Countering the Risks of AI Overreliance: Leveraging Multi-Agent Debate for Complementary Human–AI Decision-Making in Legal Contexts

Balancing Trust and Deliberation in Human–AI Decision Support: The Effects of Explainable AI and Cognitive Forcing Functions

This work examines how six decision-support mechanisms affect engagement, trust, and collaborative task performance in a diabetes meal-planning scenario and argues for a contextual, balanced pairing of CFF and XAI design that accounts for interactivity, decision frequency, and task complexity.

Confidence Amplification in Human–AI Moral Decision-Making

As artificial intelligence (AI) systems increasingly engage users in value-laden discussions, a key concern is whether they shape not only what people decide but how confident they feel. We examined confidence amplification, shifts in certainty without decision reversals, in human–AI moral decision-making. In a 2 (Time...

Human-weighted ethical pluralism in AI decision support: introducing the weighted meta-decision engine (WMDE) through the Omelas dilemma

The Weighted Meta-Decision Engine (WMDE), a human-governed, AI-assisted procedure that separates several functions: selecting relevant ethical theories, generating theory-specific AI proposals, evaluating those proposals, and combining them through human-derived weights, is introduced.

Defensible but Not Owned: Evidence of Agency Erosion in Agentic AI-Assisted Argumentation A Pilot Study from a Multi-Stage Policy-Writing Simulation

Human realignment

It is found that at least for the time being, explicit normative instructions are not fully able to realign AI advice with the normative convictions of the population, or the legislator deciding on its behalf.

Modeling AI Overreliance as a Complex Adaptive System

This work model it as a population process: agents repeatedly solve a task alone, accept an AI answer, or verify it, updating a Bayesian belief about AI quality and, when networked, learning from peers.