DFL framework improves action and outcome fairness in policy learning.
problem Fairness in policy learning, especially action and outcome fairness.
method Integrates action and outcome fairness into a multi-objective optimization problem using a lexicographic weighted Tchebyshev method.
result DFL framework improves both action and outcome fairness with minimal value reduction.
Fairness measures fail in predictive settings that intentionally shift outcomes.
problem Fairness measures fail in performative prediction settings.
method Formalized concept shift and counterfactual outcomes.
result Predictors that are fair during training become unfair during deployment.
New research shows fairness in machine learning can sometimes make disadvantaged groups worse off.
problem The impact of fairness constraints in machine learning on different groups.
method Unified, population-level (Bayes) framework for binary classification under prevalent group fairness notions.
result Fairness in machine learning can lead to leveling down, making one or both groups worse off.
Paper introduces fair GLMs with convex penalty for equalizing GLM outcomes.
problem Achieving fairness in GLMs for practical use.
method Two fairness criteria based on GLM outcomes/log-likelihoods, achieved via a convex penalty on linear components.
result The fair GLM estimator is efficient and can handle various response variables.
We study notions of fairness in decision-making systems when individuals have diverse preferences over the possible outcomes of the decisions. Our starting point is the seminal work of Dwork et al. which introduced a notion of individual fairness (IF): given a task-specific similarity metric, every pair of individuals …
This paper introduces efficient approximations for fairness criteria in regression models.
problem Measuring fairness in real-valued outcomes (regression settings) is computationally challenging.
method Fast approximations of mutual information for independence, separation, and sufficiency fairness criteria.
result The method achieves state-of-the-art accuracy/fairness tradeoffs in real-world datasets.
Study fairness in intervention to maximize outcomes.
problem Fairness in intervention on a given node.
method Counterfactual estimation with partial causal model knowledge.
result Theoretical guarantees on error probability and effectiveness of algorithm.
In this paper, we consider the problem of fair statistical inference involving outcome variables. Examples include classification and regression problems, and estimating treatment effects in randomized trials or observational data. The issue of fairness arises in such problems where some covariates or treatments are "s…
The paper introduces a new algorithm for fair decision-making in outcome control tasks.
problem Fair and equitable automated decision-making in outcome control tasks.
method Causal analysis and optimization to ensure fairness in decision-making.
result Developed an algorithm for maximizing Y while ensuring causal fairness. Paper addresses fairness issues in error-prone outcomes.
problem Fairness in error-prone outcomes.
method Combining fair ML methods and measurement models.
result Using a latent variable model removes detected unfairness.
Approach collects missing outcomes to improve fairness in classification.
problem Lack of true outcomes for incorrectly classified samples leads to biased classifiers.
method Exploration-based data collection to ensure all subpopulations are represented and fairness properties are encoded.
result Trained classifier converges to a fair classifier with bounded false positives.
New approach to algorithmic fairness for human-AI collaboration considers compliance with human decisions.
problem Current fairness approaches assume perfect human compliance, but real-world compliance is often poor.
method Defines compliance-robustly fair algorithms and proposes an optimization strategy to improve fairness.
result Algorithmic recommendations can improve fairness even if humans do not fully comply with fair algorithms.
The adoption of automated, data-driven decision making in an ever expanding range of applications has raised concerns about its potential unfairness towards certain social groups. In this context, a number of recent studies have focused on defining, detecting, and removing unfairness from data-driven decision systems. …
New metric MADD assesses fairness of predictive student models.
problem Predictive student models can be biased and unfair, leading to discrimination.
method Proposes MADD metric to analyze model's discriminatory behaviors.
result Fair predictive performance does not guarantee fair behaviors or outcomes.
How do we learn from biased data? Historical datasets often reflect historical prejudices; sensitive or protected attributes may affect the observed treatments and outcomes. Classification algorithms tasked with predicting outcomes accurately from these datasets tend to replicate these biases. We advocate a causal mode…
Advocates focusing on utility functions to avoid unfair outcomes.
problem Unfair outcomes from fairness criteria in optimizing policies.
method Defines value of information fairness and proposes modifying utility functions.
result Value of information fairness leads to better answers than existing fairness notions.
Model shows partial compliance can lead to less fair outcomes than expected.
problem How partial compliance affects fairness in competitive markets.
method Simple model of employment market, simulation to explore effects.
result Partial compliance can lead to less fair outcomes than expected.
