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Given that there are a variety of stakeholders involved in, and affected by, decisions from machine learning (ML) models, it is important to consider that different stakeholders have different transparency needs.
The Mythos of Model Interpretability
Zachary C. Lipton. 2016 · 2016
Earlier work this paper cites.
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
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016 · 2016
Earlier work this paper cites.
Towards a Rigorous Science of Interpretable Machine Learning
Finale Doshi-Velez and Been Kim. 2017 · 2017
Earlier work this paper cites.
A Unified Approach to Interpreting Model Predictions. In NIPS
Scott M Lundberg and Su-In Lee. 2017 · 2017
Earlier work this paper cites.
A Survey of Methods for Explaining Black Box Models
Riccardo Guidotti, Anna Monreale, Franco Turini, Dino Pedreschi, and Fosca Giannotti. 2018 · 2018
Earlier work this paper cites.
Metrics for Explainable AI: Challenges and Prospects
Robert R. Hoffman, Shane T. Mueller, Gary Klein, and Jordan Litman. 2018 · 2018
Earlier work this paper cites.
Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR
Sandra Wachter, Brent Mittelstadt, and Chris Russell. 2018 · 2018
Cited alongside, same era.
Toward Algorithmic Accountability in Public Services: A Qualitative Study of Affected Community Perspectives on Algorithmic Decision-Making in Child Welfare Services. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
Anna Brown, Alexandra Chouldechova, Emily Putnam-Hornstein, Andrew Tobin, and Rhema Vaithianathan. 2019 · 2019
Cited alongside, same era.
FOCUS: Flexible Optimizable Counterfactual Explanations for Tree Ensembles
Ana Lucic, Harrie Oosterhuis, Hinda Haned, and Maarten de Rijke. 2019 · 2019
Cited alongside, same era.
Model Cards for Model Reporting. In Proceedings of the Conference on Fairness, Accountability, and Transparency - FAT* ’19
Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, and Timnit Gebru. 2019 · 2019
Cited alongside, same era.
Explainable Machine Learning in Deployment. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency
Umang Bhatt, Alice Xiang, Shubham Sharma, Adrian Weller, Ankur Taly, Yunhan Jia, Joydeep Ghosh, Ruchir Puri, José M. F. Moura, and Peter Eckersley. 2020b · 2020
Later among the works it cites.
Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé III, and Kate Crawford. 2020 · 2020
Later among the works it cites.
Interpreting Interpretability: Understanding Data Scientists’ Use of Interpretability Tools for Machine Learning. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
Harmanpreet Kaur, Harsha Nori, Samuel Jenkins, Rich Caruana, Hanna Wallach, and Jennifer Wortman Vaughan. 2020 · 2020
Later among the works it cites.
Questioning the AI: Informing Design Practices for Explainable AI User Experiences. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
Q Vera Liao, Daniel Gruen, and Sarah Miller. 2020 · 2020
Later among the works it cites.
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Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead
Cynthia Rudin. 2019 · 2019
Cited alongside, same era.
Machine learning explainability for external stakeholders
Umang Bhatt, McKane Andrus, Adrian Weller, and Alice Xiang. 2020a · 2020
Cited alongside, same era.
A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems
Sina Mohseni, Niloofar Zarei, and Eric D. Ragan. 2020 · 2020
Later among the works it cites.