Title: Individual Fairness In Strategic Classification
Venue: NeurIPS 2025
Accepted: yes
Keywords: Individual Fairness

Strategic Classification

Group Fairness
Abstract: Strategic classification, where individuals modify their features to influence machine learning (ML) decisions, presents critical fairness challenges. While group fairness in this setting has been widely studied, individual fairness remains underexplored. We analyze threshold-based classifiers and prove that deterministic thresholds violate individual fairness. Then, we investigate the possibility of using a randomized classifier to achieve individual fairness. We introduce conditions under which a randomized classifier ensures individual fairness and leverage these conditions to find an optimal and individually fair randomized classifier through a linear programming problem. Additionally, we demonstrate that our approach can be extended to group fairness notions. Experiments on real-world datasets confirm that our method effectively mitigates unfairness and improves the fairness-accuracy trade-off.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors consider the problem of enforcing individual fairness when deploying threshold classifiers in situations where individuals engage in strategic behavior. They find that deterministic classifiers will fail to enforce individual fairness with respect to both outcome and cost, the authors also show that deploying a randomized classifier can fix this problem and provide algorithms that find an optimal but fair distribution over the thresholds by breaking the distribution $p(t)$ into piecewise constant functions. Results are extended to group fairness and experiments are provided on FICO and law school data sets.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n* The authors analysis includes a novel cost-based definition of individual fairness for strategic classification which requires $|C(x_1, f)-C(x_2,f)|<|x_1-x_2|$. This contrasts with the usual outcome based definitions of individual fairness.\n* The theory and methodology are evaluated in two real world settings (admissions and credit scoring) where individuals are likely to engage in strategic behavior.\n\n**Weaknesses**\n\n* The theory requires the following assumptions: (i) The existence of a function $l$ s.t. $Pr(Y=1|x) \\geq Pr(Y=1|z)$ iff $l(x) \\geq l(z)$. (ii) c(x,x') = g(l(x)-l(x')) for some increasing function $g$. (iii) The learner deploys classifier $f(x;t) = 1 [l(\\Delta(x(f)) ≥ t]$. (i) is reasonable for many applications but (iii) implicitly requires that the learner know $l$ which is a very strong assumption and (ii) requires that the cost be directly related to l(), but often the features that individuals can game are not the direct cause of the outcome of interest. For example, someones credit often does not directly cause a default in debt down the line. \n* Unless I missed it, the group fairness results seem to miss a theorem on the feasibility of the constraint (20).\n* The paper is very dense with derivations, including many inline definitions of constants and algebra steps making readability limited.\n\nQUESTIONS:\n* Is it unknown if the constraint (20) is feasible to enforce? Can the authors add more discussion on the use of their relatively strong assumptions?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-20"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper studies individual fairness in strategic classification, where individuals may modify their features to influence machine learning decisions. The authors demonstrate that deterministic threshold-based classifiers inherently violate individual fairness in such settings, particularly concerning the cost individuals incur to adjust their features. To address this, they propose randomized threshold classifiers, proving under certain distribution conditions that these classifiers can guarantee individual fairness while maintaining high accuracy. They design these classifiers as an efficient linear programming problem and extend the method to incorporate group fairness notions. Experiments on the FICO and Law School datasets show that the proposed method significantly improves individual fairness and group fairness simultaneously.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n- This study addresses the underexplored area of individual fairness.\n- It proves when individual fairness is impossible with deterministic thresholds and how to achieve it using randomized thresholds.\n- It presents a linear programming formulation for designing fair classifiers and extends the approach to incorporate group fairness simultaneously with individual fairness.\n- The approach is tested on two datasets with a detailed quantitative analysis.\n\nWeaknesses:\n- The approach relies on threshold-based classifiers and assumptions on l(x), which limits its applicability to broader ML settings.\n- The experiments are limited to 2 datasets and do not test sensitivity to hyperparameters such as the number of bins (k), which could generate computational challenges if larger values are required.\n\nQUESTIONS:\n- Given the method relies on threshold-based classifiers and specific assumptions on l(x), could the authors give an example of scenarios where these assumptions might not hold and discuss whether the method could be adapted to such cases?\n\n- How sensitive is the method’s performance (fairness vs. accuracy) to hyperparameters like the number of bins k. This could help to tune the method effectively.\n\n- Could the authors elaborate on the practical structure or interpretability of the learned threshold distribution p(t)? For example, does p(t) typically concentrate around certain thresholds, and how should this be interpreted in terms of fairness-accuracy trade-offs?\n\nVery nice work !\n\nLIMITATIONS:\nYes. The authors explicitly discuss limitations regarding reliance on threshold-based classifiers, assumptions on the existence of a score function l(x), small datasets, and simplex solver complexity.