tianjian (TJ) Zhang
California State University Dominguez Hills
tzhang@csudh.edu

Yingqin Zheng
University of Essex
y.zheng@essex.ac.uk

This minitrack provides a forum for open and vibrant discussion for research related to the use of Information and Communication Technologies (ICT) in understanding and promoting social justice. Social justice has become particularly relevant to information systems (IS) researchers in light of the emergence of increasingly autonomous and sophisticated ICT, including biometrics and deep learning-based AI systems.

Social justice is the belief that everyone deserves fair and equal treatment and serves as a theoretical grounding for burgeoning research related to the oppressive and dehumanizing nature of modern ICT. Such technologies are developed and deployed based on the mass acquisition and curation of humancentric data, in some cases without consent from individuals, which serve as an affront to human dignity and very essence of being humans in a just society. Thus, ICT and Social justice research refer to studies about actions that promote equal rights, equal opportunities, and equal treatment between individuals, organizations, and the technologies themselves, as well as studies that highlight the use of ICT to uncover social injustice.

The guiding principles of social justice are human rights, access to basic elements such as food, water, shelter, safety, education, and opportunities, equal participation in decision-making, and equity to reduce systemic barriers to ensure every individual is treated fairly and equitably.

So why is social justice part of our remit as IS researchers? Walsham (2005) says that ICTs are involved in the way that we as individuals carry out our work and leisure activities, in the way that we organize ourselves in groups, in the forms that our organizations take, in the types of societies we create, and thus in the future of the world. ICTs are therefore deeply implicated in social justice, as IS inscribe our understanding of the world, and our attendant prejudices. Emergent ICT such as biometrics and modern AI systems are often, by design, developed through the collection and extraction of increasing amounts of human data, and in turn can unilaterally shape our perceptions of the world, and thus pose imminent and existential threats to social justice and humanity.

This minitrack invites submissions of original work concerning the intersection of IS research with social justice. Studies about the uses of ICT to uncover inequalities and injustice, and to promote justice at all levels (i.e. racial, climate, age, etc.) and equality and equity for those with fewer privileges such as people of color (POC), refugees and asylum seekers, unhoused, and people with disabilities. We also welcome critical approaches to these topics. Our goal is to spur discussion through research explorations that can enhance understanding and enliven new opportunities to derive novel ways of preserving and improving individual and societal well-being. The relevant topics for the minitrack include, but are not limited to, the following areas:

• ICT and social inclusion
• ICT and racial injustice
• ICT and equality and equity
• ICT and climate justice
• ICT and voting rights.
• ICT and income gap
• ICT and ageism
• ICT and individuality, humanness, and human dignity
• Feminist perspectives in data justice

Minitrack Co-Chairs:

Jay Killoran (Primary Contact)
University of Victoria
jkilloran@uvic.ca

Andrew Park
University of Victoria
apark1@uvic.ca

Jan Kietzmann
University of Victoria
jkietzma@uvic.ca

This minitrack highlights experiential pluralism, the recognition that human engagement with the world— through cognition, affect, sensory processing, and social interaction—is inherently diverse. Neurodiversity encompasses natural variations in neurological functioning that influence perception, communication, and behavior. This includes, but is not limited to, autism, ADHD, dyslexia, dyspraxia, and other neurodevelopmental variations, as well as cognitive, affective, sensory, and social processing styles that do not necessarily align with specific diagnoses.

Digitalized workplaces, technology-mediated services, and AI-driven decision-making processes are often built on unspoken assumptions about how individuals think, communicate, and process information. Yet, human experiences are fundamentally diverse—no two individuals perceive, process, or interact with the world in the same way, and digitalized environments should reflect this diversity.

This minitrack invites research that places neurodiversity at the core of discussions on the evolution of digital services, AI, digitalized workplaces, and online platforms across various domains. We encourage contributions that investigate:

• • Neurodiversity in digitalized environments: How do digital workspaces, health services, educational platforms, and online communities include or exclude diverse cognitive, affective, sensory, and social styles? What roles do organizational structures and digital tools play in shaping neuro-inclusive experiences?
• • AI, automation, and neurodiversity: How do AI-driven systems (e.g., hiring algorithms, content recommendations, automation tools) reflect or fail to reflect experiential pluralism, and how can they be adapted?
• • Time, attention, and cognitive rhythms in digital interactions: How do different cognitive, affective, sensory, and social styles interact with expectations around synchronicity, responsiveness, and multitasking in digital environments?
• • Beyond accessibility: rethinking digital system design through experiential pluralism: How can digital platforms, from dating apps to healthcare portals, be designed from inception with neurodiverse participation rather than merely adapted for inclusivity?

Minitrack Co-Chairs:

Maylis Saigot (Primary Contact)
University of Queensland
m.saigot@uq.edu.au

Rob Gleasure
Copenhagen Business School
rg.digi@cbs.dk

Claudia Lemke
Berlin School of Economics and Law (HWR Berlin)
claudia.lemke@hwr-berlin.de