FADE framework improves fairness and accuracy in ensemble learning.
problem Improving fairness in existing models without sacrificing accuracy.
method Flexible fair ensemble learning framework targeting multiple fairness criteria.
result Multiple unfairness measures can be minimized simultaneously with little impact on accuracy.
Fair k-means algorithm ensures equitable costs for different groups.
problem K-means clustering can result in biased outcomes for subgroups of data.
method Presented a fair k-means objective and algorithm (Fair-Lloyd) to choose cluster centers that provide equitable costs for different groups.
result Fair-Lloyd algorithm ensures all groups have equal costs in the output k-clustering, with negligible increase in running time.
The paper critiques ε-fairness, showing it can lead to unfair outcomes and proposes a utility-based approach.
problem The limitations of probabilistic fairness metrics in real-world contexts.
method Utility-based approach to measure fairness, addressing the issue of unavailable data on false negatives.
result A utility-based approach uncovers necessary actions to achieve true fairness, contrasting with traditional probability-based evaluations.
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.
Now that machine learning algorithms lie at the center of many resource allocation pipelines, computer scientists have been unwittingly cast as partial social planners. Given this state of affairs, important questions follow. What is the relationship between fairness as defined by computer scientists and notions of soc…
New fairness notion helps identify fair auditors for evaluating decision-support systems.
problem Identifying fair auditors to evaluate decision-support systems for bias.
method Introducing a non-comparative fairness notion based on desired system properties.
result The proposed fairness notion provides guarantees in terms of comparative fairness.
Classifiers that achieve demographic balance by explicitly using protected attributes such as race or gender are often politically or culturally controversial due to their lack of individual fairness, i.e. individuals with similar qualifications will receive different outcomes. Individually and group fair decision crit…
This work shows how evaluation metrics can be seen as fair gambles.
problem The relationship and evaluation of machine learning forecasts.
method Using game-theoretic probability, the authors show evaluation metrics as fair gambles.
result Standard evaluation metrics are fair gambler outcomes, with calibration and regret metrics on two dimensions.
Consider a binary decision making process where a single machine learning classifier replaces a multitude of humans. We raise questions about the resulting loss of diversity in the decision making process. We study the potential benefits of using random classifier ensembles instead of a single classifier in the context…
Develops a method to ensure fairness across multiple sensitive attributes in machine learning.
problem Ensuring fairness among demographic groups formed by multiple sensitive attributes.
method Formulates intersectional fairness as a mutual information minimization problem and proposes a generic end-to-end algorithmic framework.
result Demonstrates effective debiasing of classification results with minimal impact to accuracy.
Optimization algorithms affect fairness in deep learning models, especially with adaptive methods like RMSProp.
problem The impact of optimization algorithms on fairness in deep learning models, particularly under imbalance.
method Stochastic differential equation analysis of optimization dynamics in an analytically tractable setup.
result RMSProp, an adaptive optimizer, converges to fairer minima than SGD under severe imbalance.
The paper proposes a method to ensure fairness in machine learning models.
problem Ensuring fairness in machine learning models powered by supervised learning.
method Optimal affine transport and Wasserstein-2 barycenter to characterize the Pareto frontier between prediction error and statistical disparity.
result The proposed method effectively balances prediction accuracy and fairness, as demonstrated by numerical simulations.
Machine learning based decision making systems are increasingly affecting humans. An individual can suffer an undesirable outcome under such decision making systems (e.g. denied credit) irrespective of whether the decision is fair or accurate. Individual recourse pertains to the problem of providing an actionable set o…
This study simulates biases in classifiers to assess fairness.
problem Mitigating biases in predictive models to ensure fairness.
method Agent-based model (ABM) to generate synthetic datasets with controlled biases, applied to offline and online learning approaches.
result Demonstrates how biases in data affect classifier outcomes and how mitigations impact feature usage.
The paper shows how to audit fairness in decisions with hidden risk factors.
problem Estimating fairness in decisions influenced by hidden, unobservable risk factors.
method Derives unbiased estimates of risk using historical data and audits existing decision-making systems.
result One can compute meaningful bounds on treatment rates for high-risk individuals, even with hidden confounders.
