430 citations · 898 across the 25 of their papers we have counts for
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cs.HC2021
Intuitively Assessing ML Model Reliability through Example-Based Explanations and Editing Model Inputs
Harini Suresh, Kathleen M. Lewis, John V. Guttag +1
Interpretability methods aim to help users build trust in and understand the capabilities of machine learning models. However, existing approaches often rely on abstract, complex v…
cs.HC2021★ 109 cited
Beyond Expertise and Roles: A Framework to Characterize the Stakeholders of Interpretable Machine Learning and their Needs
Harini Suresh, Steven R. Gomez, Kevin K. Nam +1
To ensure accountability and mitigate harm, it is critical that diverse stakeholders can interrogate black-box automated systems and find information that is understandable, releva…