Spotting (and Missing) Algorithmic Bias: Investigating User Understanding in a Fairness Assessment Tool

Fairness metric selection is typically left to data scientists, but which biases are problematic and which metric captures them best depends on stakeholders' experience and domain knowledge. This calls for involving non-technical stakeholders, but the research prototypes built for this purpose so fa