Responsible AI: The Good, The Bad, The AI
Akbar Anbar Jafari, Cagri Ozcinar, Gholamreza Anbarjafari
TL;DR
This paper tackles balancing AI's strategic value with responsible deployment. It reframes responsible AI governance as paradox management, showing that conventional trade-offs tend to amplify tensions between value creation and risk mitigation. The PRAIG framework integrates taxonomies of AI benefits and harms with an integrated governance model and four paradox-management strategies, underpinned by formal constructs (e.g., $V(\mathcal{C})$ and $R(\mathcal{C})$) and dynamics. It validates the framework via a systematic literature review, design-science development, and expert evaluation, and outlines a research agenda for empirical testing. Practically, it guides executives to embrace paradox, tailor governance to context, and leverage feedback loops to sustain responsible AI deployment.
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.