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
Big decisions about people’s lives are increasingly made by software systems and algorithms. This primer explores issues of algorithmic accountability, or the process of assigning responsibility for harm when algorithmic decision-making results in discriminatory and inequitable outcomes.
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:
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.