The Data&Society Research Institute just published its primer on “Algorithmic Accountability,” originally presented to the Congressional Progressive Caucus on April 18, 2018 as “Tech Algorithm Briefing: How Algorithms Perpetuate Racial Bias and Inequality.” Here’s a synopsis of its contents:
Algorithmic Accountability: A Primer explores issues of algorithmic accountability, or the process of assigning responsibility for harm when algorithmic decision-making results in discriminatory and inequitable outcomes.
Currently, 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–even when those attributes are not referenced explicitly–because there are many effective proxies for the same information.
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.