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Responsible AI: The Good, The Bad, The AI

arXiv 2601.21095 ↗First posted 28 Jan 2026cs.AIcited by 0

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Abstract (arXiv)

The rapid proliferation of artificial intelligence across organizational contexts has generated profound strategic opportunities while introducing significant ethical and operational risks. Despite growing scholarly attention to responsible AI, extant literature remains fragmented and is often adopting either an optimistic stance emphasizing value creation or an excessively cautious perspective fixated on potential harms. This paper addresses this gap by presenting a comprehensive examination of AI's dual nature through the lens of strategic information systems. Drawing upon a systematic synthesis of the responsible AI literature and grounded in paradox theory, we develop the Paradox-based Responsible AI Governance (PRAIG) framework that articulates: (1) the strategic benefits of AI adoption, (2) the inherent risks and unintended consequences, and (3) governance mechanisms that enable organizations to navigate these tensions. Our framework advances theoretical understanding by conceptualizing responsible AI governance as the dynamic management of paradoxical tensions between value creation and risk mitigation. We provide formal propositions demonstrating that trade-off approaches amplify rather than resolve these tensions, and we develop a taxonomy of paradox management strategies with specified contingency conditions. For practitioners, we offer actionable guidance for developing governance structures that neither stifle innovation nor expose organizations to unacceptable risks. The paper concludes with a research agenda for advancing responsible AI governance scholarship.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 67 references that are in the map. First posted on arXiv 28 Jan 2026.

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References · 11

  1. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  2. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
  3. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,543
  4. 2019Green AIcited by 620
  5. 2020Artificial Intelligence, Values and Alignmentcited by 443
  6. 2019Principles alone cannot guarantee ethical AIcited by 134
  7. 2023Generative AIcited by 119
  8. 2019Artificial Intelligence: the global landscape of ethics guidelinescited by 107
  9. 2021AI in Finance: Challenges, Techniques and Opportunitiescited by 81
  10. 2021Responsible AI: Gender bias assessment in emotion recognitioncited by 22
  11. 2025A Mathematical Framework for AI Singularity: Conditions, Bounds, and Control of Recursive Improvementcited by 5

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