122 citations · 553 across the 26 of their papers we have counts for
11 papers · 1 filter
A Holistic Approach to Interpretability in Financial Lending: Models, Visualizations, and Summary-Explanations
Chaofan Chen, Kangcheng Lin, Cynthia Rudin +3
Lending decisions are usually made with proprietary models that provide minimally acceptable explanations to users. In a future world without such secrecy, what decision support to…
Partially Interpretable Estimators (PIE): Black-Box-Refined Interpretable Machine Learning
Tong Wang, Jingyi Yang, Yunyi Li +1
We propose Partially Interpretable Estimators (PIE) which attribute a prediction to individual features via an interpretable model, while a (possibly) small part of the PIE predict…
Augmented Fairness: An Interpretable Model Augmenting Decision-Makers' Fairness
Tong Wang, Maytal Saar-Tsechansky
We propose a model-agnostic approach for mitigating the prediction bias of a black-box decision-maker, and in particular, a human decision-maker. Our method detects in the feature…
Same-Day Delivery with Fairness
Xinwei Chen, Tong Wang, Barrett W. Thomas +1
The demand for same-day delivery (SDD) has increased rapidly in the last few years and has particularly boomed during the COVID-19 pandemic. The fast growth is not without its chal…
Early Predictions for Medical Crowdfunding: A Deep Learning Approach Using Diverse Inputs
Tong Wang, Fujie Jin, Yu Hu +1
Medical crowdfunding is a popular channel for people needing financial help paying medical bills to collect donations from large numbers of people. However, large heterogeneity exi…
Model-Agnostic Linear Competitors -- When Interpretable Models Compete and Collaborate with Black-Box Models
Hassan Rafique, Tong Wang, Qihang Lin
Driven by an increasing need for model interpretability, interpretable models have become strong competitors for black-box models in many real applications. In this paper, we propo…