Equality of Effort via Algorithmic Recourse

• URL: http://arxiv.org/abs/2211.11892v1
• Date: Mon, 21 Nov 2022 22:41:24 GMT
• Title: Equality of Effort via Algorithmic Recourse
• Authors: Francesca E. D. Raimondi, Andrew R. Lawrence, Hana Chockler
• Abstract summary: This paper proposes a method for measuring fairness through equality of effort by applying algorithmic recourse through minimal interventions. We extend the existing definition of equality of effort and present an algorithm for its assessment via algorithmic recourse.
• Score: 3.3517146652431378
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper proposes a method for measuring fairness through equality of effort by applying algorithmic recourse through minimal interventions. Equality of effort is a property that can be quantified at both the individual and the group level. It answers the counterfactual question: what is the minimal cost for a protected individual or the average minimal cost for a protected group of individuals to reverse the outcome computed by an automated system? Algorithmic recourse increases the flexibility and applicability of the notion of equal effort: it overcomes its previous limitations by reconciling multiple treatment variables, introducing feasibility and plausibility constraints, and integrating the actual relative costs of interventions. We extend the existing definition of equality of effort and present an algorithm for its assessment via algorithmic recourse. We validate our approach both on synthetic data and on the German credit dataset.

Related papers

• Optimal Multi-Fidelity Best-Arm Identification [65.23078799972188]
  In bandit best-arm identification, an algorithm is tasked with finding the arm with highest mean reward with a specified accuracy as fast as possible. We study multi-fidelity best-arm identification, in which the can choose to sample an arm at a lower fidelity (less accurate mean estimate) for a lower cost. Several methods have been proposed for tackling this problem, but their optimality remain elusive, notably due to loose lower bounds on the total cost needed to identify the best arm.
  arXiv  Detail & Related papers  (2024-06-05T08:02:40Z)
• Simple Steps to Success: Axiomatics of Distance-Based Algorithmic Recourse [13.207786673115296]
  We present Stepwise Explainable Paths (StEP), an axiomatically justified framework to compute direction-based algorithmic recourse. StEP offers provable privacy and robustness guarantees, and outperforms the state-of-the-art on several established recourse desiderata.
  arXiv  Detail & Related papers  (2023-06-27T15:35:22Z)
• Theoretically Principled Federated Learning for Balancing Privacy and Utility [61.03993520243198]
  We propose a general learning framework for the protection mechanisms that protects privacy via distorting model parameters. It can achieve personalized utility-privacy trade-off for each model parameter, on each client, at each communication round in federated learning.
  arXiv  Detail & Related papers  (2023-05-24T13:44:02Z)
• Stability is Stable: Connections between Replicability, Privacy, and Adaptive Generalization [26.4468964378511]
  A replicable algorithm gives the same output with high probability when its randomness is fixed. Using replicable algorithms for data analysis can facilitate the verification of published results. We establish new connections and separations between replicability and standard notions of algorithmic stability.
  arXiv  Detail & Related papers  (2023-03-22T21:35:50Z)
• Improved Policy Evaluation for Randomized Trials of Algorithmic Resource Allocation [54.72195809248172]
  We present a new estimator leveraging our proposed novel concept, that involves retrospective reshuffling of participants across experimental arms at the end of an RCT. We prove theoretically that such an estimator is more accurate than common estimators based on sample means.
  arXiv  Detail & Related papers  (2023-02-06T05:17:22Z)
• Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse [34.39887495671287]
  We propose an objective function which simultaneously minimizes the gap between the achieved (resulting) and desired recourse invalidation rates. We develop novel theoretical results to characterize the recourse invalidation rates corresponding to any given instance. Experimental evaluation with multiple real world datasets demonstrates the efficacy of the proposed framework.
  arXiv  Detail & Related papers  (2022-03-13T21:39:24Z)
• Instance-Dependent Confidence and Early Stopping for Reinforcement Learning [99.57168572237421]
  Various algorithms for reinforcement learning (RL) exhibit dramatic variation in their convergence rates as a function of problem structure. This research provides guarantees that explain textitex post the performance differences observed. A natural next step is to convert these theoretical guarantees into guidelines that are useful in practice.
  arXiv  Detail & Related papers  (2022-01-21T04:25:35Z)
• Test Score Algorithms for Budgeted Stochastic Utility Maximization [12.360522095604983]
  We extend an existing scoring mechanism, namely the replication test scores, to incorporate heterogeneous item costs as well as item values. Our algorithms and approximation guarantees assume that test scores are noisy estimates of certain expected values. We show how our algorithm can be adapted to the setting where items arrive in a fashion while maintaining the same approximation guarantee.
  arXiv  Detail & Related papers  (2020-12-30T15:28:41Z)
• CoinDICE: Off-Policy Confidence Interval Estimation [107.86876722777535]
  We study high-confidence behavior-agnostic off-policy evaluation in reinforcement learning. We show in a variety of benchmarks that the confidence interval estimates are tighter and more accurate than existing methods.
  arXiv  Detail & Related papers  (2020-10-22T12:39:11Z)
• Fair Policy Targeting [0.6091702876917281]
  One of the major concerns of targeting interventions on individuals in social welfare programs is discrimination. This paper addresses the question of the design of fair and efficient treatment allocation rules.
  arXiv  Detail & Related papers  (2020-05-25T20:45:25Z)
• Fair Correlation Clustering [92.15492066925977]
  We obtain approximation algorithms for correlation clustering under several important types of fairness constraints. We show that fair solutions to correlation clustering can be obtained with limited increase in cost compared to the state-of-the-art (unfair) algorithms.
  arXiv  Detail & Related papers  (2020-02-06T14:28:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.