AAAI - AISI Track, 2026
This paper explores how fairness interventions in algorithmic hiring systems can create misleading perceptions of equity. Using audit study data, we evaluate the effectiveness and limitations of these interventions in real-world settings.
Fairness in Hiring Decisions Using ML Models and Audit Study Data
EU funded research between European Universities about construals for purposes and their applications towards creating open interactive resources