Upol Ehsan

Explainability Pitfalls: Beyond Dark Patterns in Explainable AI

2021 · arXiv (Cornell University)

To make Explainable AI (XAI) systems trustworthy, understanding harmful effects is just as important as producing well-designed explanations. In this paper, we address an important yet unarticulated type of negative effect in XAI. We introduce …

Rationalization: A Neural Machine Translation Approach to Generating\n Natural Language Explanations

2017 · arXiv (Cornell University)

We introduce AI rationalization, an approach for generating explanations of\nautonomous system behavior as if a human had performed the behavior. We\ndescribe a rationalization technique that uses neural machine translation to\ntranslate internal state-action representations of an …