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On the Fairness of Causal Algorithmic Recourse
[article]
2022
arXiv
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Algorithmic fairness is typically studied from the perspective of predictions. ...
We study theoretically and empirically how to enforce fair causal recourse by altering the classifier and perform a case study on the Adult dataset. ...
Acknowledgements We are grateful to Chris Russell for insightful feedback on connections to existing fairness notions within machine learning and philosophy. ...
arXiv:2010.06529v5
fatcat:mrd5yjey55gvzknxuwvtmgc4ru
A survey of algorithmic recourse:contrastive explanations and consequential recommendations
2022
ACM Computing Surveys
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In this work, we focus on algorithmic recourse , which is concerned with providing explanations and recommendations to individuals who are unfavorably treated by automated decision-making systems. ...
We first perform an extensive literature review, and align the efforts of many authors by presenting unified definitions , formulations , and solutions to recourse. ...
AHK is also appreciative of Julius von Kügelgen and Umang Bhatt for fruitful discussions on recourse and fairness, Muhammad Waleed Gondal and the anonymous reviewers for constructive feedback throughout ...
doi:10.1145/3527848
fatcat:fo6dntzsvnbfdhwz7bmsgcgb54
A survey of algorithmic recourse: definitions, formulations, solutions, and prospects
[article]
2020
arXiv
pre-print
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In this work, we focus on algorithmic recourse, which is concerned with providing explanations and recommendations to individuals who are unfavourably treated by automated decision-making systems. ...
We first perform an extensive literature review, and align the efforts of many authors by presenting unified definitions, formulations, and solutions to recourse. ...
This is the concern of algorithmic recourse. Contributions: Our review brings together the plethora of recent works on algorithmic recourse. ...
arXiv:2010.04050v1
fatcat:zsrqbickgvd4bbhnle4nlk7buq
On the Adversarial Robustness of Causal Algorithmic Recourse
[article]
2021
arXiv
pre-print
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In order to shift part of the burden of robustness from the decision-subject to the decision-maker, we propose a model regularizer that encourages the additional cost of seeking robust recourse to be low ...
Recourse recommendations should ideally be robust to reasonably small uncertainty in the features of the individual seeking recourse. ...
On the fairness of causal algorithmic recourse. ICML 2021 Workshop on
Algorithmic Recourse, 2021.
[40] Sandra Wachter, Brent Mittelstadt, and Chris Russell. ...
arXiv:2112.11313v1
fatcat:ko4gnhopijduhihzmli5iha2mi
Algorithmic Recourse in Partially and Fully Confounded Settings Through Bounding Counterfactual Effects
[article]
2021
arXiv
pre-print
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If the lower bound is above a certain threshold, i.e., on the other side of the decision boundary, recourse is guaranteed in expectation. ...
The proposed approach only requires specification of the causal graph and confounding structure and bounds the expected counterfactual effect of recourse actions. ...
This work was supported by the German Federal Ministry of Education and Research (BMBF): Tübingen AI Center, FKZ: 01IS18039B; and by the Machine Learning Cluster of Excellence, EXC number 2064/1 -Project ...
arXiv:2106.11849v1
fatcat:fcbkijcym5ci3jeebveuq4qlmm
A Causal Perspective on Meaningful and Robust Algorithmic Recourse
[article]
2021
arXiv
pre-print
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Additionally, we introduce a relaxation of MAR called effective algorithmic recourse (EAR), which, under certain assumptions, yields meaningful recourse by only allowing interventions on causes of the ...
Extending the work of Karimi et al. (2021), we propose meaningful algorithmic recourse (MAR) that only recommends actions that improve both prediction and target. ...
We take a causal perspective on the issue at hand and argue that robustness and meaningfulness are related problems ICML (International Conference on Machine Learning) Workshop on Algorithmic Recourse. ...
arXiv:2107.07853v1
fatcat:dic2ilqtuvh37igrmfppkqmb7y
Explaining Black-Box Algorithms Using Probabilistic Contrastive Counterfactuals
[article]
2021
arXiv
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Experiments on synthetic data further demonstrate the correctness of LEWIS's explanations and the scalability of its recourse algorithm. ...
knowledge of the underlying causal model and (3)makes no assumptions about the internals of an algorithmic system except for the availability of its input-output data. ...
