Counterfactual fairness not equivalent to demographic parity, finds study.
problem Equivalence between causal and probabilistic concepts in fairness metrics.
method Close examination of recent claim about counterfactual fairness.
result Counterfactual fairness is not equivalent to demographic parity.
Proposes a framework to create fair IDRs by enforcing demographic parity constraints.
problem Discrimination in IDRs trained on biased data.
method Incorporates DP and CDP constraints into IDR estimation.
result Theoretically optimal IDRs can be efficiently obtained through perturbations.
New methods target conditional demographic parity using optimal transport distances.
problem Auditing and enforcing conditional demographic parity (CDP) in models with complex conditioning variables.
method Developed novel measures of conditional demographic disparity (CDD) based on optimal transport distances and regularization-based approaches.
result Validated methods airbit{} and airlp{} effectively target CDP in real-world datasets with continuous model outputs.
We find the optimal error for a constrained regression model under a linear model.
problem Minimizing error while adhering to demographic parity constraints.
method Proposed a minimax optimal error analysis for a demographic parity-constrained regression problem within a linear model.
result The minimax optimal error is characterized by $Θ( rac{dM}{n})$.
Algorithm ensures demographic parity in regression without sensitive attribute data.
problem Performing regression with demographic parity constraints.
method Post-processing algorithm using accurate estimates and sensitive attribute predictor.
result Generates predictions meeting demographic parity constraint.
New framework tackles fairness in link prediction beyond demographic parity.
problem Systemic biases in link prediction can exacerbate societal inequalities.
method Formalizes limitations of existing fairness evaluations and proposes a new framework.
result Proposes a lightweight post-processing method combined with decoupled link predictors.
Meta-theorems validate fair regression algorithms under demographic parity constraints.
problem Regression under demographic parity constraints.
method Meta-theorems and post-processing methods.
result Fair minimax optimal regression can be achieved through post-processing.
Integrates differential privacy and demographic parity in multi-class classification.
problem Ensuring fairness and privacy in sensitive applications.
method Designs DP2DP algorithm that enforces both demographic parity and differential privacy.
result DP2DP converges towards demographic parity at nearly the same rate as non-private methods, achieving state-of-the-art trade-offs.
New framework enforces demographic parity on distribution tails.
problem Enforcing demographic parity on entire distribution can degrade accuracy.
method Optimal transport theory, focusing on distribution tails.
result More nuanced and context-sensitive fairness interventions.
Unified framework for fair regression under demographic parity.
problem Ensuring fairness in regression tasks subject to demographic parity constraints.
method Proposes a unified framework applicable to various regression tasks with a broad spectrum of loss functions, derived a novel characterization of the fair risk minimizer, and established theoretical consistency and convergence rates.
result Effective minimization of risk while satisfying fairness constraints across various regression settings.
The paper explores fair regression and classification under demographic parity constraints.
problem Ensuring fairness in regression and classification models under demographic parity constraints.
method Characterizes the optimal fair regression function using a barycenter problem with optimal transport costs and studies the connection between fair classification and regression.
result The optimal fair regression function is derived from the solution to a barycenter problem with optimal transport costs, and the optimal fair cost-sensitive classifiers can be derived by applying thresholds to this function.
Decomposes bias in linear models under demographic parity constraints.
problem Understanding and quantifying bias in linear models under fairness constraints.
method Post-processing framework to decompose bias into direct and indirect components.
result Analytical characterization of how demographic parity reshapes model coefficients.
Extends Demographic Parity for fairer wage predictions with expert knowledge.
problem Inadequate fairness metrics limit user domain knowledge and ignore intersectional fairness.
method Develops a parametric method to incorporate expert knowledge in fair predictions.
result Offers a robust solution for real-life applications with limited data and spending constraints.
The paper tackles fair set-valued classification under demographic parity constraints.
problem Set-valued classification can amplify discriminatory bias, especially in multiclass settings.
method Proposes two strategies: an oracle-based method and a proxy method, both aiming to satisfy demographic parity and expected size constraints.
result Established distribution-free convergence rates and excess-risk bounds for both methods.
A non-trivial predictor satisfies demographic parity and equalizes group risks in regression.
problem Achieving fairness in regression models while maintaining equal risks across groups.
method Provided an explicit example of a non-constant predictor satisfying Demographic Parity and Equal Group-Wise Risks.
result First explicit construction of a non-constant predictor satisfying both fairness notions.
