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Gamut: A Design Probe to Understand How Data Scientists Understand Machine Learning Models

Authors:

Steven M. DruckerAuthors Info & Claims

CHI '19: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

Paper No.: 579, Pages 1 - 13

https://doi.org/10.1145/3290605.3300809

Published: 02 May 2019 Publication History

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Abstract

Without good models and the right tools to interpret them, data scientists risk making decisions based on hidden biases, spurious correlations, and false generalizations. This has led to a rallying cry for model interpretability. Yet the concept of interpretability remains nebulous, such that researchers and tool designers lack actionable guidelines for how to incorporate interpretability into models and accompanying tools. Through an iterative design process with expert machine learning researchers and practitioners, we designed a visual analytics system, Gamut, to explore how interactive interfaces could better support model interpretation. Using Gamut as a probe, we investigated why and how professional data scientists interpret models, and how interface affordances can support data scientists in answering questions about model interpretability. Our investigation showed that interpretability is not a monolithic concept: data scientists have different reasons to interpret models and tailor explanations for specific audiences, often balancing competing concerns of simplicity and completeness. Participants also asked to use Gamut in their work, highlighting its potential to help data scientists understand their own data.

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Cited By

Meyer LZhu J(2024)Slide to Explore 'What If': An Analysis of Explainable InterfacesAdjunct Proceedings of the 2024 Nordic Conference on Human-Computer Interaction10.1145/3677045.3685416(1-6)Online publication date: 13-Oct-2024
https://dl.acm.org/doi/10.1145/3677045.3685416
Goldstein HTao JHatfield-Dodds ZPierce BHead A(2024)Tyche: Making Sense of PBT EffectivenessProceedings of the 37th Annual ACM Symposium on User Interface Software and Technology10.1145/3654777.3676407(1-16)Online publication date: 13-Oct-2024
https://dl.acm.org/doi/10.1145/3654777.3676407
Das Antar AMolaei SChen YLee MBanovic N(2024)VIME: Visual Interactive Model Explorer for Identifying Capabilities and Limitations of Machine Learning Models for Sequential Decision-MakingProceedings of the 37th Annual ACM Symposium on User Interface Software and Technology10.1145/3654777.3676323(1-21)Online publication date: 13-Oct-2024
https://dl.acm.org/doi/10.1145/3654777.3676323
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Index Terms

Gamut: A Design Probe to Understand How Data Scientists Understand Machine Learning Models
1. Computing methodologies
  1. Machine learning
2. Human-centered computing
  1. Visualization
    1. Empirical studies in visualization
    2. Visualization systems and tools

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cover image ACM Conferences

CHI '19: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

May 2019

9077 pages

ISBN:9781450359702

DOI:10.1145/3290605

General Chairs:
Stephen Brewster
University of Glasgow, Scotland, UK
,
Geraldine Fitzpatrick
TU Wien, Austria
,
Program Chairs:
Anna Cox
University College London, UK
,
Vassilis Kostakos
University of Melbourne, Australia

Copyright © 2019 ACM.

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Published: 02 May 2019

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SIGCHI

CHI '19: CHI Conference on Human Factors in Computing Systems

May 4 - 9, 2019

Glasgow, Scotland Uk

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CHI '19 Paper Acceptance Rate 703 of 2,958 submissions, 24%;

Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

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Cited By

Meyer LZhu J(2024)Slide to Explore 'What If': An Analysis of Explainable InterfacesAdjunct Proceedings of the 2024 Nordic Conference on Human-Computer Interaction10.1145/3677045.3685416(1-6)Online publication date: 13-Oct-2024
https://dl.acm.org/doi/10.1145/3677045.3685416
Goldstein HTao JHatfield-Dodds ZPierce BHead A(2024)Tyche: Making Sense of PBT EffectivenessProceedings of the 37th Annual ACM Symposium on User Interface Software and Technology10.1145/3654777.3676407(1-16)Online publication date: 13-Oct-2024
https://dl.acm.org/doi/10.1145/3654777.3676407
Das Antar AMolaei SChen YLee MBanovic N(2024)VIME: Visual Interactive Model Explorer for Identifying Capabilities and Limitations of Machine Learning Models for Sequential Decision-MakingProceedings of the 37th Annual ACM Symposium on User Interface Software and Technology10.1145/3654777.3676323(1-21)Online publication date: 13-Oct-2024
https://dl.acm.org/doi/10.1145/3654777.3676323
Benjamin JLindley JEdwards ERubegni EKorjakow TGrist DSharkey R(2024)Responding to Generative AI Technologies with Research-through-Design: The Ryelands AI Lab as an Exploratory StudyProceedings of the 2024 ACM Designing Interactive Systems Conference10.1145/3643834.3660677(1823-1841)Online publication date: 1-Jul-2024
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Kaur HConrad MRule DLampe CGilbert E(2024)Interpretability Gone Bad: The Role of Bounded Rationality in How Practitioners Understand Machine LearningProceedings of the ACM on Human-Computer Interaction10.1145/36373548:CSCW1(1-34)Online publication date: 26-Apr-2024
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Okolo CAgarwal DDell NVashistha A(2024)"If it is easy to understand then it will have value": Examining Perceptions of Explainable AI with Community Health Workers in Rural IndiaProceedings of the ACM on Human-Computer Interaction10.1145/36373488:CSCW1(1-28)Online publication date: 26-Apr-2024
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Nahar NRowlett JBray MOmar ZPapademetris XMenon AKästner C(2024)Regulating Explainability in Machine Learning Applications -- Observations from a Policy Design ExperimentProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3659028(2101-2112)Online publication date: 3-Jun-2024
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Deck LSchoeffer JDe-Arteaga MKühl N(2024)A Critical Survey on Fairness Benefits of Explainable AIProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658990(1579-1595)Online publication date: 3-Jun-2024
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