Algorithmic fairness

John Patty and Elizabeth Maggie Penn

Chapter 3 in Elgar Encyclopedia of Public Choice, 2025, pp 17-26 from Edward Elgar Publishing

Abstract: Algorithmic fairness is a term describing the conceptualization, and remediation, of bias in rule-based procedures for making decisions about different types of individuals. At heart is a presumption that some ways of discriminating between people are undesirable (i.e., “unfair”), while others are acceptable or even desirable (i.e., “permissible”). This field has quickly emerged as an active, important, and multidisciplinary research agenda driven by the increased use, and sophistication, of algorithms that affect people on an everyday basis. In this chapter, we discuss the emerging literature on algorithmic fairness and some of its connections to public choice, political economy, and social choice.

Keywords: Algorithmic fairness; Classification; Statistical discrimination; Inequality; AI; Machine learning (search for similar items in EconPapers)
Date: 2025
ISBN: 9781802207743
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.elgaronline.com/doi/10.4337/9781802207750.00008 (application/pdf)
Our link check indicates that this URL is bad, the error code is: 403 Forbidden

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:elg:eechap:21298_3

Ordering information: This item can be ordered from
http://www.e-elgar.com

Access Statistics for this chapter

More chapters in Chapters from Edward Elgar Publishing
Bibliographic data for series maintained by Jack Sweeney ().