ECHO: A Participatory Framework for Bias-Anchored AI Harm Anticipation

arXiv:2512.03068v2 Announce Type: replace-cross Abstract: Artificial Intelligence (AI) systems increasingly shape consequential decisions, creating value but also potential harms for individuals, social groups, and society. This has prompted calls for proactive approaches that anticipate harms early in the AI lifecycle. Although prior research identifies AI biases as sources of harm, the associations between particular lifecycle biases and harms remain insufficiently understood. We introduce exttt{ECHO}, a systematic, context-sensitive, and participatory framework that anchors early harm anticipation in lifecycle biases and elicits their perceived associations with potential harms.exttt{ECHO} identifies domain-specific stakeholders, instantiates biases through vignettes, collects harm judgements from human participants and a large language model (LLM), and organises them into descriptive and inferential ethical matrices. Applied to disease diagnosis and hiring, exttt{ECHO} surfaced non-uniform, context-sensitive bias--harm patterns indicating which harms were perceived as plausible consequences of particular AI biases. The theoretical interpretability of these patterns and the inferential support for specific associations strengthen the plausibility of the mappings. By linking stakeholder-specific anticipated harms to lifecycle biases, exttt{ECHO} supports source-level harm anticipation and provides structured input to subsequent AI governance actions

Related

• Whose fairness? Structural concentration in AI bias research
• From Silos to Systems: Process-Oriented Hazard Analysis for AI Systems
• Ethics Testing: Proactive Identification of Generative AI System Harms
• Assessing Trustworthiness of AI Training Dataset using Subjective Logic -- A Use Case on Bias

Source: arXiv cs.AI | 2026-10-02