Identifying bias when sensitive attribute data is unavailable

The perils of automated decision-making systems are becoming increasingly apparent, with racial and gender bias documented in algorithmic hiring decisions, health care provision, and beyond. Decisions made by algorithmic systems may reflect issues with the historical data used to build them, and understanding discriminatory patterns in these systems can be a challenging task [1]. Moreover, … Continue reading “Identifying bias when sensitive attribute data is unavailable”