We relax demographic parity in regression by enforcing parity at quantile levels and score thresholds.
problem Enforcing full distributional fairness in regression can lead to substantial accuracy loss.
method Introduce (ℓ, Z)-fair predictor, derive closed-form solutions, and develop post-processing algorithm. result The risk gap to the continuous optimum vanishes as the grid is refined, and we enable targeted fairness corrections.
We mathematically compare four competing definitions of group-level nondiscrimination: demographic parity, equalized odds, predictive parity, and calibration. Using the theoretical framework of Friedler et al., we study the properties of each definition under various worldviews, which are assumptions about how, if at a…
We introduce the BriarPatch, a pixel-space intervention that obscures sensitive attributes from representations encoded in pre-trained classifiers. The patches encourage internal model representations not to encode sensitive information, which has the effect of pushing downstream predictors towards exhibiting demograph…
The study connects fairness constraints with optimal transport to derive new insights in classification.
problem Ensuring fairness in classification models without sacrificing performance.
method Using Wasserstein barycenters and optimal transport, the study characterizes optimal classification functions under fairness constraints.
result Maximizing fairness under demographic parity is equivalent to solving a regression problem.
We propose a novel algorithm for learning fair representations that can simultaneously mitigate two notions of disparity among different demographic subgroups in the classification setting. Two key components underpinning the design of our algorithm are balanced error rate and conditional alignment of representations. …
The study of fairness in intelligent decision systems has mostly ignored long-term influence on the underlying population. Yet fairness considerations (e.g. affirmative action) have often the implicit goal of achieving balance among groups within the population. The most basic notion of balance is eventual equality bet…
In this paper, we introduce the Fairness GAN, an approach for generating a dataset that is plausibly similar to a given multimedia dataset, but is more fair with respect to protected attributes in allocative decision making. We propose a novel auxiliary classifier GAN that strives for demographic parity or equality of …
Fair active learning selects data points to balance model accuracy and fairness.
problem Ensuring fairness in machine learning models used in high-stakes applications.
method Designing algorithms for fair active learning that select data points to balance model accuracy and fairness, focusing on demographic parity.
result Demonstrated the effectiveness of the proposed fair active learning approach over benchmark datasets.
Study efficient auditing of ML fairness models.
problem Scalability of auditing ML models for fairness.
method Query-based auditing algorithms for estimating demographic parity.
result Optimal deterministic and practical randomized algorithms for fairness estimation.
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.
The paper tackles fair representation learning by smoothing feature mappings.
problem Legal liability for discriminatory use of data by organizations.
method Mapping features to a fair representation space, certifying fairness through chi-squared mutual information.
result Smoothing representation distribution provides generalization guarantees of fairness and maintains accuracy for downstream tasks.
Unified framework for fair regression in aware and unaware settings.
problem Lack of principled methods for fair regression in unawareness settings.
method Formulated as an optimal transport problem, unifying aware and unaware settings.
result Characterizes optimal prediction functions via optimal transport maps under different penalties.
New algorithm ensures fairness without sacrificing accuracy.
problem Ensuring fairness in machine learning without harming accuracy.
method Demographic-Agnostic Fairness without Harm (DAFH) algorithm.
result DAFH algorithm achieves higher accuracy than existing methods.
Developing learning methods which do not discriminate subgroups in the population is a central goal of algorithmic fairness. One way to reach this goal is by modifying the data representation in order to meet certain fairness constraints. In this work we measure fairness according to demographic parity. This requires t…
This paper tackles fair Bayes-optimal classifiers under predictive parity, proving their limitations and proposing a new algorithm.
problem Ensuring fair Bayes-optimal classifiers under predictive parity, especially when group performance levels vary widely.
method Proving the limitations of fair Bayes-optimal classifiers under predictive parity and proposing a new adaptive thresholding algorithm, FairBayes-DPP.
result Fair Bayes-optimal classifiers under predictive parity may not hold if group performance levels vary widely, leading to within-group unfairness.
Medical imaging models may encode demographic attributes without violating fairness, depending on the approach.
problem Discrimination in medical imaging models due to encoding demographic attributes.
method Examined marginal and class-conditional representation invariance, traditional fairness notions, and counterfactual fairness.
result Demographically invariant models may not necessarily be fair, and encoding demographic attributes can be advantageous.
A new model shows fairness mechanisms can improve selection utility even without implicit bias.
problem Improving selection fairness without introducing a utility trade-off.
method A model with latent quality and group-dependent variance, comparing fairness mechanisms to group-oblivious selection.
result Demographic parity always increases selection utility, while γ-rules weakly increase it. Develops a method for fairness in multi-task learning using Wasserstein barycenters.
problem Extending fairness to multi-task learning with shared representations.
method Definition of Strong Demographic Parity extended to multi-task learning using multi-marginal Wasserstein barycenters. Closed form solution for optimal fair predictor.
result Empirical results show practical value of post-processing methodology in promoting fair decision-making.
