Explaining Decisions in ML Models: a Parameterized Complexity Analysis

TL;DR

This paper offers a detailed theoretical analysis of the computational complexity involved in generating explanations for various transparent machine learning models, covering abductive and contrastive explanations at local and global levels.

Contribution

It provides a comprehensive parameterized complexity framework for explanation problems across multiple ML models, filling a key gap in explainable AI research.

Findings

Complexity results vary across models and explanation types.

Some explanation problems are computationally hard, others are tractable.

The analysis guides future research in developing efficient explanation methods.

Abstract

This paper presents a comprehensive theoretical investigation into the parameterized complexity of explanation problems in various machine learning (ML) models. Contrary to the prevalent black-box perception, our study focuses on models with transparent internal mechanisms. We address two principal types of explanation problems: abductive and contrastive, both in their local and global variants. Our analysis encompasses diverse ML models, including Decision Trees, Decision Sets, Decision Lists, Ordered Binary Decision Diagrams, Random Forests, and Boolean Circuits, and ensembles thereof, each offering unique explanatory challenges. This research fills a significant gap in explainable AI (XAI) by providing a foundational understanding of the complexities of generating explanations for these models. This work provides insights vital for further research in the domain of XAI, contributing to the broader discourse on the necessity of transparency and accountability in AI systems.