Dr. Kriti Dhingra is an Associate Professor at Vivekananda Institute of Professional studies -Technical Campus, GGSIPU where she contributes to the academic mission of nurturing technology-driven business education. She holds a Doctorate in Information Technology, a postgraduate degree in Master of Computer Applications from Guru Gobind Singh Indraprastha University (GGSIPU), and a Bachelor of Science (Honours) in Computer Science from the University of Delhi — reflecting a rigorous and comprehensive foundation in computing and information sciences. Dr. Dhingra is UGC NET qualified in Computer Science and Applications, a distinction that underscores her academic excellence and subject matter expertise. Her primary research interests span Knowledge Management, Learning Analytics, and Machine Learning — areas that collectively position her at the intersection of human cognition, data-driven decision-making, and intelligent systems. She has made significant contributions to academic scholarship through numerous publications in peer-reviewed national and international journals of repute. As a published author, she has also authored books in the domains of Business Analytics, Design Thinking and Innovation, E-Commerce and Information System Management, serving as valuable academic resources for students and practitioners alike. She may be contacted at kriti.dhingra@vips.edu

Diversity, Equity, and Inclusiveness in the Artificial Intelligence Era: Challenges, Frameworks, and Pathways Forward The proliferation of artificial intelligence (AI) across social, institutional, and economic domains presents both unprecedented opportunities and profound ethical challenges with respect to diversity, equity, and inclusiveness (DEI). Rooted in decades of scholarship on systemic inequality, algorithmic governance, and the sociology of technology, this session critically examines the ways in which AI systems both reflect and reproduce structural disparities — while simultaneously interrogating the conditions under which they may serve as instruments of equity. Drawing on interdisciplinary literature spanning computer science, critical race theory, feminist technology studies, and science and technology studies (STS), this session advances the argument that AI is not a neutral technological artifact but a sociotechnical construct embedded within — and constitutive of — existing power relations. Empirical evidence from domains including hiring and recruitment, criminal justice, healthcare, and financial services demonstrates that algorithmic systems, when developed without deliberate attention to representational diversity and distributional fairness, systematically disadvantage historically marginalized populations. This session further examines structural barriers to DEI within AI development ecosystems, including the persistent underrepresentation of women, racially minoritized groups, and Global South perspectives in AI research, engineering, and governance. It interrogates how homogeneity in development teams and training datasets contributes to epistemic narrowness, resulting in AI systems that inadequately account for the complexity of human diversity. Building on established frameworks — including fairness-aware machine learning, participatory design, and algorithmic impact assessment — the session proposes a multi-level approach to embedding DEI across the full AI lifecycle: from problem conceptualization and data curation to model evaluation, deployment, and regulatory oversight. Particular attention is given to the role of institutional accountability mechanisms, inclusive policy design, and community-centered AI governance in advancing equitable outcomes.