Related papers: Responsible AI: The Good, The Bad, The AI

Responsible AI: The Good, The Bad, The AI

URL: http://arxiv.org/abs/2601.21095v1
Date: Wed, 28 Jan 2026 22:33:27 GMT
Title: Responsible AI: The Good, The Bad, The AI
Authors: Akbar Anbar Jafari, Cagri Ozcinar, Gholamreza Anbarjafari,
Abstract summary: This paper presents a comprehensive examination of AI's dual nature through the lens of strategic information systems.<n>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.<n>The paper concludes with a research agenda for advancing responsible AI governance scholarship.
Score: 1.932555230783329
License: http://creativecommons.org/licenses/by/4.0/
Abstract: 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.

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