Algorithmic fairness. The critical role of causality and background knowledge is recognized and acknowledged in the algorithmic fairness literature [45, 43, 65, 76, 25, 79, 81, 78] . ...
arXiv:2103.11972v2
fatcat:vh6f4f2kvfezrf6i26a2cflqpa
Algorithmic Recourse: from Counterfactual Explanations to Interventions
[article]
2020
arXiv
pre-print
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2020 pre-print arXiv:2002.06278v3 fatcat:hjinex45lnaoxmxbewytt46cyiOther Versions
In this work, we rely on causal reasoning to caution against the use of counterfactual explanations as a recommendable set of actions for recourse. ...
Existing works have primarily focused on designing algorithms to obtain counterfactual explanations for a wide range of settings. ...
Acknowledgments and Disclosure of Funding The authors would like to thank Adrián Javaloy Bornás and Julius von Kügelgen for their valuable feedback on drafts of the manuscript. ...
arXiv:2002.06278v4
fatcat:eanijkx2kza5zbysp5bnidn7zm
Multi-Agent Algorithmic Recourse
[article]
2021
arXiv
pre-print
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Past work has largely focused on the effect algorithmic recourse has on a single agent. ...
In this work, we show that when the assumption of a single agent environment is relaxed, current approaches to algorithmic recourse fail to guarantee certain ethically desirable properties. ...
fair way. ...
arXiv:2110.00673v1
fatcat:ib6ojpe7afa7zaqraqjvchttte
Counterfactual Explanations Can Be Manipulated
[article]
2021
arXiv
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We perform experiments on loan and violent crime prediction data sets where certain subgroups achieve up to 20x lower cost recourse under the perturbation. ...
We describe how these models can unfairly provide low-cost recourse for specific subgroups in the data while appearing fair to auditors. ...
The views expressed are those of the authors and do not reflect the official policy or position of the funding agencies. ...
arXiv:2106.02666v2
fatcat:twqf4amos5bo5p2npmft4z2pvi
Algorithmic recourse under imperfect causal knowledge: a probabilistic approach
[article]
2020
arXiv
pre-print
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Recent work has discussed the limitations of counterfactual explanations to recommend actions for algorithmic recourse, and argued for the need of taking causal relationships between features into consideration ...
The second removes any assumptions on the structural equations by instead computing the average effect of recourse actions on individuals similar to the person who seeks recourse, leading to a novel subpopulation-based ...
Moreover, a special thanks to Adrià Garriga-Alonso for insightful input on some of the GP-derivations and to Adrián Javaloy Bornás for invaluable help with the CVAE-training. ...
arXiv:2006.06831v3
fatcat:cxklr77ghbhzxhgjonsgpngy24
Amortized Generation of Sequential Algorithmic Recourses for Black-box Models
[article]
2021
arXiv
pre-print
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Algorithmic Recourses (ARs) provide "what if" feedback of the form "if an input datapoint were x' instead of x, then an ML-based system's output would be y' instead of y." ...
We evaluate our approach using three real-world datasets and show successful generation of sequential ARs that respect other recourse desiderata. ...
Results Table 3 shows the performance of FASTAR and all the baselines on the recourse desiderata. ...
arXiv:2106.03962v2
fatcat:535mjkbur5arragdefsh5so2ve
Causal Learning for Socially Responsible AI
[article]
2022
arXiv
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One of these approaches is causal learning (CL). We survey state-of-the-art methods of CL for SRAI. ...
We begin by examining the seven CL tools to enhance the social responsibility of AI, then review how existing works have succeeded using these tools to tackle issues in developing SRAI such as fairness ...
Acknowledgements This material is based upon work supported by, or in part by, the U.S. Army Research Laboratory and the U.S. ...
arXiv:2104.12278v2
fatcat:hk42masibjai7pnv5y2t7gj7ja
Algorithmic Recourse in the Wild: Understanding the Impact of Data and Model Shifts
[article]
2021
arXiv
pre-print
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Our theoretical results establish a lower bound on the probability of recourse invalidation due to model shifts, and show the existence of a tradeoff between this invalidation probability and typical notions ...
of "cost" minimized by modern recourse generation algorithms. ...
Conclusion In this paper, we analyse the impact of distribution shifts on recourses generated by state-of-the-art algorithms. ...
arXiv:2012.11788v3
fatcat:ilfoacj7ufhppb3mkgdmn6defe
Learning Models for Actionable Recourse
[article]
2022
arXiv
pre-print
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We demonstrate the efficacy of our approach via extensive experiments on real data. ...
., an applicant denied a loan) with recourse -- i.e., a description of how they can change their features to obtain a positive outcome. ...
The views expressed are those of the authors and do not reflect the official policy or position of the funding agencies. ...
arXiv:2011.06146v3
fatcat:xe5mjazbczfwnkouuzhmjf5l5e
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