Editable machine learning models? A rule-based framework for user studies of explainability

DOI10.1007/s11634-020-00419-2zbMath1459.68203OpenAlexW3086877397MaRDI QIDQ2022486

Tomáš Kliegr, Stanislav Vojíř

Publication date: 29 April 2021

Published in: Advances in Data Analysis and Classification. ADAC (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11634-020-00419-2

zbMATH Keywords

crowdsourcingcognitive computingrule learningexplainable artificial intelligencelegal complianceuser experiment

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Knowledge representation (68T30)

Related Items (1)

Disjunctive Rule Lists

Uses Software

• RuleML
• Psychophysics Toolbox
• DeepRED
• PyGaze

Cites Work

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• On cognitive preferences and the plausibility of rule-based models
• Ultra-strong machine learning: comprehensibility of programs learned with ILP
• Explanation in artificial intelligence: insights from the social sciences
• The GUHA method of automatic hypotheses determination
• Foundations of Rule Learning

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