全球智库信息集成服务系统

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.

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.

• Chapter One

  Introduction

• Chapter Two

  Concepts of Equity

• Chapter Three

  Domain Exploration: Algorithms in Auto Insurance

• Chapter Four

  Domain Exploration: Algorithms in Employee Recruitment

• Chapter Five

  Domain Exploration: Algorithms in U.S. Criminal Justice Systems

• Chapter Six

  Analytic Case Study: Auditing Algorithm Use in North Carolina's Criminal Justice System

• Chapter Seven

  Insights and Recommendations

• Appendix A

  Supplemental Tables