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Building human-machine trust via interpretability

Published: 27 January 2019 Publication History

Abstract

Developing human-machine trust is a prerequisite for adoption of machine learning systems in decision critical settings (e.g healthcare and governance). Users develop appropriate trust in these systems when they understand how the systems make their decisions. Interpretability not only helps users understand what a system learns but also helps users contest that system to align with their intuition. We propose an algorithm, AVA: Aggregate Valuation of Antecedents, that generates a consensus feature attribution, retrieving local explanations and capturing global patterns learned by a model. Our empirical results show that AVA rivals current benchmarks.

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Lundberg, S. M., and Lee, S.-I. 2017. A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems 30. 4765–4774.
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Narodytska, N., and Walsh, T. 2014. The computational impact of partial votes on strategic voting. In ECAI.
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Negahban, S.; Oh, S.; and Shah, D. 2012. Iterative ranking from pair-wise comparisons. In Advances in Neural Information Processing Systems 25. 2474–2482.
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Ribeiro, M. T.; Singh, S.; and Guestrin, C. 2016. Why should I trust you?: Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD, 1135–1144.
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Sundararajan, M.; Taly, A.; and Yan, Q. 2017. Axiomatic attribution for deep networks. In Proceedings of the 34th International Conference on Machine Learning, volume 70.

Cited By

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  • (2024)Impact of Model Interpretability and Outcome Feedback on Trust in AIProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642780(1-25)Online publication date: 11-May-2024

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          Published In

          cover image Guide Proceedings
          AAAI'19/IAAI'19/EAAI'19: Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence
          January 2019
          10088 pages
          ISBN:978-1-57735-809-1

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          • Association for the Advancement of Artificial Intelligence

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          AAAI Press

          Publication History

          Published: 27 January 2019

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          • (2024)Impact of Model Interpretability and Outcome Feedback on Trust in AIProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642780(1-25)Online publication date: 11-May-2024

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