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Authors: Riccardo Crupi 1 ; Beatriz San Miguel González 2 ; Alessandro Castelnovo 1 and Daniele Regoli 1

Affiliations: 1 Intesa Sanpaolo S.p.A., Turin, Italy ; 2 Fujitsu Research of Europe, Madrid, Spain

Keyword(s): Explainable Artificial Intelligence, Counterfactual Explanations, Causality, Recourse.

Abstract: Over the last years, there has been a growing debate on the ethical issues of Artificial Intelligence (AI). Explainable Artificial Intelligence (XAI) has appeared as a key element to enhance trust of AI systems from both technological and human-understandable perspectives. In this sense, counterfactual explanations are becoming a de facto solution for end users to assist them in acting to achieve a desired outcome. In this paper, we present a new method called Counterfactual Explanations as Interventions in Latent Space (CEILS) to generate explanations focused on the production of feasible user actions. The main features of CEILS are: it takes into account the underlying causal relations by design, and can be set on top of an arbitrary counterfactual explanation generator. We demonstrate how CEILS succeeds through its evaluation on a real dataset of the financial domain.

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Paper citation in several formats:
Crupi, R.; San Miguel González, B.; Castelnovo, A. and Regoli, D. (2022). Leveraging Causal Relations to Provide Counterfactual Explanations and Feasible Recommendations to End Users. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-547-0; ISSN 2184-433X, SciTePress, pages 24-32. DOI: 10.5220/0010761500003116

@conference{icaart22,
author={Riccardo Crupi. and Beatriz {San Miguel González}. and Alessandro Castelnovo. and Daniele Regoli.},
title={Leveraging Causal Relations to Provide Counterfactual Explanations and Feasible Recommendations to End Users},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2022},
pages={24-32},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010761500003116},
isbn={978-989-758-547-0},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - Leveraging Causal Relations to Provide Counterfactual Explanations and Feasible Recommendations to End Users
SN - 978-989-758-547-0
IS - 2184-433X
AU - Crupi, R.
AU - San Miguel González, B.
AU - Castelnovo, A.
AU - Regoli, D.
PY - 2022
SP - 24
EP - 32
DO - 10.5220/0010761500003116
PB - SciTePress

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