Are Model Explanations Useful in Practice? Rethinking How to Support Human-ML Interactions
Pages 942 - 944
References
[1]
Julius Adebayo, Justin Gilmer, Michael Muelly, Ian Goodfellow, Moritz Hardt, and Been Kim. 2018. Sanity checks for saliency maps. arXiv preprint arXiv:1810.03292 (2018).
[2]
Julius Adebayo, Michael Muelly, Harold Abelson, and Been Kim. 2022. Post hoc explanations may be ineffective for detecting unknown spurious correlation. In International Conference on Learning Representations.
[3]
Kasun Amarasinghe, Kit T Rodolfa, Sérgio Jesus, Valerie Chen, Vladimir Balayan, Pedro Saleiro, Pedro Bizarro, Ameet Talwalkar, and Rayid Ghani. 2022. On the Importance of Application-Grounded Experimental Design for Evaluating Explainable ML Methods. arXiv preprint arXiv:2206.13503 (2022).
[4]
Gagan Bansal, Tongshuang Wu, Joyce Zhou, Raymond Fok, Besmira Nushi, Ece Kamar, Marco Tulio Ribeiro, and Daniel Weld. 2021. Does the whole exceed its parts? the effect of ai explanations on complementary team performance. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. 1–16.
[5]
Stevie Chancellor. 2023. Toward Practices for Human-Centered Machine Learning. Commun. ACM 66, 3 (2023), 78–85.
[6]
Valerie Chen, Nari Johnson, Nicholay Topin, Gregory Plumb, and Ameet Talwalkar. 2022. Use-Case-Grounded Simulations for Explanation Evaluation. NeurIPS (2022).
[7]
Valerie Chen, Jeffrey Li, Joon Sik Kim, Gregory Plumb, and Ameet Talwalkar. 2022. Interpretable Machine Learning. Commun. ACM 65, 8 (2022).
[8]
Valerie Chen, Q Vera Liao, Jennifer Wortman Vaughan, and Gagan Bansal. 2023. Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations. CSCW (2023).
[9]
Joon Sik Kim, Valerie Chen, Danish Pruthi, Nihar B Shah, and Ameet Talwalkar. 2022. Assisting Human Decisions in Document Matching. NAACL HCI+NLP Workshop (2022).
[10]
Scott M Lundberg and Su-In Lee. 2017. A unified approach to interpreting model predictions. In Proceedings of the 31st international conference on neural information processing systems. 4768–4777.
[11]
Ada Martin, Valerie Chen, Sérgio Jesus, and Pedro Saleiro. 2023. A Case Study on Designing Evaluations of ML Explanations with Simulated User Studies. ICLR Workshop on Trustworthy ML (2023).
[12]
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016. " Why should i trust you?" Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. 1135–1144.
[13]
Ando Saabas. 2015. Interpreting random forests. http://blog.datadive.net/interpreting-random-forests/
[14]
Yunfeng Zhang, Q Vera Liao, and Rachel KE Bellamy. 2020. Effect of confidence and explanation on accuracy and trust calibration in AI-assisted decision making. In Proceedings of the 2020 conference on fairness, accountability, and transparency. 295–305.
Index Terms
- Are Model Explanations Useful in Practice? Rethinking How to Support Human-ML Interactions
Index terms have been assigned to the content through auto-classification.
Recommendations
Rethinking Construction: On Luciano Floridi's `Against Digital Ontology'
In the fourteenth chapter of The Philosophy of Information, Luciano Floridi puts forth a criticism of `digital ontology' as a step toward the articulation of an `informational structural realism'. Based on the claims made in the chapter, the present ...
Support Newcomer's Learning in Community of Practice: In Terms of Legitimate Peripheral Participation
ICEE '10: Proceedings of the 2010 International Conference on E-Business and E-GovernmentCommunity of practice is critical to organization learning. This paper takes the newcomer as subject. It analyzes the factors affected newcomer’s legitimate participation and peripheral participation, then it suggests how to support newcomer’s learning ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
August 2023
1026 pages
ISBN:9798400702310
DOI:10.1145/3600211
Copyright © 2023 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 29 August 2023
Check for updates
Qualifiers
- Abstract
- Research
- Refereed limited
Conference
AIES '23
Sponsor:
Acceptance Rates
Overall Acceptance Rate 61 of 162 submissions, 38%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 116Total Downloads
- Downloads (Last 12 months)60
- Downloads (Last 6 weeks)0
Reflects downloads up to 14 Feb 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format