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Towards Unifying Feature Attribution and Counterfactual Explanations: Different Means to the Same End
Ramaravind Kommiya Mothilal, Divyat Mahajan, Chenhao Tan +1
Feature attributions and counterfactual explanations are popular approaches to explain a ML model. The former assigns an importance score to each input feature, while the latter pr…
Evaluating and Characterizing Human Rationales
Samuel Carton, Anirudh Rathore, Chenhao Tan
Two main approaches for evaluating the quality of machine-generated rationales are: 1) using human rationales as a gold standard; and 2) automated metrics based on how rationales a…
Characterizing the Value of Information in Medical Notes
Chao-Chun Hsu, Shantanu Karnwal, Sendhil Mullainathan +2
Machine learning models depend on the quality of input data. As electronic health records are widely adopted, the amount of data in health care is growing, along with complaints ab…
Harnessing Explanations to Bridge AI and Humans
Vivian Lai, Samuel Carton, Chenhao Tan
Machine learning models are increasingly integrated into societally critical applications such as recidivism prediction and medical diagnosis, thanks to their superior predictive p…
"Why is 'Chicago' deceptive?" Towards Building Model-Driven Tutorials for Humans
Vivian Lai, Han Liu, Chenhao Tan
To support human decision making with machine learning models, we often need to elucidate patterns embedded in the models that are unsalient, unknown, or counterintuitive to humans…