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<p>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 PRISMA&#xa0;2020 compliant systematic review of 88 peer-reviewed studies (2018–2025) and grounded in paradox theory, we develop the Paradox-based Responsible AI Governance (PRAIG) framework, which articulates: (1) the strategic benefits of AI adoption (<i>the Good</i>), (2) the inherent risks and unintended consequences (<i>the Bad</i>), and (3) governance mechanisms that enable organizations to navigate these tensions (<i>the AI</i>). 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—explicitly positioned as <i>illustrative</i> formalisations of the underlying conceptual logic rather than deductively closed theorems—that show why trade-off (Pareto) approaches systematically amplify rather than resolve these tensions when the value–risk frontier is non-stationary, and we develop a taxonomy of paradox management strategies with operational decision rules and contingency conditions. The framework is validated through a structured expert panel (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(n=12\)</EquationSource> </InlineEquation>, with reported inter-rater reliability and qualitative feedback) and four illustrative organizational cases. 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.</p>

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Responsible AI: the good, the bad, the AI

  • 期刊论文

  • 发表日期：

    2026年7月1日

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  • Akbar Anbar,
  • Cagri Ozcinar,
  • Gholamreza Anbarjafari

摘要

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 PRISMA 2020 compliant systematic review of 88 peer-reviewed studies (2018–2025) and grounded in paradox theory, we develop the Paradox-based Responsible AI Governance (PRAIG) framework, which articulates: (1) the strategic benefits of AI adoption (the Good), (2) the inherent risks and unintended consequences (the Bad), and (3) governance mechanisms that enable organizations to navigate these tensions (the AI). 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—explicitly positioned as illustrative formalisations of the underlying conceptual logic rather than deductively closed theorems—that show why trade-off (Pareto) approaches systematically amplify rather than resolve these tensions when the value–risk frontier is non-stationary, and we develop a taxonomy of paradox management strategies with operational decision rules and contingency conditions. The framework is validated through a structured expert panel ( \(n=12\) , with reported inter-rater reliability and qualitative feedback) and four illustrative organizational cases. 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.