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 2, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper considers individually fair decision-making settings in which individuals can adjust their features (by paying some cost). Assuming that each individual will behave optimally, constraints for individual fairness are derived and a randomized classifier with maximal accuracy is constructed using linear programming. Evaluations on Law School and FICO datasets show that individual fairness can be provided by this approach with reasonable decreases in predictive performance.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n+ This paper identifies an important gap in fair machine learning research: individual fairness in strategic classification settings.\n+ The approach is novel, leveraging a distribution over linear threshold functions obtained using a linear program to provide individual fairness.\n+ The approach is versatile and can be combined with group fairness criteria, as the authors demonstrate.\n+ The experimental results show a good trade-off between predictive performance and providing individual fairness. \n\nWeaknesses:\n- The assumptions about the cost function (relating specifically to the linear threshold function) appear unnatural to me (see Q1), decreasing the general applicability of the approach to only work in settings with very strict assumptions.\n-  The framing of the paper seems a little strange to me. I believe individual fairness generally requires randomized decisions, so the focus on this aspect in the strategic setting is unnecessary (see Q2).\n- Related to the last point, since (I believe) randomization is essential for individual fairness, this suggests that better randomized baselines should be employed (Q3) rather than the deterministic classifier.\n\n----\n\nThere is a small typo in the Section 3 header.\n\nQUESTIONS:\nQ1. Why are the costs of feature adjustments assumed to be related to the l function in Eq. (7)? Is the method strongly dependent on this assumption (i.e., necessary for individual fairness, or computationally needed)? It seems that in real applications, the cost would simply be domain specific (and given) and generally unrelated to the prediction function. If given an arbitrary cost function, what could be done using your approach?\n\nQ2. It seems individual fairness requires randomized classifiers in general (i.e., in non-strategic settings). Why is there such a strong emphasis on this randomization in the paper? \n\nQ3. In (8), what does the left-hand side notation mean? Should (3) also match this notation?\n\nQ4. Are there smoother probability baselines (e.g., based on logistic regression) to compare against?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a method for individual fairness in with respect to varying response costs: interventions on some feature(s), e.g. recourse. Specifically, the authors define their method wrt the Best Response Cost. The authors give significant analytical results over this problem, specifically focusing on randomized classifiers.\n\nThe proposed method is clearly outlined in Algorithms 1 and 2. The authors evaluate their method on two real-world datasets, FICO and Law School. Overall, the method evaluates very well, and flexibly trades off between F1 and individual fairness (alternatively: group fairness).\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n\n1. Overall, this is a significant result in individual fairness under recourse. In several domains (e.g. financial), recourse is a legal requirement, and there is much concern about fairness implications (both group and individual) for these recourse responses.\n\n2. The model seems to evaluate quite well, and the evaluation methodology is strong. \n\n3. The model is quite flexible to both individual fairness and statistical parity regularization, L_c, Ω, respectively. This shows significant contribution to group fairness under recourse as well.\n\nWeaknesses\n\n1. The primary weakness seems to be the requirement for a randomized classifier class. While fair recourse is crucial in some domains (Strength #1), so is model *interpretability*. A randomized class of model seems less amenable to feature-attribution scores (e.g. with SHAP), and other governance requirements for these same domains. So in a sense, it seems one step forward (recourse), and one step back (governance). \n\n2. The class of base classifier here seems very restrictive. Assumption 2.1 assumes continuous and differentiable. But tree-based models tend to be ubiquitous in the domains relevant to recourse (e.g. finance). So, overall, what is the problem domain where the base model is differentiable, *and* recourse is of value, *and* interpretability isn't? \n\nWhile I think the result is significant in the IF literature, my doubt is whether the model addresses the paired limitations in strategic classification. \n\n(Small) Clarity: In the limitations, the authors note that the linear optimization problem with K variables is 2^ K. But in the evaluation section, the binning factor is K=80, 100. This seems(?) intractable. Are these different uses of K? \n\nDisclaimer: Some of the details around group fairness Eqs 18-22 I only understood intuitively. I defer the correctness of these to another reviewer. For these analytical results I'm primarily dropping my confidence.\n\nQUESTIONS:\nMostly from above:\n\n1. Could you please elaborate on feature-attribution and and interpretability aspects of randomized classifiers? Is there any way to use a randomized classifier class that is both interpretable and individually-fair wrt intervention costs?  \n\n2. Is there a non-diferentiable heuristic for Ack which handles other base model classes?\n\nLIMITATIONS:\nOverall, the authors address the model's computational limitations, and the broader potential impacts. The authors demonstrate group- and individual-fairness, which is a strength in the fairness literature; the authors might consider more elaboration of the conflict between groupwise and individual fairness to highlight that this method doesn't sacrifice one for the other.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 2, "CLARITY": 2, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-07-04"}