T-REx: Table Repair Explanations

TL;DR

T-REx is a system that explains data repairs by using Shapley values to identify the most influential constraints and cells, helping users understand and modify data repair outcomes.

Contribution

It introduces a generic, model-agnostic explanation system for data repair algorithms using Shapley values, applicable to any repair method as a black box.

Findings

Provides ranked explanations of constraints and cells influencing repairs

Enables users to understand the repair process better

Supports modification of data based on explanations

Abstract

Data repair is a common and crucial step in many frameworks today, as applications may use data from different sources and of different levels of credibility. Thus, this step has been the focus of many works, proposing diverse approaches. To assist users in understanding the output of such data repair algorithms, we propose T-REx, a system for providing data repair explanations through Shapley values. The system is generic and not specific to a given repair algorithm or approach: it treats the algorithm as a black box. Given a specific table cell selected by the user, T-REx employs Shapley values to explain the significance of each constraint and each table cell in the repair of the cell of interest. T-REx then ranks the constraints and table cells according to their importance in the repair of this cell. This explanation allows users to understand the repair process, as well as to act based on this knowledge, to modify the most influencing constraints or the original database.