FounData talk: Franco Turini: Local Rule-Based Explanation of Black Box Decision Systems

Title: Local Rule-Based Explanation of Black Box Decision Systems

Speaker: Franco Turini (University of Pisa) 

Abstract

The recent years have witnessed the rise of accurate but obscure decision systems which hide the logic of their internal decision processes to the users. The lack of explanations for the decisions of black box systems is a key ethical issue, and a limitation to the adoption of machine learning components in socially sensitive and safety-critical contexts. In this talk I focus on the problem of black box outcome explanation, i.e., explaining the reasons of the decision taken on a specific instance. I report on  LORE, an agnostic method able to provide interpretable and faithful explanations.

LORE first leans a local interpretable predictor on a synthetic neighborhood generated by a genetic algorithm. Then it derives from the logic of the local interpretable predictor an explanation consisting of: a decision rule, which explains the reasons of the decision; and a set of counterfactual rules, suggesting the changes in the instance’s features that lead to a different outcome. 

Host: Ira Assent (FounData)