Name
Abstract | ||
|---|---|---|
Machine learned models exhibit bias, often because the datasets used to train them are biased. This presents a serious problem for the deployment of such technology, as the resulting models might perform poorly on populations that are minorities within the training set and ultimately present higher risks to them. We propose to use high-fidelity computer simulations to interrogate and diagnose biases within ML classifiers. We present a framework that leverages Bayesian parameter search to efficiently characterize the high dimensional feature space and more quickly identify weakness in performance. We apply our approach to an example domain, face detection, and show that it can be used to help identify demographic biases in commercial face application programming interfaces (APIs). |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Learning | Training set,Feature vector,Software deployment,Application programming interface,Artificial intelligence,Face detection,Mathematics,Machine learning,Bayesian probability |
DocType | Volume | Citations |
Journal | abs/1810.00471 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel J McDuff | 1 | 672 | 61.67 |
| Roger Cheng | 2 | 0 | 0.68 |
| Ashish Kapoor | 3 | 1833 | 119.72 |