Demographic parity in regression and classification within the unawareness framework

• URL: http://arxiv.org/abs/2409.02471v1
• Date: Wed, 4 Sep 2024 06:43:17 GMT
• Title: Demographic parity in regression and classification within the unawareness framework
• Authors: Vincent Divol, Solenne Gaucher,
• Abstract summary: We characterize the optimal fair regression function when minimizing the quadratic loss. We also study the connection between optimal fair cost-sensitive classification, and optimal fair regression.
• Score: 8.057006406834466
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper explores the theoretical foundations of fair regression under the constraint of demographic parity within the unawareness framework, where disparate treatment is prohibited, extending existing results where such treatment is permitted. Specifically, we aim to characterize the optimal fair regression function when minimizing the quadratic loss. Our results reveal that this function is given by the solution to a barycenter problem with optimal transport costs. Additionally, we study the connection between optimal fair cost-sensitive classification, and optimal fair regression. We demonstrate that nestedness of the decision sets of the classifiers is both necessary and sufficient to establish a form of equivalence between classification and regression. Under this nestedness assumption, the optimal classifiers can be derived by applying thresholds to the optimal fair regression function; conversely, the optimal fair regression function is characterized by the family of cost-sensitive classifiers.

Related papers

• Provably Mitigating Overoptimization in RLHF: Your SFT Loss is Implicitly an Adversarial Regularizer [52.09480867526656]
  We identify the source of misalignment as a form of distributional shift and uncertainty in learning human preferences. To mitigate overoptimization, we first propose a theoretical algorithm that chooses the best policy for an adversarially chosen reward model. Using the equivalence between reward models and the corresponding optimal policy, the algorithm features a simple objective that combines a preference optimization loss and a supervised learning loss.
  arXiv  Detail & Related papers  (2024-05-26T05:38:50Z)
• Optimal Baseline Corrections for Off-Policy Contextual Bandits [61.740094604552475]
  We aim to learn decision policies that optimize an unbiased offline estimate of an online reward metric. We propose a single framework built on their equivalence in learning scenarios. Our framework enables us to characterize the variance-optimal unbiased estimator and provide a closed-form solution for it.
  arXiv  Detail & Related papers  (2024-05-09T12:52:22Z)
• Advancing Counterfactual Inference through Nonlinear Quantile Regression [77.28323341329461]
  We propose a framework for efficient and effective counterfactual inference implemented with neural networks. The proposed approach enhances the capacity to generalize estimated counterfactual outcomes to unseen data. Empirical results conducted on multiple datasets offer compelling support for our theoretical assertions.
  arXiv  Detail & Related papers  (2023-06-09T08:30:51Z)
• Variational Classification [51.2541371924591]
  We derive a variational objective to train the model, analogous to the evidence lower bound (ELBO) used to train variational auto-encoders. Treating inputs to the softmax layer as samples of a latent variable, our abstracted perspective reveals a potential inconsistency. We induce a chosen latent distribution, instead of the implicit assumption found in a standard softmax layer.
  arXiv  Detail & Related papers  (2023-05-17T17:47:19Z)
• Mean Parity Fair Regression in RKHS [43.98593032593897]
  We study the fair regression problem under the notion of Mean Parity (MP) fairness. We address this problem by leveraging reproducing kernel Hilbert space (RKHS) We derive a corresponding regression function that can be implemented efficiently and provides interpretable tradeoffs.
  arXiv  Detail & Related papers  (2023-02-21T02:44:50Z)
• Fair learning with Wasserstein barycenters for non-decomposable performance measures [8.508198765617198]
  We show that maximizing accuracy under the demographic parity constraint is equivalent to solving a corresponding regression problem. We extend this result to linear-fractional classification measures (e.g., $rm F$-score, AM measure, balanced accuracy, etc.)
  arXiv  Detail & Related papers  (2022-09-01T13:06:43Z)
• False Correlation Reduction for Offline Reinforcement Learning [115.11954432080749]
  We propose falSe COrrelation REduction (SCORE) for offline RL, a practically effective and theoretically provable algorithm. We empirically show that SCORE achieves the SoTA performance with 3.1x acceleration on various tasks in a standard benchmark (D4RL)
  arXiv  Detail & Related papers  (2021-10-24T15:34:03Z)
• Achieving Fairness with a Simple Ridge Penalty [0.0]
  We propose an alternative, more flexible approach to this task that enforces a user-defined level fairness constraint. Our proposal produces three limitations of the former approach.
  arXiv  Detail & Related papers  (2021-05-18T15:43:57Z)
• Addressing Fairness in Classification with a Model-Agnostic Multi-Objective Algorithm [33.145522561104464]
  The goal of fairness in classification is to learn a classifier that does not discriminate against groups of individuals based on sensitive attributes, such as race and gender. One approach to designing fair algorithms is to use relaxations of fairness notions as regularization terms. We leverage this property to define a differentiable relaxation that approximates fairness notions provably better than existing relaxations.
  arXiv  Detail & Related papers  (2020-09-09T17:40:24Z)
• Fair Regression with Wasserstein Barycenters [39.818025466204055]
  We study the problem of learning a real-valued function that satisfies the Demographic Parity constraint. It demands the distribution of the predicted output to be independent of the sensitive attribute. We establish a connection between fair regression and optimal transport theory, based on which we derive a close form expression for the optimal fair predictor.
  arXiv  Detail & Related papers  (2020-06-12T16:10:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.