Recent work on fairness in machine learning has primarily emphasized how to define, quantify, and encourage "fair" outcomes. Less attention has been paid, however, to the ethical foundations which underlie such efforts. Among the ethical perspectives that should be taken into consideration is consequentialism, the posi…
The paper tackles fairness in data and algorithms, expanding on prior work.
problem Discrimination and disparate treatment in data and algorithms.
method Targeted learning for nonparametric inference of fairness in the data generating process.
result Derivation and validation of estimators for fairness metrics like demographic parity and equal opportunity.
Paper uses conformal prediction sets to make criminal justice risk assessments fairer.
problem Fairness issues in criminal justice risk assessment algorithms.
method Adopting conformal prediction sets to remove unfairness from algorithms and covariates.
result Constructs confusion tables and measures fairness effectively free of racial differences.
Machine learning algorithms are increasingly involved in sensitive decision-making process with adversarial implications on individuals. This paper presents mdfa, an approach that identifies the characteristics of the victims of a classifier's discrimination. We measure discrimination as a violation of multi-differenti…
Study examines impact of fairness penalties on clinical risk prediction models.
problem Widespread health disparities in machine learning-guided clinical decision-making.
method Empirical study across multiple databases, outcomes, and sensitive attributes.
result Penalizing fairness violations nearly universally degrades model performance and fairness metrics.
In this paper, we advocate for representation learning as the key to mitigating unfair prediction outcomes downstream. Motivated by a scenario where learned representations are used by third parties with unknown objectives, we propose and explore adversarial representation learning as a natural method of ensuring those…
New algorithm improves group fairness in social classification problems by exploiting performativity.
problem Inequities in social classification problems due to performativity.
method Develops algorithmic fairness practices that leverage performativity to achieve stronger group fairness guarantees.
result Achieves stronger group fairness guarantees compared to non-performative settings.
Online learning with one-sided feedback aims to maximize accuracy while ensuring fairness.
problem Maximizing accuracy in online learning with limited feedback and ensuring fairness.
method Extending the framework of Bechavod et al. (2020) to incorporate dynamic panels of auditors, reducing the problem to a contextual combinatorial semi-bandit, and leveraging Exp2 and Context-Semi-Bandit-FTPL algorithms.
result Multi-criteria no regret guarantees for accuracy and fairness are provided.
Study examines fairness in machine learning for credit scoring.
problem Bias in machine learning models for credit scoring.
method Comprehensive experimental study of fairness-aware machine learning models.
result Fairness-aware models improve fairness while maintaining accuracy.
The wide spread usage of automated data-driven decision support systems has raised a lot of concerns regarding accountability and fairness of the employed models in the absence of human supervision. Existing fairness-aware approaches tackle fairness as a batch learning problem and aim at learning a fair model which can…
Study reveals AI skin cancer classifiers underperform for darker skin phototypes, advocating for fairness auditing.
problem AI bias in dermatology, particularly for darker skin phototypes.
method Predictive Representativity (PR) framework, evaluating classifiers on HAM10000 and BOSQUE Test sets.
result Substantial performance disparities by skin phototype, highlighting AI bias.
Unified framework improves fair classification by selecting representative data points.
problem Improving fair classification outcomes in the presence of unintentional biases.
method Develops a unified framework to jointly optimize accuracy and fairness, recasting as mixed-integer convex programs.
result The framework can be used to enhance classification fairness by selecting more representative data points.
A novel multi-objective optimization framework improves insurance pricing fairness.
problem Exacerbated trade-offs between competing fairness criteria in insurance pricing using machine learning.
method Proposes a novel multi-objective optimization framework using NSGA-II to jointly optimize accuracy and fairness criteria.
result Consistently achieves a balanced compromise between accuracy and fairness, outperforming single-model approaches.
New algorithm makes machine learning fairer by removing bias from data.
problem Reduces bias in machine learning models through orthogonal data transformation.
method Orthogonal to Bias (OB) algorithm based on structural causal models.
result Promotes counterfactual fairness without sacrificing model accuracy.
Algorithm identifies intended fairness constraints from expert demonstrations for fair clustering.
problem Fair clustering challenges due to incomplete fairness constraints.
method Algorithm identifies fairness metric from expert demonstrations and generates clusters.
result Algorithm identifies and generates fair clusters from limited expert demonstrations.
Automates fairness and accuracy optimization in deep learning models for tabular data.
problem Improving fairness and accuracy in neural models for tabular data.
method Employed multi-objective Neural Architecture Search (NAS) and Hyperparameter Optimization (HPO) to find new models.
result Jointly optimized architectures that consistently outperform single-objective fairness mitigation methods.