Algorithmic bias detection and mitigation: Best practices and policies to reduce consumer harms

Individual Author(s) / Organizational Author
Lee, Nicol
Resnick, Paul
Barton, Genie
Publisher
The Brookings Institution
Date
May 2019
Abstract / Description

The private and public sectors are increasingly turning to artificial intelligence (AI) systems and machine learning algorithms to automate simple and complex decision-making processes. The mass-scale digitization of data and the emerging technologies that use them are disrupting most economic sectors, including transportation, retail, advertising, and energy, and other areas. AI is also having an impact on democracy and governance as computerized systems are being deployed to improve accuracy and drive objectivity in government functions.

The availability of massive data sets has made it easy to derive new insights through computers. As a result, algorithms, which are a set of step-by-step instructions that computers follow to perform a task, have become more sophisticated and pervasive tools for automated decision-making. While algorithms are used in many contexts, we focus on computer models that make inferences from data about people, including their identities, their demographic attributes, their preferences, and their likely future behaviors, as well as the objects related to them. (author abstract)

Artifact Type
Research
Reference Type
Report
P4HE Authored
No
Topic Area
Policy and Practice