Score-Based Explanations in Data Management and Machine Learning
We describe some approaches to explanations for observed outcomes in data management and machine learning. They are based on the assignment of numerical scores to predefined and potentially relevant inputs. More specifically, we consider explanations for query answers in databases, and for results from classification models. The described approaches are mostly of a causal and counterfactual nature. We argue for the need to bring domain and semantic knowledge into score computations; and suggest some ways to do this.
Code (0)
등록된 구현이 없습니다.
Tasks
BIG-bench Machine LearningcounterfactualManagementSimilar Papers 제목 키워드 기반
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. Specia…
BIG-bench Machine LearningcounterfactualCounterfactual ReasoningManagementAttribution-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 dif…
Logical ReasoningManagementAttribution-Scores in Data Management and Explainable Machine Learning
We describe recent research on the use of actual causality in the definition of responsibility scores as explanations for query answers in databases, and for outcomes from classification models in machine learning. In th…
ClassificationManagementExplainable Data-Driven Optimization: From Context to Decision and Back Again
Data-driven optimization uses contextual information and machine learning algorithms to find solutions to decision problems with uncertain parameters. While a vast body of work is dedicated to interpreting machine learni…
counterfactualCounterfactual ExplanationManagementSelective Explanations
Feature attribution methods explain black-box machine learning (ML) models by assigning importance scores to input features. These methods can be computationally expensive for large ML models. To address this challenge, …