Sep 2026· AI and Ethics· Vol 6· 0 citations· 57 references
Ethics and Social Impacts of AI
TL;DR
The study demonstrates how MHC can be translated from an ethical principle into governance practice and shows that meaningful control depends not only on human involvement in decisions, but also on defined purposes and boundaries, operational procedures for review and correction, legal safeguards, and institutional capacity for oversight and intervention.
By showing that AI use intensifies rather than replaces human judgment, the study underscores the continued centrality of human responsibility in public decision-making and suggests that effective AI governance requires a shift from restrictive, compliance-focused approaches towards enabling and adaptive strategies.
The STAGE framework is proposed as a practical approach to maintaining human judgment and organizational accountability when integrating AI into regulatory compliance and focuses on whether organizations can use AI to identify risks while keeping qualified people responsible for interpreting alerts, making decisions, d...
Monisade Oluwagbemigun· International journal of res...· 0 citations
The article develops a four-dimensional framework comprising technological infrastructure, institutional delegation, legal-regulatory structuring, and platform power, and identifies four mechanisms through which authority is formed: efficiency legitimation, risk categorization, responsibility diffusion, and institution...
This study conceptualizes AI governance and integrates cross-sector governance risks and challenges to develop an adaptive, ethical and responsible AI governance framework that embeds risk management, responsiveness, and ethics-by-design across the AI lifecycle.
Z. R. M. Abdullah Kaiser· Discover Cities· 0 citations
This paper argues for a transition from AI Governance as Compliance to AI Governance Engineering , a systems-oriented discipline in which governance is embedded throughout the enterprise intelligence lifecycle, enabling enterprise intelligence systems that are secure, explainable, trustworthy, and governable by design.
Faruk Çelikkanat· International Journal of Res...· 0 citations
This Policy and Practice Review concludes that AI-enabled administration remains legitimate only where public authorities retain the capacity to understand, justify, correct, suspend and democratically control the systems they use.
A. Dragomir, Iulea Bulea, Lucian Tarnu· Frontiers in Political Scien...· 2 citations
Related blog posts
MIT News · Artificial Intelligence· news.mit.eduOct 30, 2026
MIT professors Adam Berinsky and Chara Podimata discuss a new project to track how AI chatbots are responding to questions about the 2026 U.S. midterm elections.
Microsoft Research Blog· microsoft.comOct 30, 2026
Evaluating Microsoft Copilot at enterprise scale requires test data that captures the full complexity of real organizations, yet real enterprise data contains sensitive information that cannot be used for evaluation under any circumstances. The post From synthetic enterprise data evaluations to real Microsoft Copilot AI confidence appeared first on Microsoft Research.
Critical flaws, compromised release channels and agentic AI threats landed within days of each other in mid-October 2026. Why “Black October” may say more about how well we now find weaknesses than about the attacks themselves. The post Black October? The Week AI Changed the Cybersecurity Conversation appeared first on GPT-Lab.
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
