Develops algorithm to reduce real-world inequality.
problem Reduces inequality in real-world disparities.
method Impact remediation framework using social science insights and constrained optimization.
result Optimal intervention policies discovered to improve equity.
Differential privacy reduces model accuracy more for underrepresented groups.
problem Differential privacy impacts model accuracy differently across groups.
method Training neural networks with differential privacy (DP-SGD).
result DP-SGD reduces accuracy more for underrepresented groups.
This paper tackles fairness in PCA by balancing it with reconstruction error.
problem Fairness concerns in PCA due to different group representation errors.
method A multi-objective optimization approach to balance fairness and reconstruction error.
result Achieving fairness with minimal loss in reconstruction error.
Develops causal framework for fair survival analysis in healthcare.
problem Fairness in survival analysis for high-stakes domains like healthcare.
method Causal framework using graphical models, conditional survival function, and Causal Reduction Theorem.
result Decomposes disparities in survival into direct, indirect, and spurious pathways.
Study shows how algorithmic prediction affects US housing market, reducing racial wealth disparities.
problem Impact of algorithmic prediction on housing market and racial wealth disparities.
method Natural experiment using digitization of housing records to study entry, allocation, and prices.
result Digitization leads to increased sale prices for minority-owned homes, reducing racial wealth disparities.
Paper identifies reductive MDPs, solving them in polynomial time.
problem Computational hardness of general MDPs and tractability of finite-horizon MDPs.
method Defines reductivity, a new class of SSPs, and develops a polynomial-time solution.
result Optimal policies can be found in polynomial time for reductive SSPs and MDPs.
Study shows racial bias in health data, which can be reduced with simple techniques.
problem Racial bias in health indicators measured by the Medical Expenditure Panel Survey (MEPS).
method Used publicly available and nationally representative MEPS data to show bias in predictive models for care management.
result Racial bias can be significantly reduced using simple mitigation techniques.
A review of contrastive dimension reduction methods for treatment vs control studies.
problem Traditional dimension reduction techniques fail to isolate treatment-specific signals.
method Systematic overview and taxonomy of CDR methods.
result Unified framework for CDR methods and applications.
The paper studies and mitigates accuracy disparity in regression models.
problem Accuracy disparity between different demographic subgroups in high-stakes domains.
method Error decomposition theorem and distribution alignment algorithm.
result The proposed algorithm effectively mitigates accuracy disparity while maintaining predictive power.
Study shows unequal success of membership inference attacks across different groups.
problem Unequal success of membership inference attacks across different groups.
method Established conditions for preventing MIAs and derived connections to fairness and differential privacy.
result Estimating disparate vulnerability to MIAs can lead to overestimation; suitable attacks and statistical framework provided.
The paper offers methods for testing disparate impact using confidence intervals.
problem Testing for disparate impact in machine learning.
method Asymptotic distribution of indexes and use of confidence intervals.
result The importance of using confidence intervals over single values in testing disparate impact.
Develops methods for fair classification under linear disparity constraints.
problem Disparate impacts of machine learning algorithms on protected groups.
method Bayes-optimal fair classification methods via pre-, in-, and post-processing.
result Explicit forms of Bayes-optimal fair classifiers under linear disparity measures.
Study shows improving information content in risk scores can reduce algorithmic discrimination.
problem Algorithmic prediction systems may discriminate against underrepresented populations.
method Investigates the role of information in fair prediction, introduces refinement concept.
result Improving information content through refinements improves downstream selection rules and reduces disparity in treatment.
Following related work in law and policy, two notions of disparity have come to shape the study of fairness in algorithmic decision-making. Algorithms exhibit treatment disparity if they formally treat members of protected subgroups differently; algorithms exhibit impact disparity when outcomes differ across subgroups,…
Study decomposes racial healthcare disparities via shifts in mediator distributions.
problem Racial disparities in healthcare expenditures and their underlying drivers.
method Framework decomposing disparities into mediator distribution shifts and residual components, using MEPS data.
result Substantial disparities persist even when mediators are equalized, suggesting unmeasured or structural factors.
Study shows explanation disparities in machine learning models are influenced by data and model properties.
problem Disparities in post-hoc machine learning explanation methods across race and gender.
method Simulations and experiments on a real-world dataset to assess challenges to explanation disparities.
result Increased covariate shift, concept shift, and omission of covariates increase explanation disparities, especially for neural network models.
