Fairness and Symbolic Regression

Understanding the impossibility of machine learning fairness with data examples

Machine learning has become increasingly prevalent in the world as a result of a combination of factors, notably computing breakthroughs and increased data availability. The three most common criteria of fairness in machine learning, despite their common sense and moral appeal, are often mutually exclusive. This frequently presents a challenge and the need to prioritize one criterion over the others. Specifically, the paper highlights the general overview of a machine learning model and presents an example of its application in the legal field that explores the existing biases in models today. We then delve into the three main criteria of machine learning fairness: independence (fairness based on outcomes being unrelated to different characteristics), separation (fairness based on equal error rates across groups), and sufficiency (fairness based on predictions being equally reliable for all groups). In the present experiment, we aimed to investigate the mutual exclusivity of these three machine-learning fairness criteria. We hypothesized that the fairness criteria being evaluated cannot all three be satisfied simultaneously, leading to a machine learning model that remains unfair. The results demonstrated that no threshold in the model simultaneously satisfied independence, separation, and sufficiency, highlighting the limitations of machine learning models that pose various issues across different sectors.

Bounding the Fairness of a Classifier Using Population-level Statistics

This work introduces a method to lower-bound the discrepancy of a classifier: a quantity that jointly captures inaccuracy and unfairness, and develops a computationally efficient procedure for calculating the tightest possible lower bound on the classifier’s discrepancy.

Algorithmic fairness as a human-technology interaction problem.

Algorithms shape high-stakes decisions across society. While promising efficiency, algorithms also raise fairness concerns. This article proposes that algorithmic fairness is best understood as a human-technology interaction problem rather than a purely technical challenge. Algorithms can reproduce human biases, amplify them through feedback loops, or create new forms of unfairness through objectives, proxies, and seemingly neutral variables. Yet they can make decision processes more explicit, disparities more visible, and actively mitigate discrimination. Fairness depends not only on statistical properties but also on how algorithms are designed, used and experienced by those affected by their decisions. This article therefore offers an interdisciplinary perspective that integrates insights from social justice, psychology, computer science, judgment and decision-making, and management.

Multi-Group Fairness Measures for Classification

A robust, reliable, and reproducible methodology for evaluating group fairness in classification algorithms, and provides a methodological approach for assessing fairness within the Sufficiency criterion by operationalizing Calibration.

Software Fairness Analysis and Repair via Causal Model-Guided Data Mutation

Federated learning with multiple, intersectional and multiclass fairness guarantees under performance budgets

Machine learning increasingly drives decisions in domains such as finance and healthcare, where ethical considerations, such as fairness, are central. In such contexts, ensuring fairness is essential, especially when decisions impact individuals and social groups. Federated learning (FL) provides a decentralized training paradigm, yet client heterogeneity and demographic imbalance can amplify disparities across subpopulations. Existing fairness-aware FL methods remain limited, often focusing on group fairness in binary classification and lacking explicit control over the trade-off between fairness and predictive performance. We introduce FedFairLAB, a FL method that enforces group, intersectional, and multiclass fairness simultaneously at both the local and global levels. A tunable performance budget allows practitioners to control how much predictive performance can be sacrificed to improve fairness. Experiments on six real-world datasets show that FedFairLAB substantially improves fairness while keeping models accurate and usable in realistic FL settings.