Standard
Explaining Multiple Instances Counterfactually:User Tests of Group-Counterfactuals for XAI. / Warren, Greta; Delaney, Eoin; Guéret, Christophe; Keane, Mark T.
Case-Based Reasoning Research and Development - 32nd International Conference, ICCBR 2024, Proceedings. red. / Juan A. Recio-Garcia; Mauricio G. Orozco-del-Castillo; Derek Bridge. Springer, 2024. s. 206-222 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 14775 LNAI).
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
Harvard
Warren, G, Delaney, E, Guéret, C & Keane, MT 2024,
Explaining Multiple Instances Counterfactually:User Tests of Group-Counterfactuals for XAI. i JA Recio-Garcia, MG Orozco-del-Castillo & D Bridge (red),
Case-Based Reasoning Research and Development - 32nd International Conference, ICCBR 2024, Proceedings. Springer, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), bind 14775 LNAI, s. 206-222, 32nd International Conference on Case-Based Reasoning, ICCBR 2024, Merida, Mexico,
01/07/2024.
https://doi.org/10.1007/978-3-031-63646-2_14
APA
Warren, G., Delaney, E., Guéret, C., & Keane, M. T. (2024).
Explaining Multiple Instances Counterfactually:User Tests of Group-Counterfactuals for XAI. I J. A. Recio-Garcia, M. G. Orozco-del-Castillo, & D. Bridge (red.),
Case-Based Reasoning Research and Development - 32nd International Conference, ICCBR 2024, Proceedings (s. 206-222). Springer. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Bind 14775 LNAI
https://doi.org/10.1007/978-3-031-63646-2_14
Vancouver
Warren G, Delaney E, Guéret C, Keane MT.
Explaining Multiple Instances Counterfactually:User Tests of Group-Counterfactuals for XAI. I Recio-Garcia JA, Orozco-del-Castillo MG, Bridge D, red., Case-Based Reasoning Research and Development - 32nd International Conference, ICCBR 2024, Proceedings. Springer. 2024. s. 206-222. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 14775 LNAI).
https://doi.org/10.1007/978-3-031-63646-2_14
Author
Warren, Greta ; Delaney, Eoin ; Guéret, Christophe ; Keane, Mark T. / Explaining Multiple Instances Counterfactually:User Tests of Group-Counterfactuals for XAI. Case-Based Reasoning Research and Development - 32nd International Conference, ICCBR 2024, Proceedings. red. / Juan A. Recio-Garcia ; Mauricio G. Orozco-del-Castillo ; Derek Bridge. Springer, 2024. s. 206-222 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 14775 LNAI).
Bibtex
@inproceedings{ffcc463ffda54599aa9ae59663e2af06,
title = "Explaining Multiple Instances Counterfactually:User Tests of Group-Counterfactuals for XAI",
abstract = "Counterfactual explanations have become a major focus for post-hoc explainability research in recent years, as they seem to provide good algorithmic recourse solutions, people can readily understand them, and they may meet legal regulations (such as GDPR in the EU). However, this large literature has only addressed the use of counterfactual explanations to explain single predictive-instances. Here, we explore a novel use case in which groups of similar instances are explained in a collective fashion using “group counterfactuals” (e.g., to highlight a repeating pattern of illness in a group of patients). Group counterfactuals potentially provide broad explanations covering multiple events/instances. A novel case-based, group-counterfactual algorithm is proposed to generate such explanations and a user study is also reported to test the psychological validity of the algorithm.",
keywords = "Counterfactuals, Explainability, User-Centered, XAI",
author = "Greta Warren and Eoin Delaney and Christophe Gu{\'e}ret and Keane, {Mark T.}",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.; 32nd International Conference on Case-Based Reasoning, ICCBR 2024 ; Conference date: 01-07-2024 Through 04-07-2024",
year = "2024",
doi = "10.1007/978-3-031-63646-2_14",
language = "English",
isbn = "9783031636455",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "206--222",
editor = "Recio-Garcia, {Juan A.} and Orozco-del-Castillo, {Mauricio G.} and Derek Bridge",
booktitle = "Case-Based Reasoning Research and Development - 32nd International Conference, ICCBR 2024, Proceedings",
address = "Switzerland",
}
RIS
TY - GEN
T1 - Explaining Multiple Instances Counterfactually:User Tests of Group-Counterfactuals for XAI
AU - Warren, Greta
AU - Delaney, Eoin
AU - Guéret, Christophe
AU - Keane, Mark T.
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - Counterfactual explanations have become a major focus for post-hoc explainability research in recent years, as they seem to provide good algorithmic recourse solutions, people can readily understand them, and they may meet legal regulations (such as GDPR in the EU). However, this large literature has only addressed the use of counterfactual explanations to explain single predictive-instances. Here, we explore a novel use case in which groups of similar instances are explained in a collective fashion using “group counterfactuals” (e.g., to highlight a repeating pattern of illness in a group of patients). Group counterfactuals potentially provide broad explanations covering multiple events/instances. A novel case-based, group-counterfactual algorithm is proposed to generate such explanations and a user study is also reported to test the psychological validity of the algorithm.
AB - Counterfactual explanations have become a major focus for post-hoc explainability research in recent years, as they seem to provide good algorithmic recourse solutions, people can readily understand them, and they may meet legal regulations (such as GDPR in the EU). However, this large literature has only addressed the use of counterfactual explanations to explain single predictive-instances. Here, we explore a novel use case in which groups of similar instances are explained in a collective fashion using “group counterfactuals” (e.g., to highlight a repeating pattern of illness in a group of patients). Group counterfactuals potentially provide broad explanations covering multiple events/instances. A novel case-based, group-counterfactual algorithm is proposed to generate such explanations and a user study is also reported to test the psychological validity of the algorithm.
KW - Counterfactuals
KW - Explainability
KW - User-Centered
KW - XAI
U2 - 10.1007/978-3-031-63646-2_14
DO - 10.1007/978-3-031-63646-2_14
M3 - Article in proceedings
AN - SCOPUS:85198402849
SN - 9783031636455
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 206
EP - 222
BT - Case-Based Reasoning Research and Development - 32nd International Conference, ICCBR 2024, Proceedings
A2 - Recio-Garcia, Juan A.
A2 - Orozco-del-Castillo, Mauricio G.
A2 - Bridge, Derek
PB - Springer
T2 - 32nd International Conference on Case-Based Reasoning, ICCBR 2024
Y2 - 1 July 2024 through 4 July 2024
ER -
