Score-Based Explanations in Data Management and Machine Learning

Attribution-Scores and Causal Counterfactuals as Explanations in Artificial Intelligence

In this expository article we highlight the relevance of explanations for artificial intelligence, in general, and for the newer developments in {\em explainable AI}, referring to origins and connections of and among different approaches. We describe in simple terms, explanations in data management and machine learning that are based on attribution-scores, and counterfactuals as found in the area of causality. We elaborate on the importance of logical reasoning when dealing with counterfactuals, and their use for score computation.

Score-Based Explanations in Data Management and Machine Learning: An Answer-Set Programming Approach to Counterfactual Analysis

We describe some recent approaches to score-based explanations for query answers in databases and outcomes from classification models in machine learning. The focus is on work done by the author and collaborators. Special emphasis is placed on declarative approaches based on answer-set programming to the use of counterfactual reasoning for score specification and computation. Several examples that illustrate the flexibility of these methods are shown.

数据管理与可解释机器学习中的归因分数

Attribution-Scores in Data Management and Explainable Machine Learning

本文介绍了实际因果性中的责任分数在数据库查询答案解释和机器学习分类模型输出解释中的应用，说明了与数据库修复的联系，扩展了分类模型的责任分数，并讨论了Shap-score的高效计算。

数据管理中的因果效应分数

The Causal-Effect Score in Data Management

本文在经典与概率数据库中引入并推广因果效应分数，用于度量元组对查询回答的因果强度，拓展了因果效应在数据管理归因中的应用。

基于因果性的解释数据管理中的评分对齐

Causality-Based Scores Alignment in Explainable Data Management

本文研究了数据库查询中因果评分的对齐问题，发现外生元组的存在对评分一致性有关键影响。

基于知识编译的神经网络分类器Shap解释得分高效计算方法

Efficient Computation of Shap Explanation Scores for Neural Network Classifiers via Knowledge Compilation

针对神经网络这类黑盒分类器的Shap解释得分计算难解问题，提出基于逻辑知识编译将二值神经网络转换为可高效计算Shap的布尔电路的方法，实验显示性能提升显著。