A Multistakeholder Approach Towards Evaluating AI Transparency Mechanisms

Copyright held by the owner/author(s). CHI’21, May 8-13, 2021, Online Virtual Conference ACM 978-1-4503-6819-3/20/04. https://doi.org/10.1145/3334480.XXXXXXX Abstract 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 [14]. Previous work found that the majority of deployed transparency mechanisms primarily serve technical stakeholders [2]. In our work, we want to investigate how well transparency mechanisms might work in practice for a more diverse set of stakeholders by conducting a large-scale, mixed-methods user study across a range of organizations, within a particular industry such as health care, criminal justice, or content moderation. In this paper, we outline the setup for our study.

[1]  Been Kim,et al.  Towards A Rigorous Science of Interpretable Machine Learning , 2017, 1702.08608.

[2]  Scott Lundberg,et al.  A Unified Approach to Interpreting Model Predictions , 2017, NIPS.

[3]  Carlos Guestrin,et al.  "Why Should I Trust You?": Explaining the Predictions of Any Classifier , 2016, ArXiv.

[4]  Alice Xiang,et al.  Machine Learning Explainability for External Stakeholders , 2020, ArXiv.

[5]  Timnit Gebru,et al.  Datasheets for datasets , 2018, Commun. ACM.

[6]  Gary Klein,et al.  Metrics for Explainable AI: Challenges and Prospects , 2018, ArXiv.

[7]  Inioluwa Deborah Raji,et al.  Model Cards for Model Reporting , 2018, FAT.

[8]  Ankur Taly,et al.  Explainable machine learning in deployment , 2020, FAT*.

[9]  Zachary Chase Lipton.  The mythos of model interpretability , 2016, ACM Queue.

[10]  Q. Liao,et al.  Questioning the AI: Informing Design Practices for Explainable AI User Experiences , 2020, CHI.

[11]  Harmanpreet Kaur,et al.  Interpreting Interpretability: Understanding Data Scientists' Use of Interpretability Tools for Machine Learning , 2020, CHI.

[12]  Cynthia Rudin,et al.  Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead , 2018, Nature Machine Intelligence.

[13]  Chris Russell,et al.  Counterfactual Explanations Without Opening the Black Box: Automated Decisions and the GDPR , 2017, ArXiv.

[14]  Eric D. Ragan,et al.  A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems , 2018, ACM Trans. Interact. Intell. Syst..

[15]  A. Chouldechova,et al.  Toward Algorithmic Accountability in Public Services: A Qualitative Study of Affected Community Perspectives on Algorithmic Decision-making in Child Welfare Services , 2019, CHI.

[16]  Franco Turini,et al.  A Survey of Methods for Explaining Black Box Models , 2018, ACM Comput. Surv..