Quantifying social harm requires moving beyond abstract ethics into hard data. We can use disparate impact metrics, a standard borrowed from civil rights law, to track how specific algorithms deny loans, housing, or employment to protected classes. By mapping these outcomes to lost wages, decreased health access, or psychological stress, we convert systemic bias into measurable socioeconomic data. We might look at the Gini coefficient to see how AI tools redistribute opportunity away from historically disadvantaged ZIP codes.
Another approach involves conducting longitudinal studies on algorithmic harms. If a facial recognition error rate causes a specific demographic to face higher rates of wrongful arrest, that constitutes a tangible cost. We can track these harms through legal filings, medical outcomes, and economic mobility stats. Instead of just discussing hypothetical