Proposes a method to achieve quantile fairness in predictions.
problem Lack of research on quantile fairness in socially sensitive domains.
method Introduces a framework to learn a real-valued quantile function under Demographic Parity fairness.
result Demonstrates superior empirical performance and uncovering fairness-accuracy trade-offs.
The paper develops a method to achieve fairness in predictions using Wasserstein barycenters.
problem Learning a fair real-valued function independent of sensitive attributes.
method Establishing a connection between fair regression and optimal transport theory, deriving a close form expression for the optimal fair predictor as the Wasserstein barycenter of sensitive groups.
result The optimal fair predictor's distribution is the Wasserstein barycenter of sensitive groups' distributions, offering an intuitive interpretation and a simple post-processing algorithm.
New method for fair regression using optimal transport.
problem Learning fair regression models under counterfactual fairness constraints.
method Causal uncertainty view, optimal transport, post-processing method.
result High-probability fairness guarantees with O(n−1/3) decay. The paper proposes a method to improve fairness in machine learning models without refitting.
problem Mitigating biases in machine learning models that disadvantage certain groups.
method Infinitesimal jackknife-based approach to drop selected training data points.
result The intervention improves fairness without significantly reducing predictive performance.
This research simplifies verification of machine learning systems using reparameterization.
problem Reduce or eliminate serious bugs in machine learning systems.
method Use proof assistants to construct machine-checked proofs of correctness, leveraging reparameterization to handle probabilistic claims.
result Demonstrates broad applicability of reparameterization to verify different types of machine learning systems.
New method mitigates bias without sensitive data using causal graph and variational autoencoder.
problem Lack of fairness strategies when sensitive attributes are not collected.
method SRCVAE framework based on causal graph for inferring a proxy sensitive attribute.
result Significant improvements in fairness metrics over existing methods.
A new fairness metric for decision-making algorithms, conditioning on known fair variables.
problem Fairness issues in decision-making systems.
method Conditional fairness metric, Derivable Conditional Fairness Regularizer (DCFR), adversarial representation.
result Traditional fairness notations are special cases of the new conditional fairness notation.
Differentially private fair binary classification algorithm developed.
problem Balancing privacy and fairness in binary classification.
method Decoupling technique for fairness, refinement for differential privacy.
result Algorithm maintains fairness, privacy, and utility guarantees.
Proposes a method to create fair ITRs that balance value and fairness.
problem Fairness issues in ITRs that can lead to unfair advantages or disadvantages.
method Optimal transport theory to transform optimal ITRs into fair ITRs.
result Established a theoretical upper bound on value loss for improved trade-off ITRs.
This research tackles group fairness in predictive process monitoring by ensuring predictions are independent of sensitive group membership.
problem Predictive models using biased historical data can perpetuate unfair behavior in new cases.
method Investigates independence through metrics like ΔDP and a composite loss function balancing predictive performance and fairness.
result Proposes and validates a composite loss function for training models that balance fairness and performance.
The paper tackles fairness in machine learning by modeling latent unbiased labels.
problem Ensuring fairness in machine learning systems that use biased data.
method Explicitly models a latent variable representing a hidden, unbiased label to achieve demographic parity.
result The latent variable approach successfully retrieves fair labels from biased data.
It has recently been shown that if feedback effects of decisions are ignored, then imposing fairness constraints such as demographic parity or equality of opportunity can actually exacerbate unfairness. We propose to address this challenge by modeling feedback effects as Markov decision processes (MDPs). First, we prop…
Mitigates bias in weakly supervised datasets.
problem Bias in weakly supervised datasets.
method Proposes a counterfactual fairness-based technique to mitigate bias.
result Improves accuracy by up to 32% while reducing demographic parity gap by 82.5%.
Distillation affects some classes more than others, impacting fairness and bias.
problem Distillation affects some classes more than others, impacting fairness and bias.
method Examined class-wise accuracy and fairness metrics (DPD, EOD) on models trained with different datasets.
result Increasing the distillation temperature improves the distilled student model's fairness and individual fairness.
This work introduces oblivious fairness definitions for image generation.
problem Fairness in image generation with uncertain sensitive attributes.
method Introduces oblivious fairness definitions and uses Posterior Sampling.
result Conditional Proportional Representation can be achieved obliviously.