Toward Machine-Guided, Human-Initiated Explanatory Interactive Learning

TL;DR

This paper introduces explanatory guided learning, a new interactive strategy where humans select queries and machines use global explanations to improve learning, reducing bias and enhancing sample efficiency.

Contribution

It proposes a novel interactive learning approach that combines human supervision with global explanations to mitigate narrative bias in explanatory active learning.

Findings

Reduces bias in model explanations

Improves sample efficiency over traditional active learning

Shows promising initial results with clustering-based prototypes

Abstract

Recent work has demonstrated the promise of combining local explanations with active learning for understanding and supervising black-box models. Here we show that, under specific conditions, these algorithms may misrepresent the quality of the model being learned. The reason is that the machine illustrates its beliefs by predicting and explaining the labels of the query instances: if the machine is unaware of its own mistakes, it may end up choosing queries on which it performs artificially well. This biases the "narrative" presented by the machine to the user.We address this narrative bias by introducing explanatory guided learning, a novel interactive learning strategy in which: i) the supervisor is in charge of choosing the query instances, while ii) the machine uses global explanations to illustrate its overall behavior and to guide the supervisor toward choosing challenging, informative instances. This strategy retains the key advantages of explanatory interaction while avoiding narrative bias and compares favorably to active learning in terms of sample complexity. An initial empirical evaluation with a clustering-based prototype highlights the promise of our approach.