Measuring the right thing: justifying metrics in AI impact assessments
This addresses the challenge of ensuring AI impact assessments are effective for policymakers and developers, though it is incremental as it builds on existing conceptual engineering tools.
The paper tackles the problem of justifying metrics in AI impact assessments, particularly for ethical and social values, by proposing a two-step approach that involves spelling out conceptions and fitting metrics to them, resulting in a framework for clearer and more motivated metric choices.
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 fairness as solidarity) and then a metric can be fitted to that conception. Both steps require separate justifications, as conceptions can be judged on how well they fit with the function of, for example, fairness. We argue that conceptual engineering offers helpful tools for this step. Second, metrics need to be fitted to a conception. We illustrate this process through an examination of competing fairness metrics to illustrate that here the additional content that a conception offers helps us justify the choice for a specific metric. We thus advocate that impact assessments are not only clear on their metrics, but also on the conceptions that motivate those metrics.