Human Cognitive Biases in Explanation-based Interaction: The Case of Within and Between Session Order Effect
Dario Pesenti, Alessandro Bogani, Katya Tentori, Stefano Teso
摘要
Explanatory Interactive Learning (XIL) is a powerful interactive learning framework designed to enable users to customize and correct AI models by interacting with their explanations. In a nutshell, XIL algorithms select a number of items on which an AI model made a decision (e.g. images and their tags) and present them to users, together with corresponding explanations (e.g. image regions that drive the model's decision). Then, users supply corrective feedback for the explanations, which the algorithm uses to improve the model. Despite showing promise in debugging tasks, recent studies have raised concerns that explanatory interaction may trigger order effects, a well-known cognitive bias in which the sequence of presented items influences users' trust and, critically, the quality of their feedback. We argue that these studies are not entirely conclusive, as the experimental designs and tasks employed differ substantially from common XIL use cases, complicating interpretation. To clarify the interplay between order effects and explanatory interaction, we ran two larger-scale user studies (n = 713 total) designed to mimic common XIL tasks. Specifically, we assessed order effects both within and between debugging sessions by manipulating the order in which correct and wrong explanations are presented to participants. Order effects had a limited, though significant, impact on users' agreement with the model (i.e., a behavioral measure of their trust), and only when examined within debugging sessions, not between them. The quality of users' feedback was generally satisfactory, with order effects exerting only a small and inconsistent influence both within and between sessions. Overall, our findings suggest that order effects do not pose a significant issue for the successful employment of XIL approaches. More broadly, our work contributes to the ongoing efforts for understanding human factors in AI. 1 1 Our study has received approval from the Ethics board of our university.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了最后一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
它引用的顶会 Paper4
- Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-makingCharvi Rastogi, Yunfeng Zhang, Dennis Wei, Kush R. Varshney 等CSCW 2022 · 被引用 184 次
- Interactive Label Cleaning with Example-based ExplanationsStefano Teso, Andrea Bontempelli, Fausto Giunchiglia, Andrea PasseriniNeurIPS 2021 · 被引用 59 次
- FIND: Human-in-the-Loop Debugging Deep Text ClassifiersPiyawat Lertvittayakumjorn, Lucia Specia, Francesca ToniEMNLP 2020 · 被引用 33 次
- Right for the Right Concept: Revising Neuro-Symbolic Concepts by Interacting With Their ExplanationsWolfgang Stammer, Patrick Schramowski, Kristian KerstingCVPR 2021
相关 Paper
- Explainable Active Learning (XAL): Toward AI Explanations as Interfaces for Machine TeachersBhavya Ghai, Q. Vera Liao, Yunfeng Zhang, Rachel K. E. Bellamy 等CSCW 2020 · 被引用 107 次
- On Selective, Mutable and Dialogic XAI: a Review of What Users Say about Different Types of Interactive ExplanationsAstrid Bertrand, Tiphaine Viard, Rafik Belloum, James R. Eagan 等CHI 2023 · 被引用 53 次
- XAIR: A Framework of Explainable AI in Augmented RealityXuhai Xu, Anna Yu, Tanya R. Jonker, Kashyap Todi 等CHI 2023 · 被引用 73 次
- Incremental XAI: Memorable Understanding of AI with Incremental ExplanationsJessica Y. Bo, Pan Hao, Brian Y. LimCHI 2024 · 被引用 19 次
- How can Explainability Methods be Used to Support Bug Identification in Computer Vision Models?Agathe Balayn, Natasa Rikalo, Christoph Lofi, Jie Yang 等CHI 2022 · 被引用 22 次
