Mateo Espinosa Zarlenga

Concept Embedding Models: Beyond the Accuracy-Explainability Trade-Off

2022 · arXiv (Cornell University)

Deploying AI-powered systems requires trustworthy models supporting effective human interactions, going beyond raw prediction accuracy. Concept bottleneck models promote trustworthiness by conditioning classification tasks on an intermediate level of human-like concepts. This enables human interventions …