Bayesian model identifies health disparities in disease progression.
problem Health disparities bias disease progression models.
method Interpretable Bayesian model accounting for three disparities.
result Model identifies and corrects for health disparities.
Study shows label errors impact model disparity metrics, proposing mitigation methods.
problem Impact of label errors on model disparity metrics.
method Empirical study, characterizing label error effects; proposing estimation and relabeling methods.
result Label errors significantly affect model disparity metrics, particularly for minority groups.
Proposes a method to quantify and decompose disparity in ML models, separating exempt and non-exempt components.
problem Quantifying disparity in ML models, especially when certain features are exempted due to their critical importance.
method Information-theoretic decomposition into exempt and non-exempt components, satisfying desirable properties.
result Proposes a measure of non-exempt disparity that satisfies all desirable properties, and shows impossibility results for observational measures.
End-to-end framework learns precise disparity for activity recognition.
problem Precise portrayal of intraclass disparity in activity recognition.
method Knowledge-directed adversarial learning framework with two competitive encoding distributions.
result Robust and generalizable performance on HAR benchmark datasets.
Paper explores fair classification with bounded disparity using finite datasets.
problem Ensuring fairness in binary classification with protected groups.
method Minimax optimal approach with fairness constraints and demographic disparity control.
result Proposes FairBayes-DDP+ method that achieves minimax lower bound on fairness-aware excess risk.
Automated data-driven decision making systems are increasingly being used to assist, or even replace humans in many settings. These systems function by learning from historical decisions, often taken by humans. In order to maximize the utility of these systems (or, classifiers), their training involves minimizing the e…
The paper examines how adversarial robustness affects accuracy disparity across different classes.
problem Understanding the impact of adversarial robustness on accuracy disparity across different classes.
method Linear classifiers under a Gaussian mixture model, decomposing the impact into inherent and imbalance effects.
result Adversarial robustness consistently degrades standard accuracy in balanced classes, but the class imbalance ratio plays a different role in accuracy disparity.
The paper addresses fairness in machine learning by adjusting input distributions.
problem Reducing disparate impact in machine learning models over different groups.
method The approach involves learning a counterfactual distribution to adjust input variables for disadvantaged groups.
result The method can reduce disparate impact without training a new model.
Paper tackles fairness in CCA by minimizing correlation disparity error.
problem Fairness issues in CCA.
method Framework to minimize correlation disparity error in CCA.
result Reduces correlation disparity error without sacrificing CCA accuracy.
New method evaluates multiple social disparities using machine learning.
problem Reduction of educational disparities across multiple dimensions.
method Triply-Robust Machine Learning Approach for Causal Decomposition Analysis.
result Simultaneous interventions across multiple domains reduce disparities.
A fair PCA method using JEVD ensures balanced data representation.
problem PCA's bias in data with demographic characteristics.
method Joint Eigenvalue Decomposition (JEVD) for fair PCA.
result JEVD optimally balances fairness and PCA's data structure.
Study highlights fairness issues in travel behavior prediction models.
problem Ethical challenges in travel behavior analysis using machine learning.
method Operationalized computational fairness by equality of opportunity; compared DNN and DCM; introduced absolute correlation regularization.
result Both DNN and DCM predict disparities across social groups, with DNN outperforming DCM in prediction disparities.
The paper compares different fairness definitions under various worldviews.
problem Avoiding disparity amplification under different worldviews.
method Mathematical comparison of four fairness definitions using a theoretical framework.
result Different worldviews require different fairness definitions to avoid disparity amplification.
Conformal prediction sets can lead to unfair outcomes.
problem Disparate impact in decision-making with conformal prediction sets.
method Experiments with human participants to demonstrate disparate impact and propose equalizing set sizes across groups.
result Providing prediction sets that satisfy Equalized Coverage increases disparate impact compared to marginal coverage.
Linear dimensionality reduction methods are a cornerstone of analyzing high dimensional data, due to their simple geometric interpretations and typically attractive computational properties. These methods capture many data features of interest, such as covariance, dynamical structure, correlation between data sets, inp…
Paper tackles fairness in algorithms by predicting protected class from auxiliary data.
problem Protected class membership is often unobserved in data, leading to unfair algorithmic decisions.
method Use auxiliary datasets to predict protected class from proxy variables and provide characterizations of possible disparities.
result Common disparity measures are generally unidentifiable with auxiliary data, highlighting the need for robust assessments.
