Concept Embedding Models: Beyond the Accuracy-Explainability Trade Off
Channel: Pietro Barbiero
Date: 2022-11-09

Problem — Human trust in deep neural networks is currently an open problem as their decision process is opaque. Current methods such as Concept Bottleneck Models make the models more interpretable at the cost of decreasing accuracy (or vice versa).

Key innovation — A supervised concept embedding layer that learns semantically meaningful concept representations and allows simple human test-time interventions.

Solution — Concept Embedding Models increase human trust in deep learning going beyond the accuracy-explainability trade-off — being both highly accurate and interpretable.

References
Mateo Espinosa Zarlenga*, Pietro Barbiero*, Gabriele Ciravegna, Giuseppe Marra, Francesco Giannini, Michelangelo Diligenti, Zohreh Shams, Frederic Precioso, Stefano Melacci, Adrian Weller, Pietro Lio, and Mateja Jamnik “Concept Embedding Models.” arXiv preprint arXiv:2209.09056 (2022). Accepted at the Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS 2022).

Links
Paper preprint: https://arxiv.org/abs/2209.09056
Medium article: https://towardsdatascience.com/concept-embedding-models-beyond-the-accuracy-explainability-trade-off-f7ba02f28fad