Counterfactual Explanations with Probabilistic Guarantees on their Robustness to Model Change

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3A10511661" target="_blank" >RIV/00216208:11320/25:10511661 - isvavai.cz</a>

Výsledek na webu

<a href="https://dl.acm.org/doi/10.1145/3690624.3709300" target="_blank" >https://dl.acm.org/doi/10.1145/3690624.3709300</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3690624.3709300" target="_blank" >10.1145/3690624.3709300</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Counterfactual Explanations with Probabilistic Guarantees on their Robustness to Model Change

Popis výsledku v původním jazyce

Counterfactual explanations (CFEs) guide users on how to adjust inputs to machine learning models to achieve desired outputs. While existing research primarily addresses static scenarios, real-world applications often involve data or model changes, potentially invalidating previously generated CFEs and rendering user-induced input changes ineffective. Current methods addressing this issue often support only specific models or change types, require extensive hyperparameter tuning, or fail to provide probabilistic guarantees on CFE robustness to model changes. This paper proposes a novel approach for generating CFEs that provides probabilistic guarantees for any model and change type, while offering interpretable and easy-to-select hyperparameters. We establish a theoretical framework for probabilistically defining robustness to model change and demonstrate how our BetaRCE method directly stems from it. BetaRCE is a post-hoc method applied alongside a chosen base CFE generation method to enhance the qua

Název v anglickém jazyce

Counterfactual Explanations with Probabilistic Guarantees on their Robustness to Model Change

Popis výsledku anglicky

Counterfactual explanations (CFEs) guide users on how to adjust inputs to machine learning models to achieve desired outputs. While existing research primarily addresses static scenarios, real-world applications often involve data or model changes, potentially invalidating previously generated CFEs and rendering user-induced input changes ineffective. Current methods addressing this issue often support only specific models or change types, require extensive hyperparameter tuning, or fail to provide probabilistic guarantees on CFE robustness to model changes. This paper proposes a novel approach for generating CFEs that provides probabilistic guarantees for any model and change type, while offering interpretable and easy-to-select hyperparameters. We establish a theoretical framework for probabilistically defining robustness to model change and demonstrate how our BetaRCE method directly stems from it. BetaRCE is a post-hoc method applied alongside a chosen base CFE generation method to enhance the qua

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

KDD &apos;25: Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining

ISBN

979-8-4007-1245-6

ISSN

—

e-ISSN

—

Počet stran výsledku

12

Strana od-do

1277-1288

Název nakladatele

Association for Computing Machinery

Místo vydání

New York, NY, USA

Místo konání akce

Toronto, Canada

Datum konání akce

3. 8. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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