Name
Abstract | ||
|---|---|---|
Artificial intelligence (AI) is being deployed in missions that are increasingly critical for human life. To build trust in AI and avoid an algorithm-based authoritarian society, automated decisions should be explainable. This is not only a right of citizens, enshrined for example in the European General Data Protection Regulation, but a desirable goal for engineers, who want to know whether the decision algorithms are capturing the relevant features. For explainability to be scalable, it should be possible to derive explanations in a systematic way. A common approach is to use simpler, more intuitive decision algorithms to build a surrogate model of the black-box model (for example a deep learning algorithm) used to make a decision. Yet, there is a risk that the surrogate model is too large for it to be really comprehensible to humans. We focus on explaining black-box models by using decision trees of limited depth as a surrogate model. Specifically, we propose an approach based on microaggregation to achieve a trade-off between the comprehensibility and the representativeness of the surrogate model on the one side and the privacy of the subjects used for training the black-box model on the other side. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.knosys.2020.105532 | Knowledge-Based Systems |
Keywords | DocType | Volume |
Explainability,Machine learning,Data protection,Microaggregation,Privacy | Journal | 194 |
ISSN | Citations | PageRank |
0950-7051 | 1 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alberto Blanco-Justicia | 1 | 14 | 6.77 |
| Josep Domingo | 2 | 67 | 12.25 |
| Sergio Martínez | 3 | 167 | 13.34 |
| David Sánchez | 4 | 690 | 33.01 |