Algorithmic Accountability examines the process of assigning responsibility for harm when algorithmic decision-making results in discriminatory and inequitable outcomes. The primer–originally prepared for the Progressive Congressional Caucus’ Tech Algorithm Briefing–explores the trade-offs debate
There are few consumer or civil rights protections that limit the types of data used to build data profiles or that require the auditing of algorithmic decision-making, even though algorithmic systems can make decisions on the basis of protected attributes like race, income, or gender.
This brief explores the trade-offs between and debates about algorithms and accountability across several key ethical dimensions, including:
- Fairness and bias;
- Opacity and transparency;
- The repurposing of data and algorithms;
- Lack of standards for auditing
- Power and control; and
- Trust and expertise.