What Makes a Good AI Review? Concern-Level Diagnostics for AI Peer Review
Evaluating AI-generated reviews by verdict agreement is widely recognized as insufficient, yet current alternatives rarely audit which concerns a system identifies, how it prioritizes them, or whether those priorities align with the review rationale that shaped the final assessment. We propose concern alignment, a diagnostic framework that evaluates AI reviews at the concern level rather than only at the verdict level. The framework's core data structure is the match graph, a bipartite alignment
Record details
Published: 21 April 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Healthcare · Transparency
Retrieved: 14 July 2026
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ethics.ai (21 April 2026), “What Makes a Good AI Review? Concern-Level Diagnostics for AI Peer Review,” evidence record 5523, https://ethics.ai/record/5523 (originally published by arXiv).
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