On the Interaction of Belief Bias and Explanations
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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On the Interaction of Belief Bias and Explanations. / González, Ana Valeria; Rogers, Anna; Søgaard, Anders.
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021. Online : Association for Computational Linguistics, 2021. s. 2930-2942.Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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TY - GEN
T1 - On the Interaction of Belief Bias and Explanations
AU - González, Ana Valeria
AU - Rogers, Anna
AU - Søgaard, Anders
PY - 2021/8/1
Y1 - 2021/8/1
N2 - A myriad of explainability methods have been proposed in recent years, but there is little consensus on how to evaluate them. While automatic metrics allow for quick enchmarking,it isn’t clear how such metrics reflect human interaction with explanations. Human evaluation is of paramount importance, but previous protocols fail to account for belief biases affecting human performance, which may lead to misleading conclusions. We provide an overview of belief bias, its role in human evaluation, and ideas for NLP practitioners on how to account for it. For t o experimental paradigms, we present a case study of gradientbased explainability ntroducing simple ways to account for humans’ prior beliefs: models of varying quality and adversarial examples. We show that conclusions about the highest performing methods change when introducing such controls, pointing to the importance of accounting for belief bias in evaluation.1 Int
AB - A myriad of explainability methods have been proposed in recent years, but there is little consensus on how to evaluate them. While automatic metrics allow for quick enchmarking,it isn’t clear how such metrics reflect human interaction with explanations. Human evaluation is of paramount importance, but previous protocols fail to account for belief biases affecting human performance, which may lead to misleading conclusions. We provide an overview of belief bias, its role in human evaluation, and ideas for NLP practitioners on how to account for it. For t o experimental paradigms, we present a case study of gradientbased explainability ntroducing simple ways to account for humans’ prior beliefs: models of varying quality and adversarial examples. We show that conclusions about the highest performing methods change when introducing such controls, pointing to the importance of accounting for belief bias in evaluation.1 Int
U2 - 10.18653/v1/2021.findings-acl.259
DO - 10.18653/v1/2021.findings-acl.259
M3 - Article in proceedings
SP - 2930
EP - 2942
BT - Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021
PB - Association for Computational Linguistics
CY - Online
T2 - Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL-IJCNLP 2021
Y2 - 1 August 2021 through 6 August 2021
ER -
ID: 285387796