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.
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.
Extract fairness policies from legal documents using machine learning.
problem Extract fairness policies from legal documents for AI applications.
method Two approaches based on semantic relatedness: Wordnet-based similarity and vector-based similarity.
result Vector-based similarity outperforms classical Wordnet-based similarity.
New framework optimizes rankings for fairness in various applications.
problem Ranking systems can unfairly prioritize certain items over others.
method Proposes a general LTR framework that optimizes fairness constraints while maximizing utility.
result Demonstrates effectiveness in individual and group-fairness settings.
Algorithm learns fair ranking from biased data.
problem Unfair ranking policies from biased implicit feedback.
method Policy-gradient approach with amortized fairness constraints.
result Efficient algorithm FULTR learns fair policies.
FEN model learns fairness and efficiency in multi-agent systems.
problem Fairness in multi-agent systems for stability and productivity.
method Hierarchical reinforcement learning model with fair-efficient reward and sub-policies.
result FEN model easily learns both fairness and efficiency in multi-agent scenarios.
Proposes fair and robust methods for estimating treatment effects.
problem Estimating treatment effects while maintaining fairness.
method Simple, nonparametric framework with fairness constraints.
result Estimators are double robust and characterize welfare trade-offs.
The paper addresses fairness in dynamic pricing for strategic buyers.
problem Price disparities among specific groups can lead to unfair perceptions and legal violations.
method Proposes a dynamic pricing policy that achieves fairness and discourages strategic behavior.
result Achieves an upper bound of O ( T + H ( T ) ) O(\sqrt{T}+H(T)) O ( T + H ( T )) regret over T T T time horizons, reducing regret by 35.06% compared to a benchmark policy. Paper develops a fair pricing algorithm for dynamic settings with uncertain demand.
problem Fair pricing in dynamic, uncertain demand scenarios.
method Contextual bandit algorithm with dynamic pricing and demand learning.
result Achieves optimal regret bound with fairness constraints.
The paper tackles fair policy targeting by optimizing allocation rules to minimize unfairness.
problem Discrimination in individualized treatments of social welfare programs.
method Formulated as a mixed-integer linear program, solved using off-the-shelf algorithms, derived regret bounds and small sample guarantees.
result Designs fair and efficient treatment allocation rules within the Pareto frontier.
A fair policy for hiring candidates from different groups is proposed in a linear contextual bandit problem.
problem Selecting candidates from different sensitive groups in a fair manner.
method A greedy policy that constructs a ridge regression estimate and computes relative rank using empirical cumulative distribution function.
result The greedy policy achieves fair pseudo-regret of order d T \sqrt{dT} d T after T T T rounds, satisfying demographic parity. Doubly fair dynamic pricing ensures equal prices for different groups over time.
problem Achieving equal prices for different groups in online dynamic pricing.
method Online learning algorithm that balances procedural and substantive fairness.
result Achieves i l d e O ( T ) ilde{O}(\sqrt{T}) i l d e O ( T ) regret, zero procedural unfairness, and i l d e O ( T ) ilde{O}(\sqrt{T}) i l d e O ( T ) substantive unfairness. PyCFRL helps ensure fair reinforcement learning policies from offline data.
problem Ensuring fairness in reinforcement learning policies for disadvantaged groups.
method Sequential data preprocessing to learn counterfactually fair policies.
result PyCFRL implements a novel algorithm for fair RL policy learning.
Recent work in fairness in machine learning has proposed adjusting for fairness by equalizing accuracy metrics across groups and has also studied how datasets affected by historical prejudices may lead to unfair decision policies. We connect these lines of work and study the residual unfairness that arises when a fairn…
The paper proposes a fair reinforcement learning framework to prevent healthcare disparities.
problem Unfair reinforcement learning policies in healthcare can lead to socioeconomically-disadvantaged subgroups being underprivileged.
method The paper introduces a counterfactual fairness framework and a sequential data preprocessing algorithm to achieve fair sequential decision making.
result The proposed approach greatly enhances fair access to counseling in a digital health dataset designed to reduce opioid misuse.
