Cited by 125Explainable Artificial Intelligence (XAI)Software Testing and Debugging TechniquesUsability and User Interface Design

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Explaining machine learning models with interactive natural language conversations using TalkToModel

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Slack, Dylan, Krishna, Satyapriya, Lakkaraju, Himabindu, Singh, Sameer·2023·Nature Machine Intelligence

DOI 10.1038/s42256-023-00692-8

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Abstract

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. However, practitioners struggle to use explainability methods because they do not know which explanation to choose and how to interpret the explanation. Here we address the challenge of using explainability methods by proposing TalkToModel: an interactive dialogue system that explains ML models through natural language conversations. TalkToModel consists of three components: an adaptive dialogue engine that interprets natural language and generates meaningful responses; an execution component that constructs the explanations used in the conversation; and a conversational interface. In real-world evaluations, 73% of healthcare workers agreed they would use TalkToModel over existing systems for understanding a disease prediction model, and 85% of ML professionals agreed TalkToModel was easier to use, demonstrating that TalkToModel is highly effective for model explainability.

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Authors: Slack, D., Krishna, S., Lakkaraju, H., & Singh, S.
Affiliations: University of California, Irvine (US)
Harvard University (US)
Corresponding author: Slack, Dylan
ORCID: Slack, Dylan
Krishna, Satyapriya
Lakkaraju, Himabindu
Singh, Sameer
Venue: Nature Machine Intelligence
Publisher: Springer Science and Business Media LLC
Published: 2023-07-27
Volume: 5
Issue: 8
Pages: 873-883
ISSN: 2522-5839 (ISSN-L)
DOI: 10.1038/s42256-023-00692-8
OpenAlex: W4385302687
Cited by: 125
References: 80
Citations by year: 2026: 31 · 2025: 53 · 2024: 33 · 2023: 8
FWCI: 11.78
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0 (tdm)
creativecommons.org/licenses/by/4.0 (vor)
Locations: Nature Machine Intelligence (publishedVersion, cc-by)
Research Square (acceptedVersion, cc-by)
Funders: NSF | Directorate for Computer & Information Science & Engineering | Division of Information and Intelligent Systems (IIS-2040989, IIS-2046873, IIS-2008956, IIS-2008461)
I was supported by a fellowship from the hasso plattner institute during the bulk of completing this work.
Google, JP Morgan, Amazon, Harvard Data Science Initiative, D^3 institute at Harvard
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
Physical Sciences › Computer Science › Software › Software Testing and Debugging Techniques
Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
Keywords: machine learning explainability, natural language dialogue systems, interactive explanations, conversational interfaces, model understanding, disease prediction model, healthcare workers

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Explaining machine learning models with interactive natural language conversations using TalkToModel

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