Title: Strategic Costs of Perceived Bias in Fair Selection
Venue: NeurIPS 2025
Accepted: yes
Keywords: Meritocratic selection

Strategic effort

Game-theoretic modeling

Perceived valuation bias

Algorithmic fairness

Social feedback loops

Representation and efficiency

Nash equilibrium

Human-AI interaction
Abstract: Meritocratic systems, from admissions to hiring, aim to impartially reward skill and effort. Yet persistent disparities across race, gender, and class challenge this ideal. Some attribute these gaps to structural inequality; others to individual choice. We develop a game-theoretic model in which candidates from different socioeconomic groups differ in their perceived post-selection value—shaped by social context and, increasingly, by AI-powered tools offering personalized career or salary guidance. Each candidate strategically chooses effort, balancing its cost against expected reward; effort translates into observable merit, and selection is based solely on merit. We characterize the unique Nash equilibrium in the large-agent limit and derive explicit formulas showing how valuation disparities and institutional selectivity jointly determine effort, representation, social welfare, and utility. We further propose a cost-sensitive optimization framework that quantifies how modifying selectivity or perceived value can reduce disparities without compromising institutional goals. Our analysis reveals a perception-driven bias: when perceptions of post-selection value differ across groups, these differences translate into rational differences in effort, propagating disparities backward through otherwise "fair" selection processes. While the model is static, it captures one stage of a broader feedback cycle linking perceptions, incentives, and outcomes—bridging rational-choice and structural explanations of inequality by showing how techno-social environments shape individual incentives in meritocratic systems.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper investigates strategic asymmetries arising from valuation differences in settings where group sizes are known and fixed, such as admissions and hiring. The authors propose a formal framework to model these strategic interactions, aiming to provide interpretable predictions and support data-grounded policy interventions. The paper presents a theoretical analysis, including definitions, theorems, and discussions of metrics.\n\nSTRENGTHS AND WEAKNESSES:\n### Strengths\n* S1 – The paper addresses a highly relevant and important problem concerning strategic behavior in critical application areas like admissions and hiring. The proposition that valuation differences lead to strategic asymmetries in fixed-group settings is compelling and aligns with real-world observations. I believe this is a very important and underresearched area of algorithmic fairness literature, focus on formal neutrality and static metrics leave much underexplored in fair selection. By focusing on valuation gaps, the work complements the dominant literature on classifier bias and post-hoc fairness metrics. \n\n* S2 – Clear Presentation of Theoretical Framework: While the mathematical depth is significant, the paper is generally well-written, well-cited, and the notation is clear. The definitions and theorems are set out lucidly, making the overall structure and conceptual flow of the theoretical framework understandable, even without deep expertise in game theory. The metrics introduced in Section 4 are clearly defined and appear appropriate for the problem context, drawing on established concepts in the relevant literature.\n\n### Weaknesses\n* W1 – While the authors claim their work is the first to study strategic asymmetries arising from valuation differences in settings with known and fixed group sizes (a compelling claim if true), the main body of the paper does not sufficiently articulate how its specific contributions differentiate it from existing literature. Although Appendix A provides detailed related work, a concise explanation of the paper's novelty should be integrated into the main text to immediately clarify its unique contribution for the reader. Despite reviewing supplementary material it still isn't perfectly clear to me what exactly is unique about your method. \n\n* W2 – The paper states that its \"framework enables interpretable predictions and supports data-grounded policy interventions.\" However, it does not explicitly elaborate on how this framework achieves these goals. While concrete guidance on parameter estimation (e.g. recovering ρ from survey or wage data) is deferred to future work, more detailed discussion or concrete examples illustrating the path from theoretical insights to practical policy implications would strengthen this claim.\n\n* W3 - Real-world fit is uncertain. The theory assumes very large applicant pools and that every person always chooses the effort level that maximises their payoff. In practice, many selection rounds involve only a few dozen or a few hundred candidates, and people often make decisions based on limited information, habits, or simple rules of thumb. Because of this, the neat patterns the model predicts might look quite different when applied to smaller, messier real-world settings.\n\nQUESTIONS:\n1. Could you elaborate on the specific aspects of your framework that enable \"interpretable predictions\" and \"data-grounded policy interventions\"? Perhaps provide a brief, concrete example of how a policy maker could use your framework's outputs.\n2. Could you briefly summarise in the main text (e.g., in the introduction or related work section) the precise novel contribution of your work especially in contrast to the most closely related prior research?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-18"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper studies a contest with two demographic groups. The groups differ only in their perception of how valuable winning the contest is. This is interpreted as the disadvantaged group having a lower valuation of the win, and how that impacts different performance criteria, including the representation ratio, the welfare ratio, and the institution’s revenue.