We combine multi-task learning and semi-supervised learning by inducing a joint embedding space between disparate label spaces and learning transfer functions between label embeddings, enabling us to jointly leverage unlabelled data and auxiliary, annotated datasets. We evaluate our approach on a variety of sequence cl…
The paper analyzes how deep neural networks handle noisy labels and finds disparate impacts.
problem Disparate impacts of noisy labels on instances with different representation frequencies.
method Quantifying harms, analyzing solutions, and comparing their impacts on different frequency instances.
result Existing solutions lead to disparate treatments, benefiting higher-frequency instances more.
DCEM algorithm reduces bias in machine learning models trained on selective labels.
problem Bias in machine learning models trained on selective labels.
method Disparate Censorship Expectation-Maximization (DCEM) algorithm.
result DCEM improves bias mitigation without sacrificing discriminative performance.
The paper introduces return parity for fairness in MDPs, addressing delayed and adverse effects.
problem Fairness in MDPs for dynamic domains with delayed and adverse effects.
method Proposes return parity, decomposes return disparity, and develops algorithms for state visitation distributional alignment.
result The proposed algorithms can successfully close the disparity gap while maintaining policy performance.
New method reduces privacy impact on model accuracy for underrepresented groups.
problem Privacy mechanisms disproportionately affect underrepresented groups in machine learning models.
method Proposes DPSGD-F, a modified DPSGD that adjusts group contributions based on clipping bias.
result DPSGD-F removes disparate impact of differential privacy on model accuracy for protected groups.
Deep chest X-ray classifiers show bias in predicting diagnoses.
problem Bias in deep learning classifiers predicting diagnoses from chest X-rays.
method Trained convolutional neural networks on multiple public datasets to predict 14 diagnostic labels.
result True positive rates vary significantly among different protected attributes, indicating bias.
New models reduce bias in machine learning for credit risk.
problem Bias in machine learning models for credit risk analysis.
method Sum Product Networks (SPNs) to identify and remove independent variables.
result Significant reduction in disparate treatment of male and female applicants.
AA extracts archetypes from data for clear feature extraction.
problem Non-convex optimization problem in AA.
method Computational procedure extracting archetypes as convex combinations of data.
result AA offers interpretable representations for high-dimensional data.
What does it mean for an algorithm to be biased? In U.S. law, unintentional bias is encoded via disparate impact, which occurs when a selection process has widely different outcomes for different groups, even as it appears to be neutral. This legal determination hinges on a definition of a protected class (ethnicity, g…
New method to quantify feature contributions to disparity without access to decision-making model.
problem Quantifying feature contributions to disparity when decision-making model is not accessible.
method Use information theory to measure redundant statistical dependency between protected attribute and feature.
result Quantify feature contributions to disparity using information theory.
Study evaluates when splitting classifiers can improve performance despite disparate treatment.
problem Impact of disparate treatment in classification models.
method Comparison of split classifiers and group-blind classifiers, quantifying performance improvement.
result Proves an equivalent expression for the benefit-of-splitting which can be efficiently computed.
Manifold matching works to identify embeddings of multiple disparate data spaces into the same low-dimensional space, where joint inference can be pursued. It is an enabling methodology for fusion and inference from multiple and massive disparate data sources. In this paper we focus on a method called Canonical Correla…
Recent work has shown that optical flow estimation can be formulated as a supervised learning task and can be successfully solved with convolutional networks. Training of the so-called FlowNet was enabled by a large synthetically generated dataset. The present paper extends the concept of optical flow estimation via co…
Study federates measurement of demographic disparities from quantile sketches.
problem Misalignment of fairness goals with siloed data collection and privacy regulations.
method Federated auditing of demographic parity through score distributions, using Wasserstein--Frechet variance and quantile summaries.
result Proposes a one-shot, communication-efficient protocol to estimate global disparity and its decomposition.
Study shows significant differences in recommendation bias between model-based and memory-based algorithms.
problem Recommendation bias disparity across different algorithms and item categories.
method Examined bias disparity in a range of collaborative recommendation algorithms and item categories.
result Significant differences found between model-based and memory-based algorithms.
Develops a new criterion for subgroup fairness in algorithmic decision support.
problem Identifying fair recommendations in algorithms despite group-level differences.
method IJDI criterion and IJDI-Scan approach to detect and mitigate disparities.
result Identifies significant disparities in recommendations across subpopulations.