Data-Centric Explanations: Explaining Training Data of Machine Learning Systems to Promote Transparency

要旨

Training datasets fundamentally impact the performance of machine learning (ML) systems. Any biases introduced during training (implicit or explicit) are often reflected in the system’s behaviors leading to questions about fairness and loss of trust in the system. Yet, information on training data is rarely communicated to stakeholders. In this work, we explore the concept of data-centric explanations for ML systems that describe the training data to end-users. Through a formative study, we investigate the potential utility of such an approach, including the information about training data that participants find most compelling. In a second study, we investigate reactions to our explanations across four different system scenarios. Our results suggest that data-centric explanations have the potential to impact how users judge the trustworthiness of a system and to assist users in assessing fairness. We discuss the implications of our findings for designing explanations to support users’ perceptions of ML systems.

著者
Ariful Islam Anik
University of Manitoba, Winnipeg, Manitoba, Canada
Andrea Bunt
University of Manitoba, Winnipeg, Manitoba, Canada
DOI

10.1145/3411764.3445736

論文URL

https://doi.org/10.1145/3411764.3445736

動画

会議: CHI 2021

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2021.acm.org/)

セッション: Computational AI Development and Explanation

[B] Paper Room 02, 2021-05-14 01:00:00~2021-05-14 03:00:00 / [C] Paper Room 02, 2021-05-14 09:00:00~2021-05-14 11:00:00 / [A] Paper Room 02, 2021-05-13 17:00:00~2021-05-13 19:00:00
Paper Room 02
12 件の発表
2021-05-14 01:00:00
2021-05-14 03:00:00
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