Name
Abstract | ||
|---|---|---|
We propose a possible solution to a public challenge posed by the Fair Isaac Corporation (FICO), which is to provide an explainable for credit risk assessment. Rather than present a black box and explain it afterwards, we provide a globally interpretable that is as accurate as other neural networks. Our two-layer additive risk model is decomposable into subscales, where each node in the second layer represents a meaningful subscale, and all of the nonlinearities are transparent. We provide three types of explanations that are simpler than, but consistent with, the global model. One of these explanation methods involves solving a minimum set cover problem to find high-support globally-consistent explanations. We present a new online visualization tool to allow users to explore the global and its explanations. |
Year | Venue | DocType |
|---|---|---|
2018 | arXiv: Learning | Journal |
Volume | Citations | PageRank |
abs/1811.12615 | 1 | 0.37 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chaofan Chen | 1 | 19 | 2.01 |
| Kangcheng Lin | 2 | 1 | 0.37 |
| Cynthia Rudin | 3 | 720 | 61.51 |
| Yaron Shaposhnik | 4 | 3 | 0.79 |
| sijia wang | 5 | 24 | 4.24 |
| Tong Wang | 6 | 35 | 5.12 |