11 citations · 24 across the 8 of their papers we have counts for
3 papers · 1 filter
Towards learning to explain with concept bottleneck models: mitigating information leakage
Joshua Lockhart, Nicolas Marchesotti, Daniele Magazzeni +1
Concept bottleneck models perform classification by first predicting which of a list of human provided concepts are true about a datapoint. Then a downstream model uses these predi…
Feature Importance for Time Series Data: Improving KernelSHAP
Mattia Villani, Joshua Lockhart, Daniele Magazzeni
Feature importance techniques have enjoyed widespread attention in the explainable AI literature as a means of determining how trained machine learning models make their prediction…
Some people aren't worth listening to: periodically retraining classifiers with feedback from a team of end users
Joshua Lockhart, Samuel Assefa, Tucker Balch +1
Document classification is ubiquitous in a business setting, but often the end users of a classifier are engaged in an ongoing feedback-retrain loop with the team that maintain it.…