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**:\n- The paper provides a new perspective on the issue of disparities in competitive selection processes (like school admissions), by considering asymmetric valuations. It is also different from the literature on strategic classification because of competition between agents, and therefore complements that line of work well, too. \n- The paper is quite well-written. The intuitive walkthrough of the main theorem is clear and informative. The sensitivity analysis corollaries and experiments are well-thought. \n- Several of the provided insights from the theoretical analysis have the potential to be informative for questions of contest/institution design and interventions to encourage underrepresented minorities to consider exerting effort for competitive opportunities. \n\n**Weaknesses**:\n- Some of the statements in the introduction and their alignment with the results are unclear. Specifically, the paper does open by noting that structural barriers are a key driver of disparity in representation rates during competitive selection processes, including “unequal access to resources that enhance merit”. The closest element of the model to these, to me, is p_a, the distribution of initial ability. However, this is taken to be group-independent throughout the paper. Beyond this, it is not clear if there are elements in the model that are capturing the “structural” rather than “behavioral” aspects. If no such \"structural\" elements exist in the model, then that makes it unclear why “This paper reconciles structural and behavioral explanations”, as stated on top of page 2. \n\n- Some additional requests for clarification and questions are added below.\n\nQUESTIONS:\n1. How is the model or its results reflecting on the underlying “structural” and societal issues?\n\n2. The paper considers the representation ratio to be the min of the representation ratios of the groups. Isn’t it always the case that the disadvantaged group ends up being underrepresented in the outcome? And if not, why not?\n\n3. It seems that the model can be used to answer an additional question: if the institution sets per-group capacities (i.e., a group specific selection rate c), then how would the effort the disadvantaged group exert compare to the one when they are under a uniform c rate? (even keeping the valuations asymmetric). Is this correct? It would be interesting to know more about this aspect as well. \n\n4. Section 8, line 299, mentions some default values when some parameters are not being varied. It is assumed then that the corresponding results sensitive to these choices? For instance, is the takeaway at the end of the section (“This suggests that expanding access is more impactful under high disparity, while improving group valuation is better when gaps are narrower.” ) independent of the \\alpha and c selected based on the JEE Advanced data?\n\n5. Typos: line 127, a missing comma in the definition of the representation ratio. Line 247: “is to that” --> “is that”\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 2, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-21"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nTo examine how selection rates can diverge across groups even under group-blind selection criteria, the authors model a contest setting in which agents rationally choose effort based on their expected benefit from selection. In this setup, selection depends on a combination of ability and effort, but only effort is chosen strategically. When the expected benefit of selection is lower for one group—modeled as a compressed distribution of valuation (captured by the parameter rho)—members of that group exert less effort on average, resulting in lower selection rates. The authors characterize equilibrium behavior in this setting and analyze how group disparities in selection, welfare, and average “quality” of selected candidates depend on key parameters: the degree of valuation compression (rho), the selectivity of the institution (cc), and the share of disadvantaged candidates in the population (alpha).\n\nSTRENGTHS AND WEAKNESSES:\nStrengths-\n\n-\tThe paper introduces an elegant and intuitive framework to capture how group-based differences in the perceived value of success (landing a job, receiving a loan, etc.) can affect effort and, in turn, selection outcomes, even when the selection rule is group-blind. While this mechanism will feel familiar to economists as a classic asymmetric-contest setup, it offers a valuable perspective for a CS/ML audience, where fairness analyses often focus narrowly on constraints and selection rules. The model highlights that disparities can arise endogenously when valuations differ across groups.\n-\tThe Nash equilibrium solution, where effort is exerted only by individuals whose valuation plus ability exceed a common threshold, is analytically clean and well suited to quantifying disparities. The resulting inequalities in selection rates and payoffs are both interpretable and empirically relevant.\n-\tThe authors clearly explain the role of key parameters in the model (such as valuation bias rho, selection rate c, and group proportion alpha) and how changes in these parameters affect the fairness metrics.\n-\tThe paper is well-motivated by real-world phenomena, such as wage gaps and unequal returns to opportunity, that suggest group-based differences in the perceived value of “winning” are empirically plausible and normatively important.\n-\tOverall, the paper brings a less-studied dimension of fairness into focus: how group disparities in incentives or perceived benefit can drive unequal outcomes even in the absence of explicit discrimination or biased selection.