Harini Suresh

schoolMassachusetts Institute of Technologybar_chart#4519 Global Rank

Total Papers

HCI Rank

#4519

Research Areas

Years Active

Recent Publications

Kaleidoscope: Semantically-grounded, Context-specific ML Model Evaluation

Desired model behavior often differs across contexts (e.g., different geographies, communities, or institutions), but there is little infrastructure to facilitate context-specific evaluations key to deployment decisions and building trust. Here, we present Kaleidoscope, a system for evaluating models in terms of user-…

IUI '221st AuthorExplainable AI (XAI) +2

Intuitively Assessing ML Model Reliability through Example-Based Explanations and Editing Model Inputs

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 visualizations that poorly map to the task at hand or require non-trivial ML expertise to interpret. Here, we present two interface modules…

CHI '211st AuthorExplainable AI (XAI) +2

Beyond Expertise and Roles: A Framework to Characterize the Stakeholders of Interpretable Machine Learning and their Needs

To ensure accountability and mitigate harm, it is critical that diverse stakeholders can interrogate black-box automated systems and find information that is understandable, relevant, and useful to them. In this paper, we eschew prior expertise- and role-based categorizations of interpretability stakeholders in favor…

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