Explaining Inference Queries with Bayesian Optimization

TL;DR

This paper introduces BOExplain, a Bayesian optimization-based framework for explaining inference query results by identifying impactful data subsets, improving explanation quality over existing methods.

Contribution

The paper presents BOExplain, a novel Bayesian optimization approach with new techniques for categorical variables, to generate better explanations for inference queries.

Findings

BOExplain produces more effective explanations than state-of-the-art methods.

It successfully explains inference queries using source and training data.

The framework is validated on real-world datasets.

Abstract

Obtaining an explanation for an SQL query result can enrich the analysis experience, reveal data errors, and provide deeper insight into the data. Inference query explanation seeks to explain unexpected aggregate query results on inference data; such queries are challenging to explain because an explanation may need to be derived from the source, training, or inference data in an ML pipeline. In this paper, we model an objective function as a black-box function and propose BOExplain, a novel framework for explaining inference queries using Bayesian optimization (BO). An explanation is a predicate defining the input tuples that should be removed so that the query result of interest is significantly affected. BO - a technique for finding the global optimum of a black-box function - is used to find the best predicate. We develop two new techniques (individual contribution encoding and warm start) to handle categorical variables. We perform experiments showing that the predicates found by BOExplain have a higher degree of explanation compared to those found by the state-of-the-art query explanation engines. We also show that BOExplain is effective at deriving explanations for inference queries from source and training data on a variety of real-world datasets. BOExplain is open-sourced as a Python package at https://github.com/sfu-db/BOExplain.