Algorithmic Fairness, Meritocracy, and Institutional Justice

Abstract This paper analyzes a variety of technical fairness metrics to show that these models share two common flaws that undermine their ability to contribute to justice. First, technical fairness metrics tend to interpret fairness as an interpersonal comparative measure. Second, they tend to formalize the fundamental value of equality of opportunity in meritocratic terms. I will argue that we must shift away from viewing algorithmic fairness in terms of interpersonal comparisons on the basis of merit and towards an understanding of fairness as an issue of institutional justice. This requires rethinking the role algorithms play in our social institutions. When the algorithmic fairness literature adopts an interpersonal meritocratic view of fairness, this places the algorithm or AI system as simply another agent interacting with individuals within society. But this misrepresents the role algorithms play in our social and institutional structures. In the central cases that animate the algorithmic fairness literature, algorithms play important roles in consequential institutional decisions. To ensure that algorithms and AI systems can serve the interest of justice, reframing algorithmic fairness in terms of institutional justice is both more apt and more action guiding. Data Availability Not applicable. Notes These three authors disagree about which criteria are necessary conditions of fairness. Hereafter cited as TJ. This paper differs from Fleisher’s (2021) critique of individual fairness metrics insofar as it demonstrates that both individual and group-based fairness metrics are built on interpersonal interpretations of fairness. By contrast, the critical philosophical literature tends to be less skeptical, offering a range of solutions to the “impossibility theorem.” See, e.g. the range of responses discussed in the introduction. Section 3.2 briefly outlines Rawls’s principles of justice before focusing on technical specifications of Rawlsian fair equality of opportunity. Franke, 2021 outlines a range of other ways Rawls’s original position and the algorithmic fairness literature diverge in defining risk attitudes, stakeholders, the least advantaged, and knowledge of probabilities. Weidinger et al.2023 empirically test Rawls’s veil of ignorance in the domain of AI and found that participants subject to the veil of ignorance appeared to be driven by increased concerns about fairness when compared with participants who has full knowledge of their own positions within the group. Their model appears far closer to Harsanyi’s utilitarian model, which Rawls rejects (TJ: 118n.11). Thanks to Clark Wolf for suggesting this connection. In Binns’s (2024) response to Franke’s article, he agrees with many aspects of the article, but ultimately argues that a framework of structural justice from Young is likely more fruitful than any Rawlsian approach. See also Franke, 2021 for a range of other incompatibilities between technical specifications of fairness and Rawls’s theory. The link between fairness and accurate predictions for an individual is also embedded in Wang et al., 2024’s defense of “harmless Rawlsian fairness.” I develop the meritocratic underpinnings of algorithmic fairness in § 3. Koopman’s call for a shift to relational equality as a broader framework within which to evaluate algorithmic fairness is another example of a structural approach that I think is a sympathetic companion to the shift towards institutional justice that I defend herein. His defense is built on Elizabeth Anderson’s relational egalitarianism, which I think is the best way to understand Rawls’s own conception of justice. I do not have space to defend this connection here, but see Watson & Hartley, 2018 and Neufeld, 2022 for defense of the connection between Rawls’s theory and relational egalitarianism. Koopman (2025) also argues that the focus on algorithmic fairness is too narrow and ignores the responsibility to build equitable data formats and structures to combat the hierarchies currently built into technologies. A number of other critics of the algorithmic fairness literature highlight the need for a broader scope of analysis beyond the discrete decisions of algorithms in a particular context. See, e.g., Hedden, 2021; Green, 2022; Lin & Chen, 2022; Binns, 2024; Franke, 2024; Koopman, 2025; Cohen & Liu, 2026. This paper complements these calls for a broader scope of analysis by arguing that understanding Rawls’s theory of institutional justice (rather than the interpersonal interpretation of fairness that animates the technical metrics inspired by Rawls) can help guide our analysis of the ways algorithms fit in a broader institutional context and the basic structure of society. Unless otherwise noted, citations in this section are to Barocas et al., 2023. For a recent analysis of Barocas et al., 2023 arguing for the ways current discussions of fairness in machine learning are truncated, see Cohen & Liu, 2026. Cohen and Liu point to three limitations of this literature, grounding their analysis in Rawls’s theory: (i) “unfair organizational decisions are not exclusively about systemic group subordination;” (ii) “achieving equality of opportunity in a society lies well beyond the reach of organizational decisions;” and (iii) “fairness has a broader reach than equality of opportunity.” They argue that these three concerns show that “the normative implications of machine learning (and other algorithmic systems) need to be explored in a broader political register.” I agree with Cohen and Liu’s analysis. My argument can be seen as diagnosing an underlying reason for these limitations, namely that the technical literature interprets fairness as an interpersonal comparative metric rather than a structural and institutional framing of justice. In addition, I highlight the ways equality of opportunity is often interpreted in meritocratic terms that diverge in important ways from their Rawlsian foundations. With their explicit focus on institutional decision-making, it may seem as if Barocas et al. are already operating with an institutional understanding of algorithmic fairness. They analyze machine learning as a way of “interrogating how institutions make decisions about individuals” and explicitly disavow comparisons between machine learning and “the subjective judgments of individual humans” (vi). However, the interpersonal, meritocratic understanding of fairness permeates their analysis in ways that undermine their goal. Luck egalitarianism might serve as a more apt inspiration for meritocratic interpretations of algorithmic fairness. Heidari et al., 2019 contrast their interpretation of Rawls’s equality of opportunity with Roemer’s luck egalitarian model. For more on luck egalitarianism and algorithmic fairness, see Castro et al., 2023. As we’ll see in § 3.2 Rawls argues against meritocratic interpretations of equality of opportunity. However, one