PERSPECTIVE article
Front. Artif. Intell.
Sec. Technology and Law
Volume 8 - 2025 | doi: 10.3389/frai.2025.1637134
ALGORITHMIC FAIRNESS: CHALLENGES TO BUILDING AN EFFECTIVE REGULATORY REGIME
Provisionally accepted
- Fairlogic, Pasadena, United States
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Unfair treatment by artificial intelligence toward protected groups has become an important topic of discussion. Its potential for causing harm has spurred many to think that legislation aimed at regulating AI systems is essential. In the US, laws have already been proposed both by Congress as well as by several key states. However, a number of challenges stand in the way of effective legislation. Proposed laws mandating testing for fairness must articulate clear positions on how fairness is defined. But the task of selecting a suitable definition (or definitions) of fairness is not a simple one. Experts in AI continue to disagree as to what constitutes algorithmic fairness, which has led to an ever-expanding list of definitions that are highly technical in nature and require expertise that most legislators simply don't possess.Complicating things further, several of the proposed definitions are incommensurable with one another, making a cross-jurisdictional regulatory regime incorporating different standards of fairness susceptible to inconsistent determinations. On top of all this, legislators must also contend with existing laws prohibiting group-based discrimination that codify conceptions of fairness that may not be suitable for evaluating certain algorithms. In this article, I examine these challenges in detail, and suggest ways to deal with them such that the regulatory regime that emerges is one that is more effective in carrying out its intended purpose.
Keywords: artificial intelligence, Algorithmic Fairness, fairness definitions, Proposed legislation, Bias, Discrimination, protected groups, Adverse impact
Received: 28 May 2025; Accepted: 12 Aug 2025.
Copyright: © 2025 Demirchyan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Greg Demirchyan, Fairlogic, Pasadena, United States
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