ECHO: A Participatory Framework for Bias-Anchored AI Harm Anticipation
Quick summary
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 \texttt{ECHO}, a systematic, context-sensitive, and participatory framework that anchors early harm ant
Key takeaways
- 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.
Why it matters
The value of this work lies as much in how it was tested as in the claim itself. Sample design, baselines, uncertainty and replication help separate a laboratory result from real-world impact.
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