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Poster
Transparency Promotion with Model-Agnostic Linear Competitors
Hassan Rafique · Tong Wang · Qihang Lin · Arshia Singhani

Thu Jul 16 06:00 AM -- 06:45 AM & Thu Jul 16 05:00 PM -- 05:45 PM (PDT) @
in Poster Session 41 »

We propose a novel type of hybrid model for multi-class classification, which utilizes competing linear models to collaborate with an existing black-box model, promoting transparency in the decision-making process. Our proposed hybrid model, Model-Agnostic Linear Competitors (MALC), brings together the interpretable power of linear models and the good predictive performance of the state-of-the-art black-box models. We formulate the training of a MALC model as a convex optimization problem, optimizing the predictive accuracy and transparency (defined as the percentage of data captured by the linear models) in the objective function. Experiments show that MALC offers more model flexibility for users to balance transparency and accuracy, in contrast to the currently available choice of either a pure black-box model or a pure interpretable model. The human evaluation also shows that more users are likely to choose MALC for this model flexibility compared with interpretable models and black-box models.

Keywords: Accountability, Transparency and Interpretability

Author Information

Hassan Rafique (The University of Iowa)
Tong Wang (University of Iowa)
Qihang Lin (University of Iowa)
Arshia Singhani (BASIS Independent Silicon Valley)

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