The paper introduces DPVar, a fairness metric that measures the variance of the conditional mean prediction to directly optimize mean demographic parity in nonlinear regression. It presents two solvers—FBO, using a closed‑form hypergradient, and ITD, which differentiates through the conditional‑mean estimator updates—to handle the resulting functional bilevel problem. Experiments on 21 tabular datasets and the Communities & Crime data show that these methods achieve competitive or superior accuracy‑fairness trade‑offs compared to existing approaches.
arXiv:2607. 05098v1 Announce Type: new Abstract: When sensitive attributes are continuous and high-dimensional $-$ demographic score vectors, posteriors over attributes, age or income profiles $-$ enforcing full statistical independence is often too restrictive, and existing relaxations rely on indirect dependence penalties or adversarial schemes that do not directly target the fairness-accuracy trade-off.
The paper introduces a unified framework for relaxed fair regression that applies to both aware and unaware settings. By modeling regression with a demographic parity penalty as an optimal transport problem, it characterizes optimal prediction functions under squared Wasserstein‑2 and Total Variation penalties, revealing distinct fairness philosophies. The authors also present FAUST, an efficient algorithm that consistently matches or surpasses state‑of‑the‑art baselines on real‑world benchmarks.
arXiv:2610.32516v1 Announce Type: cross Abstract: Learned representations often encode sensitive attributes, such as gender or race, that may be undesirable or unsafe to retain. Concept erasure aims...
FairSplit decomposes the embedding space into a Fair Space for prediction and a Sensitive Space that isolates sensitive information, using a learnable mask and the Hilbert‑Schmidt Independence Criterion (HSIC) to guide the separation. The method simultaneously trains for classification accuracy and independence from sensitive attributes, providing theoretical bounds on the demographic parity gap based on the Fair Space dimensionality and HSIC. It supports multiclass tasks and non‑binary sensitive attributes, achieving competitive accuracy‑fairness trade‑offs on established benchmarks.
The paper introduces a targeted learning approach to assess fairness in the data generating process used for training predictive models, rather than focusing solely on model performance. It derives estimators for demographic parity, equal opportunity, and conditional mutual information, and demonstrates through simulations that these estimators improve confidence‑interval coverage and reduce false detections of population unfairness compared to fixed‑model inference. The authors also apply the method to real data to illustrate its practical utility.
arXiv:2605. 05882v2 Announce Type: replace-cross Abstract: Artificial-intelligence systems are becoming ubiquitous in society, yet their predictions typically inherit biases with respect to protected attributes such as race, gender, or age.
The paper introduces a control‑variable framework for deep neural networks to mitigate omitted variable bias, particularly shortcut learning where covariates like demographics influence predictions. It refits the final layer of a pre‑trained network using cross‑fitting with ridge penalisation, orthogonalises covariate effects, and marginalises predictions over covariate distributions to achieve unbiased, interpretable results. Experiments on simulated images and neuroimaging data show consistent estimation of true effects and performance close to models trained on unconfounded data.
arXiv:2606. 20461v1 Announce Type: new Abstract: Machine learning models have been shown to exhibit discriminatory outcomes or degraded performance for individuals at the intersection of multiple sensitive attributes, such as race and gender.
arXiv:2608.24582v1 Announce Type: cross Abstract: Credit risk models increasingly need to combine predictive accuracy with transparent explanations and auditable fairness constraints. Logistic regres...
arXiv:2604. 16610v2 Announce Type: replace-cross Abstract: Machine learning models often inherit biases from historical data, raising critical concerns about fairness and accountability.
arXiv:2605. 28626v2 Announce Type: replace Abstract: Hybrid interpretable models combine a transparent component with a black-box model by assigning some examples to the former and deferring the rest to the latter.
arXiv:2603.05575v2 Announce Type: replace-cross Abstract: We study prediction-powered conditional inference in the setting where labeled data are scarce, unlabeled covariates are abundant, and a blac...