Beneficial and harmful explanatory machine learning
Machine Learning, 2021. [All Versions]. Given the recent successes of Deep Learning in AI there has been increased interest in the role and need for explanations in machine learned theories. A distinct notion in this context is that of Michie’s definition of ultra-strong…
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Abduction-Based Explanations for Machine Learning Models ojs.aaai.org
AAAI'19, 2019. [All Versions]. The growing range of applications of Machine Learning (ML) in a multitude of settings motivates the ability of computing small explanations for predictions made. Small explanations are generally accepted as easier for human decision makers to…
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Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP link.springer.com
Machine Learning, 2018. [All Versions]. During the 1980s Michie defined Machine Learning in terms of two orthogonal axes of performance: predictive accuracy and comprehensibility of generated hypotheses. Since predictive accuracy was readily measurable and comprehensibility not…
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Explainable Machine learning in Credit Risk Management papers.ssrn.com
Proposes an explainable AI model that can be used in credit risk management and, in particular, in measuring the risks that arise when credit is borrowed employing credit scoring platforms.
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Explaining machine learning models with interactive natural language conversations using TalkToModel nature.com
Nature Machine Intelligence, 2023. [All Versions]. Practitioners increasingly use machine learning (ML) models, yet models have become more complex and harder to understand. To understand complex models, researchers have proposed techniques to explain model predictions…
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Probabilistic machine learning and artificial intelligence nature.com
Nature, 2015. [All Versions]. Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The…
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Probabilistic Sufficient Explanations ijcai.org
IJCAI'21, 2021. [All Versions]. Understanding the behavior of learned classifiers is an important task, and various black-box explanations, logical reasoning approaches, and model-specific methods have been proposed. This paper introduces probabilistic sufficient explanations…
AGI & CoCoSci › Papers > Explainability > Strong Machine Learning: “Machine Learning, 2021. [All Versions]. Given the recent successes of Deep Learning in AI there has been increased interest in the role and need for explanations in machine learned theories. A distinct notion in this context is that of Michie’s definition of ultra-strong…”