Logic Synthesis and Verification Algorithms

doi:10.1007/0-387-31005-3

Cited by 4 publications

(1 citation statement)

References 72 publications

(154 reference statements)

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“…There exist also standard references with detailed overviews of the uses of prime implicants in the context of boolean functions[74,136]. Generalizations of prime implicants beyond boolean domains have been considered before[222].…”

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confidence: 99%

Logic-Based Explainability in Machine Learning

Marques-Silva¹

2022

Preprint

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The last decade witnessed an ever-increasing stream of successes in Machine Learning (ML). These successes offer clear evidence that ML is bound to become pervasive in a wide range of practical uses, including many that directly affect humans. Unfortunately, the operation of the most successful ML models is incomprehensible for human decision makers. As a result, the use of ML models, especially in highrisk and safety-critical settings is not without concern. In recent years, there have been efforts on devising approaches for explaining ML models. Most of these efforts have focused on so-called model-agnostic approaches. However, all model-agnostic and related approaches offer no guarantees of rigor, hence being referred to as non-formal. For example, such non-formal explanations can be consistent with different predictions, which renders them useless in practice. This paper overviews the ongoing research efforts on computing rigorous model-based explanations of ML models; these being referred to as formal explanations. These efforts encompass a variety of topics, that include the actual definitions of explanations, the characterization of the complexity of computing explanations, the currently best logical encodings for reasoning about different ML models, and also how to make explanations interpretable for human decision makers, among others.

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confidence: 99%

Logic-Based Explainability in Machine Learning

Marques-Silva¹

2022

Preprint

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On the Difficulty of Drawing the Line

Boßelmann

Naujokat

Steffen

2018

Leveraging Applications of Formal Methods, Verification and Validation. Modeling

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Logic-Based Explainability in Machine Learning

Marques-Silva

2023

Lecture Notes in Computer Science

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In recent years, the impact of machine learning (ML) and artificial intelligence (AI) in society has been absolutely remarkable. This impact is expected to continue in the foreseeable future. However, the adoption of AI/ML is also a cause of grave concern. The operation of the most advances AI/ML models is often beyond the grasp of human decision makers. As a result, decisions that impact humans may not be understood and may lack rigorous validation. Explainable AI (XAI) is concerned with providing human decision-makers with understandable explanations for the predictions made by ML models. As a result, XAI is a cornerstone of trustworthy AI. Despite its strategic importance, most work on XAI lacks rigor, and so its use in high-risk or safety-critical domains serves to foster distrust instead of contributing to build muchneeded trust. Logic-based XAI has recently emerged as a rigorous alternative to those other non-rigorous methods of XAI. This paper provides a technical survey of logic-based XAI, its origins, the current topics of research, and emerging future topics of research. The paper also highlights the many myths that pervade non-rigorous approaches for XAI.

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Logic Synthesis and Verification Algorithms

Cited by 4 publications

References 72 publications

Logic-Based Explainability in Machine Learning

Logic-Based Explainability in Machine Learning

On the Difficulty of Drawing the Line

Logic-Based Explainability in Machine Learning

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