End-to-End Fairness Optimization with Fair Decision-Focused Learning

• URL: http://arxiv.org/abs/2607.29441v1
• Date: Fri, 31 Jul 2026 14:07:43 GMT
• Title: End-to-End Fairness Optimization with Fair Decision-Focused Learning
• Abstract summary: We introduce end-to-end fairness optimization (E2EFO) as a unifying framework that integrates fairness across the prediction-to-decision pipeline.<n>Within this framework, we propose a training paradigm that jointly accounts for prediction accuracy, prediction fairness and decision regret.
• Score: 20.41775720778342
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Many real-world systems rely on predictive models to inform decisions, and fairness concerns arise in both the prediction and decision stages. We introduce end-to-end fairness optimization (E2EFO) as a unifying framework that integrates fairness across the prediction-to-decision pipeline. We focus on resource allocation with group-based fairness: the prediction task estimates allocation impacts while limiting accuracy disparity across groups, and the decision task distributes those impacts equitably by optimizing a group-based alpha-fairness measure. Within this framework, we propose fair decision-focused learning (FDFL), a training paradigm that jointly accounts for prediction accuracy, prediction fairness, and decision regret -- the loss in decision fairness due to imperfect predictions. FDFL trains the predictor by gradient descent, combining the objective gradients through multi-task learning techniques. The core computational challenge is the decision Jacobian with respect to the predictor parameters: we derive exact closed-form formulas for a tractable class of fair allocation and apply a differentiable optimization layer in the general case. We further establish a finite-sample generalization bound for the scalarized FDFL objective. Numerical experiments on a healthcare-based single resource allocation and a synthetic multiple resource allocation illustrate the value of jointly accounting for prediction fairness and decision fairness in prediction-informed decision-making.

Related papers

• Fairness vs Performance: Characterizing the Pareto Frontier of Algorithmic Decision Systems [0.0]
  Design of fair algorithmic decision systems requires balancing model performance with fairness toward affected individuals.<n>We investigate fairness in binary prediction-based decision problems by conceptualizing decision making as a multi-objective optimization problem.
  arXiv  Detail & Related papers  (2026-05-11T14:04:27Z)
• Possibilistic Predictive Uncertainty for Deep Learning [75.00607359322863]
  We introduce Dirichlet-approximated possibilistic posterior predictions (DAPPr)<n>DAPPr is a principled framework leveraging possibility theory.<n>This projection-and-approximation strategy yields a simple training objective with closed-form solutions.
  arXiv  Detail & Related papers  (2026-05-01T12:14:01Z)
• Fair regression under localized demographic parity constraints [9.082079838093371]
  We introduce a novel ($ell$, Z)-fair predictor, which imposes groupwise CDF constraints of the form F f |S=s (z m ) = $ell$ m for prescribed pairs.<n> Experiments on synthetic and real datasets illustrate an interpretable fairness-accuracy trade-off, enabling targeted corrections at decision-relevant quantiles or thresholds.
  arXiv  Detail & Related papers  (2026-03-26T09:21:05Z)
• The Statistical Fairness-Accuracy Frontier [50.323024516295725]
  Machine learning models must balance accuracy and fairness, but these goals often conflict.<n>A useful tool for understanding this trade-off is the fairness-accuracy frontier, which characterizes the set of models that cannot be simultaneously improved in both fairness and accuracy.<n>We study the FA frontier in the finite-sample regime, showing how it deviates from its population counterpart and quantifying the worst-case gap between them.
  arXiv  Detail & Related papers  (2025-08-25T03:01:35Z)
• Online Decision-Focused Learning [74.3205104323777]
  Decision-focused learning (DFL) is an increasingly popular paradigm for training models whose predictive outputs are used in decision-making tasks.<n>In this paper, we regularize the objective function to make it different and investigate how to overcome nonoptimality function.<n>We also showcase the effectiveness of our algorithms on a knapsack experiment, where they outperform two standard benchmarks.
  arXiv  Detail & Related papers  (2025-05-19T10:40:30Z)
• Decision-centric fairness: Evaluation and optimization for resource allocation problems [1.5623752145311105]
  We propose a decision-centric fairness methodology that induces fairness only within the decision-making region.<n>We empirically compare our approach to a global fairness approach on multiple (semi-synthetic) datasets.
  arXiv  Detail & Related papers  (2025-04-29T11:12:36Z)
• Identifying and Mitigating Social Bias Knowledge in Language Models [52.52955281662332]
  We propose a novel debiasing approach, Fairness Stamp (FAST), which enables fine-grained calibration of individual social biases.<n>FAST surpasses state-of-the-art baselines with superior debiasing performance.<n>This highlights the potential of fine-grained debiasing strategies to achieve fairness in large language models.
  arXiv  Detail & Related papers  (2024-08-07T17:14:58Z)
• Boosting Fair Classifier Generalization through Adaptive Priority Reweighing [59.801444556074394]
  A performance-promising fair algorithm with better generalizability is needed. This paper proposes a novel adaptive reweighing method to eliminate the impact of the distribution shifts between training and test data on model generalizability.
  arXiv  Detail & Related papers  (2023-09-15T13:04:55Z)
• Federated Conformal Predictors for Distributed Uncertainty Quantification [83.50609351513886]
  Conformal prediction is emerging as a popular paradigm for providing rigorous uncertainty quantification in machine learning. In this paper, we extend conformal prediction to the federated learning setting. We propose a weaker notion of partial exchangeability, better suited to the FL setting, and use it to develop the Federated Conformal Prediction framework.
  arXiv  Detail & Related papers  (2023-05-27T19:57:27Z)
• Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach [66.9033666087719]
  This paper extends the inference view and describes a variational inference formulation of federated learning. We apply FedEP on standard federated learning benchmarks and find that it outperforms strong baselines in terms of both convergence speed and accuracy.
  arXiv  Detail & Related papers  (2023-02-08T17:58:11Z)
• The Sharpe predictor for fairness in machine learning [0.0]
  In machine learning applications, unfair predictions may discriminate against a minority group. Most existing approaches for fair machine learning (FML) treat fairness as a constraint or a penalization term in the optimization of a ML model. We introduce a new paradigm for FML based on Multi-Objective Optimization (SMOO), where accuracy and fairness metrics stand as conflicting objectives to be optimized simultaneously. The Sharpe predictor for FML provides the highest prediction return (accuracy) per unit of prediction risk (unfairness).
  arXiv  Detail & Related papers  (2021-08-13T22:22:34Z)
• Accuracy and Fairness Trade-offs in Machine Learning: A Stochastic Multi-Objective Approach [0.0]
  In the application of machine learning to real-life decision-making systems, the prediction outcomes might discriminate against people with sensitive attributes, leading to unfairness. The commonly used strategy in fair machine learning is to include fairness as a constraint or a penalization term in the minimization of the prediction loss. In this paper, we introduce a new approach to handle fairness by formulating a multi-objective optimization problem.
  arXiv  Detail & Related papers  (2020-08-03T18:51:24Z)

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.