© 2019 IEEE. Tools that enhance interpretability of classifiers tend to focus on the knowledgeable data scientist. Here we propose techniques that generate textual explanations of the internal behavior of a given classifier, aiming at less technically proficient users of machine learning resources. Our approach has been positively evaluated by a group of users who received its textual output.
|Original language||American English|
|Number of pages||4|
|State||Published - 1 Jun 2019|
|Event||Proceedings - IEEE 2nd International Conference on Artificial Intelligence and Knowledge Engineering, AIKE 2019 - |
Duration: 1 Jun 2019 → …
|Conference||Proceedings - IEEE 2nd International Conference on Artificial Intelligence and Knowledge Engineering, AIKE 2019|
|Period||1/06/19 → …|