Search Results for « Responsible AI »

Abstract Responsible artificial intelligence (RAI) has emerged in response to growing concerns about the impact of AI. While high-level principles have been provided, operationalizing these principles poses challenges. This study, grounded in recent RAI literature in organizational contexts and dynamic capability theory, and informed by literature on RAI principles and expert interviews in organizations deploying AI systems, (1) problematizes the high-level principles and low-level requirements and underscores the need for mid-level norms by adopting dynamic capability as a theoretical lens, and (2) develops five themes to capture firms’ RAI capability, including (i) understandable AI model, (ii) bias remediation, (iii) responsiveness, (iv) harmless, and vi) common good. As our contribution to the field of information systems (IS), this study extends the emerging literature on operationalizing RAI and dynamic capabilities, empirically elucidating the capabilities needed by firms. For IS practice, we provide organizations deploying AI with novel insights to aid in the responsible implementation of AI.

The rapid diffusion of artificial intelligence (AI) has transformed governance from a predominantly technical challenge into a normative and institutional one. Although the Responsible AI literature has established widely accepted ethical principles including fairness, accountability, transparency, explainability, and human oversight, it has devoted comparatively little attention to the institutions responsible for translating these principles into legitimate governance. Existing scholarship largely treats institutions as mechanisms for implementing ethical commitments rather than as normative actors whose responsibility itself requires philosophical justification. This article addresses that conceptual gap by asking: Can Responsible AI exist without Responsible Institutions? Using a philosophical and interdisciplinary conceptual analysis, it draws on Responsible AI scholarship, institutional theory, public administration, political philosophy, and digital government to develop Institutional Responsibility as a distinct normative concept and proposes the Institutional Responsibility Framework (IRF) as its principal theoretical contribution. The framework conceptualises responsibility as institutionally constituted, distributed, exercised, justified, and sustained across complex AI governance ecosystems. It argues that Responsible AI presupposes responsible institutions because ethical principles cannot govern AI independently of the institutional arrangements that establish legitimate authority, coordinate governance, and sustain public trust. By relocating responsibility from isolated actors and principle-based compliance to the institutional foundations of governance, the article advances a new philosophical foundation for Responsible AI. It contributes to AI ethics, institutional theory, public administration, and digital government by demonstrating that trustworthy, legitimate, and democratically accountable AI governance ultimately depends upon the existence of responsible institutions rather than ethical principles alone.