Gabriele Ciravegna

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 …

Extending Logic Explained Networks to Text Classification

2022 · arXiv (Cornell University)

Recently, Logic Explained Networks (LENs) have been proposed as explainable-by-design neural models providing logic explanations for their predictions. However, these models have only been applied to vision and tabular data, and they mostly favour the …