The paper tackles fair decision-making by correcting biases in data.
problem Discriminatory biases in data and decision-making perpetuate injustice.
method Causal inference and constrained optimization to learn fair policies.
result The approach ensures fair policies that satisfy given constraints.
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.
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.
Paper proposes using unlabeled data for fair decision-making.
problem Bias in decision-making algorithms due to biased labels and selective labeling.
method Variational autoencoder for learning unbiased data representations from both labeled and unlabeled data.
result Method learns fair and stable decision policies with high utility.
Paper proposes OPF policy for fair resource allocation with sublinear regret.
problem Fair resource allocation in an online setting against an unrestricted adversary.
method Online Proportional Fair (OPF) policy achieving approximate sublinear regret.
result OPF policy achieves c α c_α c α -approximate sublinear regret with c α ≤ 1.445 c_α \leq 1.445 c α ≤ 1.445 . Efficiently audits model fairness with continuous monitoring and flexible data collection.
problem Continuous monitoring and flexible data collection for fairness auditing.
method Sequential, anytime-valid inference and game-theoretic statistics.
result Demonstrated efficacy on three fairness datasets.
The paper tackles fair sharing of exploration costs across groups in online learning.
problem Sharing the cost of exploration fairly across multiple groups in online learning.
method The paper introduces the 'grouped' bandit model and uses axiomatic bargaining theory, specifically the Nash bargaining solution, to formalize fairness.
result The paper derives policies that are optimally fair and regret-optimal, showing that regret-optimal policies can be unfair.
DQ4FairIM uses RL to maximize influence while ensuring fairness across all groups.
problem Fairness in influence maximization in social networks.
method Fairness-aware deep RL method using Structure2Vec network embedding.
result DQ4FairIM achieves higher fairness than fairness-agnostic and fairness-aware baselines.
A new framework for fair unemployment benefits using game theory.
problem Designing fair and sustainable unemployment benefits.
method Cooperative game theory and real-time fiscal policy.
result A fair, debt-free, and asymptotically risk-free payroll tax rule.
New algorithm for fair ranking in contextual bandits with concave rewards.
problem Fair ranking in recommendation systems.
method Geometric interpretation of CBCR as optimization, Frank-Wolfe analyses.
result First algorithm with provably vanishing regret for CBCR.
Fairness in algorithmic decision-making processes is attracting increasing concern. When an algorithm is applied to human-related decision-making an estimator solely optimizing its predictive power can learn biases on the existing data, which motivates us the notion of fairness in machine learning. while several differ…
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.
The paper examines how slightly biasing towards under-represented groups in sequential selection processes can lead to long-term fairness.
problem Designing fair sequential decision-making processes for long-term social fairness.
method Proposes Multi-agent Fair-Greedy policy to balance score maximization and fairness.
result Proves convergence to long-term fairness target set by agents when score distributions are identical.
Introduces principal fairness for fair decision-making.
problem Discrimination among similarly affected individuals.
method Uses principal stratification from causal inference.
result Explicitly accounts for decision impacts, not just protected attributes.
The paper explores how fairness policies can lead to social equality in decision-making.
problem Fairness in decision-making systems and its long-term effects on the population.
method A model that combines selection process and dynamics of group qualifications, focusing on demographic parity.
result Unconstrained fairness policies may not lead to equality, and imposing demographic parity can worsen qualifications in some cases.
Unfair pricing policies have been shown to be one of the most negative perceptions customers can have concerning pricing, and may result in long-term losses for a company. Despite the fact that dynamic pricing models help companies maximize revenue, fairness and equality should be taken into account in order to avoid u…
This paper addresses dynamic price discrimination with fairness constraints.
problem Dynamic price discrimination with fairness constraints in online retailing.
method Nonparametric demand models, dynamic pricing policy, regret minimization.
result Optimal dynamic pricing policy with i l d e O ( T 4 / 5 ) ilde{O}(T^{4/5}) i l d e O ( T 4/5 ) regret for price fairness. New algorithms ensure fairness in sequential decisions, accounting for feedback effects.
problem Ignoring feedback effects can lead to unfair outcomes in sequential decision-making.
method Model feedback effects as MDPs and propose fair properties and algorithms.
result Demonstrated the necessity of considering dynamical effects for fairness.
