The primary focus of this report is understanding how to evaluate the equitable use of algorithms across a range of specific applications. The report outlines concepts of equity from philosophical, legal, and technical standpoints to draw insights that apply across algorithmic decisionmaking contexts for several domains, including auto insurance, recruiting, and criminal justice.

Algorithmic Equity

A Framework for Social Applications

Osonde A. Osoba, Benjamin Boudreaux, Jessica Saunders, J. Luke Irwin, Pam A. Mueller, Samantha Cherney

ResearchPublished Jul 11, 2019

Social institutions — such as markets, social media platforms, criminal justice systems, and employment processes — increasingly leverage algorithms for decisionmaking purposes. This report examines potential pathologies in institutional use of algorithmic decisionmaking tools. The primary focus of this report is to understand how to evaluate the equitable use of algorithms across a range of specific applications. The report outlines concepts of equity from philosophical, legal, and technical traditions to draw insights that apply across algorithmic decisionmaking contexts. The researchers develop a framework for examining algorithmic decisionmaking and work through three domain explorations (auto insurance, job recruitment, and criminal justice). In addition, the work contains a deep dive into an algorithm audit of a part of the North Carolina criminal justice system. The work ends with overall insights and recommendations of practical mechanisms for algorithmic governance. The subject of the report is important because unaddressed equity challenges can undermine the stability and legitimacy of social institutions and lead to severe adverse impacts on affected people.

Key Findings

Fairness, or equity, is a complex and very domain-specific concept

  • Equity is a contested concept, and it will require domain-specific examination.
  • Prohibiting the use of sensitive attributes is less effective, given advanced algorithms and large secondary data sets.
  • The ground truth accuracy criteria may be inadequate.
  • Algorithmic transparency is important, but it is not a panacea.
  • Implementation practices matter. Implementation and institutional factors can both improve or reduce fairness, regardless of the fairness of the algorithms themselves.
  • There are useful technical and regulatory interventions that can address or reduce equity challenges in algorithmic decisionmaking.

Recommendations

  • Particular, domain-specific concepts of equity should be clear to stakeholders.
  • Rely less on sensitive attribute designation for assuring equity.
  • Ground truth accuracy may not always be a neutral success condition for judging decisionmaking models.
  • Signals of trustworthiness — opening the "black box" — have to be appropriate to the institutional context and relevant stakeholders.
  • Institutions deploying algorithms should put procedures in place to monitor or evaluate the response to algorithmic decisionmaking artifacts, not just the performance of these artifacts.
  • Designers and deployers of algorithms may benefit from adopting an algorithmic equity checklist approach to minimize undesirable equity outcomes.

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Osoba, Osonde A., Benjamin Boudreaux, Jessica Saunders, J. Luke Irwin, Pam A. Mueller, and Samantha Cherney, Algorithmic Equity: A Framework for Social Applications. Santa Monica, CA: RAND Corporation, 2019. https://www.rand.org/pubs/research_reports/RR2708.html.
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