Explanations can help users of Artificial Intelligent (AI) systems
gain a better understanding of the reasoning behind the model’s
decision, facilitate their trust in AI, and assist them in making informed decisions. Due to its numerous benefits in improving how
users interact and collaborate with AI, this has stirred the AI/ML
community towards developing understandable or interpretable
models to a larger degree, while design researchers continue to
study and research ways to present explanations of these models’
decisions in a coherent form. However, there is still the lack of
intentional design effort from the HCI community around these explanation system designs. In this paper, we contribute a framework
to support the design and validation of explainable AI systems; one
that requires carefully thinking through design decisions at several
important decision points. This framework captures key aspects
of explanations ranging from target users, to the data, to the AI
models in use. We also discuss how we applied our framework to
design an explanation interface for trace link prediction of software
artifacts.
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No Explainability without Accountability: An Empirical Study of Explanations and Feedback in Interactive ML
Automatically generated explanations of how machine learning (ML) models reason can help users understand and accept them. However, explanations can have unintended consequences: promoting over-reliance or undermining trust. This paper investigates how explanations shape users' perceptions of ML models with or without the ability to provide feedback to them: (1) does revealing model flaws increase users' desire to "fix" them; (2) does providing explanations cause users to believe - wrongly - that models are introspective, and will thus improve over time. Through two controlled experiments - varying model quality - we show how the combination of explanations and user feedback impacted perceptions, such as frustration and expectations of model improvement. Explanations without opportunity for feedback were frustrating with a lower quality model, while interactions between explanation and feedback for the higher quality model suggest that detailed feedback should not be requested without explanation. Users expected model correction, regardless of whether they provided feedback or received explanations.
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- Award ID(s):
- 1409287
- NSF-PAR ID:
- 10212067
- Date Published:
- Journal Name:
- CHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
- Page Range / eLocation ID:
- 1 to 13
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3313831.3376624