EgalMAB solves fair resource allocation in stochastic bandits.
problem Fair resource allocation in a stochastic multi-armed bandit setting.
method Design and analysis of UCB-based policy EgalUCB.
result Established upper bounds on cumulative regret.
New model for fair clustering ensures balanced representation of protected attributes.
problem Ensuring fair representation in clustering for protected attributes.
method Model-based formulation of fair clustering, balancing protected attributes across clusters.
result Demonstrates improved fairness in clustering through a new model.
Libra ensures fair order-matching in electronic financial exchanges.
problem Technical shortcomings and infrastructure complexities in electronic trading.
method Formally defined temporal fairness, evaluated existing fair market designs, introduced Libra.
result Libra is more robust and resilient to technical manipulation than existing designs.
Study explores fairness in loan decisions using dynamic modeling.
problem Fairness constraints do not always benefit disadvantaged groups.
method Continuous population state representation using Beta distribution; model of population dynamics under lending decisions.
result Optimal lender behavior can lead to unfair outcomes, but common fairness constraints cause convergence to the same equilibrium.
The paper explores fair machine learning policies for balancing competing objectives in noisy data.
problem Balancing competing objectives in noisy data.
method Analyzes a class of policies that trace an empirical Pareto frontier based on learned scores.
result Characterizes optimal strategies and bounds Pareto errors due to score inaccuracies.
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.
Causal models help ensure fairness in systems with changing environments.
problem Ensuring fairness in systems with dynamic, long-term effects.
method Causal directed acyclic graphs (DAGs) to model fairness and manipulate causal assumptions.
result Causal assumptions enable simulation and off-policy estimation of interventions.
The paper tackles fair decision-making with imperfect labels.
problem Predictive models learn from biased data due to selective labeling.
method Proposes learning decision policies that maximize utility under fairness constraints.
result Learning to decide improves fairness and utility compared to traditional risk minimization.
Paper proposes fair ML predictors to avoid discrimination.
problem Discrimination in ML predictors from historical data.
method Proposes two algorithms to adjust ML predictors for fairness.
result Proves fair EO and AA predictors are optimal in performance.
The paper analyzes how employers can efficiently screen candidates using multiple tests, considering both skill estimation and fairness.
problem How to efficiently screen candidates using multiple noisy signals without violating fairness.
method The paper extends traditional screening models to a multi-test setting, analyzing optimal employer policies for both fixed and dynamic test assignments.
result A fundamental impossibility emerges when noise levels vary across groups, making it impossible to administer the same number of tests and maintain the same outcomes.
New method corrects bias in recommendation systems for diverse user groups.
problem Bias in recommendation systems due to MNAR data.
method Counterfactual Robust Risk Minimization (CRRM) framework.
result Empirical validation of CRRM's superiority in fairness and generalization.
Graph neural networks optimize radio resource management policies for wireless networks.
problem Optimizing user selection and power control in wireless networks with fairness constraints.
method Formulated as a Lagrangian dual problem, RRM policies are parameterized by a GNN architecture trained on channel conditions.
result The method achieves superior tradeoff between average and 5th percentile rates, demonstrating fairness.
Paper connects fair machine learning to political philosophy, highlighting flaws in ideal approaches.
problem Lack of natural formulation for social desiderata in machine learning.
method Proposes metrics and algorithms to satisfy subsets of fairness parities, trading off against utility.
result Misguided fair machine learning algorithms reflect broader flaws in ideal methodological approaches.
Study optimal and equitable encouragement policies for treatment adherence.
problem Optimal treatment adherence policies in the presence of human non-adherence.
method Covariate-conditional no-direct-effect model of encouragement; tractable policy characterizations under constraints.
result Induced treatment take-up is the fairness target, not recommendation rates.
Study optimizes health incentives to balance efficiency and fairness.
problem Designing health incentives to balance efficiency and fairness.
method Inverse behavioral optimization framework integrating QALY-based incentives and adaptive learning.
result Modern health systems operate near an efficiency-saturated frontier, with small fairness adjustments yielding diminishing returns.