Name
Abstract | ||
|---|---|---|
In real-world applications, such as loan approvals or claims management, machine learn-ing (ML) models need to be updated or retrained to adhere to new rules and regulations.But how can a new model be built and new decision boundaries be formed without having new training data available? We present the AI Model Explorer and Editor tool (AIMEE) for model exploration and model editing using human understandable rules. It addresses the problem of changing decision boundaries by leveraging user-specified feedback rules that are used to pre-process training data such that a retrained model will reflect user changes.The pre-processing step uses synthetic oversampling and relabeling and assumes black-box access to the algorithm that retrains the model. AIMEE provides interactive methods to edit rule sets, visualize changes to decision boundaries, and generate interpretable comparisons of model changes so that users see their feedback reflected in the updated model. The demo shows an end-to-end solution that supports the full update lifecycle of an ML model. |
Year | Venue | DocType |
|---|---|---|
2021 | Annual Conference on Neural Information Processing Systems | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Owen Cornec | 1 | 0 | 0.34 |
| Rahul Nair | 2 | 0 | 1.01 |
| Elizabeth Daly | 3 | 0 | 2.37 |
| Oznur Alkan | 4 | 0 | 1.01 |
| Dennis Wei | 5 | 0 | 0.34 |