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20142025
most citedInterpretable to Whom? A Role-based Model for Analyzing Interpretable Machine Learning Systems

83 citations · 161 across the 12 of their papers we have counts for

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cs.LG2024

Can we Constrain Concept Bottleneck Models to Learn Semantically Meaningful Input Features?

Jack Furby, Daniel Cunnington, Dave Braines +1

Concept Bottleneck Models (CBMs) are regarded as inherently interpretable because they first predict a set of human-defined concepts which are used to predict a task label. For inh…