\n\nWeaknesses \n\n-\tTo some extent, the paper’s strength is also its weakness: while the analysis is elegant and clean, it may be too stylized to capture important real-world dynamics. In particular, it abstracts away from richer feedback mechanisms and strategic institutional behavior that are well-studied in the economics literature on statistical discrimination.\n-\tFor example, the paper repeatedly invokes the idea of a “feedback loop,” but it is unclear what that loop entails. The model is static, and group disparities arise entirely from exogenous differences in valuations. That is, differences in selection rates do not feed back into future valuations, institutional behavior, or perceptions, making the use of “feedback” more rhetorical than actually part of the model.\n-\tSurprisingly, the paper does not engage with the economics literature on statistical discrimination, despite addressing similar questions. While the authors may not come from an economics background, their model aligns more naturally with this tradition than with many of the social science works cited. Foundational work such as Phelps’s “The Statistical Theory of Racism and Sexism” (1972), Arrow’s “The Theory of Discrimination” (1973), Coate and Loury’s “Will Affirmative-Action Policies Eliminate Negative Stereotypes?” (1993), and Craig and Fryer’s “Complementary Bias: A Model of Two-Sided Statistical Discrimination” (2013) examine settings where disparities emerge endogenously from a feedback loop between agent behavior and institutional inference. These models typically feature rational expectations equilibria in which institutions interpret noisy signals differently across groups, reinforcing group-level behavioral patterns.\n-\tMore broadly, engaging with this literature would not only help position the contribution more clearly but also point to natural extensions. For instance, the current model assumes that institutional payoff is a direct function of observable scores. However, in many settings, especially in hiring or lending, payoff depends on unobserved or latent characteristics like ability (or creditworthiness), and effort merely increases the signal. Modeling the institution as one that tries to infer ability from scores (and does so differently for each group) would allow the paper to explore how strategic inference might perpetuate or mitigate disparities. It would also bring the institutional actor more clearly into focus, rather than treating it as a passive selector.\n-\tAnother potential limitation is the assumption that the disadvantaged group has lower valuations. While this is plausible in some setting, for example, where minorities being treated poorly at work (created a lower value to the job, assuming job climate is part of the evaluation), there are others (like lending) where the opposite may be true: marginalized groups might derive higher value from access to opportunity (loans or jobs), due to lack of outside options or greater marginal returns. It would be helpful for the authors to clarify when this compressed valuation assumption is realistic and when it might not be.\n-\tRelatedly, the model assumes that differences in valuation are problematic, but it is worth considering when they may reflect legitimate preference heterogeneity. For example, if one group places more value on work-life balance and therefore exerts less effort for high-paying jobs, should we interpret the resulting selection rate disparities as unfair? The paper might benefit from more discussion of when group differences in effort due to valuation gaps are normatively troubling.\n-\tThe assumption of a threshold strategy—where agents either exert just enough effort to be selected or none at all—also limits applicability. In many real-world contests (like job applications, school admissions), individuals may over-prepare or exert continuous effort for marginal improvements, especially when downstream rewards like salary or financial aid are continuous rather than binary. This opens the door to modeling richer selection environments, such as multi-stage contests or settings with heterogeneous reward structures.\n-\tOverall, while these issues do not undermine the core contribution, the paper would benefit from a more nuanced discussion of its simplifying assumptions, clearer positioning relative to existing models of discrimination and effort, and acknowledgment of avenues for future work.\n\nQUESTIONS:\n1.\tClarify the meaning of the feedback loop- the abstract and introduction refer to a behavioral “feedback loop” as a core motivation, but the model appears static: group-based disparities arise from fixed, exogenous differences in valuations. Could you clarify whether the feedback loop is meant as part of the formal model or just the broader motivation? If the latter, it would help to revise the language to avoid suggesting a dynamic or endogenous mechanism. extensions (e.g., beliefs updating over time or feedback via algorithmic guidance tools). \n2.\tposition the paper relative to the statistical discrimination literature- the model closely resembles ideas from the economics literature on statistical discrimination. These works emphasize feedback between group behavior and institutional inference. Your model differs in that the institution is passive and group-blind, but that distinction is not clearly drawn. I recommend engaging with this literature more explicitly to clarify what is novel about your setup and what simplifying assumptions are being made. \n3.\tBetter motivate the setup and assumptions- for example, the assumption that one group derives less value from selection (modeled via a compressed valuation distribution) is plausible in some domains, but less so in others. I would encourage you to (a) clarify when this assumption holds, (b) discuss when it might be reversed, and (c) consider how the model might be adapted to allow for heterogeneity in valuations or outside options.\n4.\tIdeally, you would be able to say something about how the analysis would change if you had a strategic firm, even if you do not formally solve the model.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}