need not be a Rawlsian to see a the “moral obligation to help people realize their potential” as an impoverished conception of both equality and justice. Critics of Rawls, including feminists, theorists of structural injustice, Marxist, and a wide variety of egalitarians articulate far more expansive views of justice. For an analysis of technical formalizations of the difference principle, see Franke, 2024. Binns, 2024 argues that many formalizations are better understood as implementing Rawls’s liberty principle. However, contra Binns, I agree with Barocas et al. who frame these as instantiations of equality of opportunity rather than equal liberty. It is also telling that an article by influential computer scientists developing a “weakly meritocratic” fairness metric (Joseph et al., 2017) was first published on Arxiv under the title “Rawlsian Fairness for Machine Learning” and claimed to be “a mathematical formalization of Rawls’s notion of fair equality of opportunity” (Joseph et al. 2016). I do not analyze this article in depth because of the changes between the different published versions. An essential Rawlsian insight is to recognize that society transforms natural differences into just and unjust social structures (TJ: 87–88). See, e.g., Schmidtz, 2006 for a defense of a pluralist account that incorporates desert (i.e., merit) as one component of justice alongside reciprocity, equality, and need. Edenberg and Wood 2023b would call this the ameliorative epistemic lens, when the goal of algorithmic fairness is to build more just social structures. They contrast this ameliorative epistemic goal with a descriptive epistemic goal that seeks to understand existing injustices in the world as it is. In these cases, interpersonal comparisons can help illuminate disparities between groups, but the interpersonal framing of algorithmic fairness is ill-equipped to correct these injustices as it fails to capture algorithms’ institutional roles. My argument differs from Almeida, Mendonca, and Filgueiras (2024)’s argument that we should think of algorithms as institutions in their own right. They argue that algorithms function as institutions and thus should be democratized (Almeida et al., 2024). While my argument is compatible with algorithms being institutions, it does not depend on viewing algorithms as institutions themselves. The fact that algorithms are integrated into important social institutions is enough for them to be evaluated in terms of institutional rather than interpersonal justice. Jain et al. (2024) build on Creel and Hellman’s work to argue that “decision-making algorithms that structure opportunity [should be] meaningfully pluralistic” in order to combat the threats to equal opportunity from algorithmic monocultures. This is an example of a structural approach that aligns with my broader call for evaluating algorithms in terms of institutional justice. Anderson, 1999 develops a sustained critique of luck egalitarianism on these grounds. Franke, 2024 also makes this point in his critique of the algorithmic fairness literature. Thanks to Chloé Bakalar for suggesting this analogy. Koopman (2025) highlights the importance of evaluating not only the data but also the ways in which it is structured as a key component of building data equality. Rawls would also argue that the difference principle is important to ensure not only fair equality of opportunity, but also that any inequalities benefit the least advantaged. However, it is not enough to add the difference principle to meritocratic interpretations of equality of opportunity. There are many other important elements of fair equality of opportunity that are not captured by meritocratic notions. Benn and Lazar (2022) analyze privacy, manipulation, and exploitation to show why we should reject the interactional approach of AI Ethics and replace it with a structural political philosophy of AI. This paper can be seen as taking these lessons into the framework of algorithmic fairness. One could see this argument as an attempt to thread a middle ground between what Lazar and Stone (2024) call the maximalists, who focus on building just societies rather than just algorithms, and the minimalists, who argue that machine learning engineers should focus on optimizing their systems according to purely epistemic standards, leaving policy-makers to shape the social impact of these systems. Iris Marion Young “broadly endorses the intuition behind John Rawls’s claim that the structure is the subject of justice” (2011: 64). However, she expands her view beyond the set of institutions functioning within society to instead emphasize a broader view of systemic wrongs within society, as well as “the everyday habits and chosen actions” of people within society (Young, 2011: 70–71). Thus, structural justice includes both elements of interpersonal and institutional justice within its remit. However, interpersonal elements of structural justice are analyzed at a broader societal level (hence structural justice) rather than the more narrow lens of interpersonal comparisons prevalent in the technical literature. 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Zimmermann, A., Vredenburgh, K., & Lazar, S. (2022). The Political Philosophy of Data and AI. Canadian Journal of Philosophy, 52(1), 1–5. Acknowledgements Thank you to the many people with whom I have discussed the issues in this article and/or who have provided excellent feedback on earlier drafts: Chloé Bakalar, Michael Barnes, Brian Berkey, Étienne Brown, Clinton Castro, Beba Cibralic, Sean Donahue, Alexis Elder, Ayelet Gordon, Tal Herman, Ned Howells-Whitaker, Daniel Kilov, Colin Koopman, Seth Lazar, Katrina Ligett, Ting-An Lin, Jonne Maas, Kobbi Nissim, Kyle van Oosterum, Tomer Shadmy, Nick Schuster, Jake Stone, Charlotte Unruh, Muthu Venkitasubramaniam, Juri Viehoff, Clark Wolf, Alexandra Wood, and Sophia Wushanley. Thanks also to participants and audiences at the MINT Lab’s Political Philosophy & AI Workshop, the Data Cooperatives Working Group, Iowa State University’s Advancing AI and Tech Ethics, and the American Philosophical Association Eastern Division for excellent questions and feedback. Thank you as well to two exceptional referees whose deep engagement with this paper vastly improved it. Funding None. Author information Authors and Affiliations Contributions Edenberg 100%. Corresponding author Ethics declarations Ethics approval and consent to participateAbstract Not applicable. Consent for publication Yes. Competing interests None. Additional information Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Rights and permissions Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. About this article Cite this article Edenberg, E. Algorithmic Fairness, Meritocracy, and Institutional Justice. Philos. Technol. 39, 183 (2026). https://doi.org/10.1007/s13347-026-01157-7 Received: Accepted: Published: Version of record: DOI: https://doi.org/10.1007/s13347-026-01157-7