Explaining machine learning models with interactive natural language conversations using TalkToModel
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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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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The future of open human feedback nature.com
Nature Machine Intelligence, 2025. [All Versions]. Human feedback on conversations with language models is central to how these systems learn about the world, improve their capabilities and are steered towards desirable and safe behaviours. However, this feedback is mostly…
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Beneficial and harmful explanatory machine learning link.springer.com
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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How laypeople evaluate scientific explanations containing jargon nature.com
Nature Human Behavior, 2025. [All Versions]. Individuals rely on others’ expertise to achieve a basic understanding of the world. But how can non-experts achieve understanding from explanations that, by definition, they are ill-equipped to assess? Across 9 experiments with…
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Interpretable Machine Learning: A Guide for Making Black Box Models Explainable christophm.github.io
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AGI & CoCoSci › Papers > Explainability > Trustworthy AI: “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…”