Responsible AI and Data Science for Social Good

Kaushik Dutta (), Ajay Kumar (), Zhiling Guo (), Martin Bichler (), Dursun Delen, Ram Ramesh () and J. Paul Brooks ()
Additional contact information
Kaushik Dutta: USF - University of South Florida [Tampa]
Ajay Kumar: EM - EMLyon Business School
Zhiling Guo: UNT - University of North Texas
Martin Bichler: TUM - Technische Universität Munchen = Technical University Munich = Université Technique de Munich
Dursun Delen: OSU - Oklahoma State University [Stillwater]
Ram Ramesh: University at Buffalo, State University of New York (United States, Buffalo) - UB
J. Paul Brooks: VCU - Virginia Commonwealth University

Post-Print from HAL

Abstract: With the rapid rise of generative artificial intelligence (AI), the responsible development and governance of AI and data science have become central concerns in both academic research and practice. As generative AI systems become increasingly embedded in daily life and play a growing role in decision making, it is essential that they operate in ethical, transparent, accountable, and socially responsible ways. In this editorial, we examine how responsible AI and data science can create meaningful societal impact across six substantive areas: judicial systems, education, communication, healthcare, bias and fairness, and interpretability. Rather than treating principles such as fairness, bias mitigation, transparency, accountability, privacy protection, robustness, interpretability, and social impact as separate organizational pillars, we view them as cross-cutting design principles that arise across these domains and methodological areas. We discuss how these principles can inform the design, deployment, and governance of AI systems that address complex societal challenges, safeguarding human values and ethical standards. Finally, we outline future research directions by contrasting pregenerative AI priorities with the emerging challenges of the postgenerative AI era. In doing so, we identify computational, methodological, and optimization frameworks that can support the responsible development and deployment of generative AI systems for meaningful societal benefit.

Keywords: Responsible AI; Generative AI; Algorithmic fairness; Explainability; Computational and optimization models (search for similar items in EconPapers)
Date: 2026-07-01
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Published in INFORMS Journal on Computing, 2026, 38 (4), pp.1033 - 1044. ⟨10.1287/ijoc.2026.ed.v38.n4⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05793237

DOI: 10.1287/ijoc.2026.ed.v38.n4

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