research-article

Interactive Model Cards: A Human-Centered Approach to Model Documentation

Authors:

Anamaria Crisan,

Margaret Drouhard,

Nazneen RajaniAuthors Info & Claims

FAccT '22: Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency

Pages 427 - 439

https://doi.org/10.1145/3531146.3533108

Published: 20 June 2022 Publication History

Abstract

Deep learning models for natural language processing (NLP) are increasingly adopted and deployed by analysts without formal training in NLP or machine learning (ML). However, the documentation intended to convey the model’s details and appropriate use is tailored primarily to individuals with ML or NLP expertise. To address this gap, we conduct a design inquiry into interactive model cards, which augment traditionally static model cards with affordances for exploring model documentation and interacting with the models themselves. Our investigation consists of an initial conceptual study with experts in ML, NLP, and AI Ethics, followed by a separate evaluative study with non-expert analysts who use ML models in their work. Using a semi-structured interview format coupled with a think-aloud protocol, we collected feedback from a total of 30 participants who engaged with different versions of standard and interactive model cards. Through a thematic analysis of the collected data, we identified several conceptual dimensions that summarize the strengths and limitations of standard and interactive model cards, including: stakeholders; design; guidance; understandability & interpretability; sensemaking & skepticism; and trust & safety. Our findings demonstrate the importance of carefully considered design and interactivity for orienting and supporting non-expert analysts using deep learning models, along with a need for consideration of broader sociotechnical contexts and organizational dynamics. We have also identified design elements, such as language, visual cues, and warnings, among others, that support interactivity and make non-interactive content accessible. We summarize our findings as design guidelines and discuss their implications for a human-centered approach towards AI/ML documentation.

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Cited By

Raftopoulos M(2024)Designing Tiny Robots: Engaging Stakeholders in Collaborative AI System DesignProceedings of the 27th International Academic Mindtrek Conference10.1145/3681716.3681722(121-132)Online publication date: 8-Oct-2024
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Mickel J(2024)Racial/Ethnic Categories in AI and Algorithmic Fairness: Why They Matter and What They RepresentProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3659050(2484-2494)Online publication date: 3-Jun-2024
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Eyuboglu SGoel KDesai AChen LMonfort MRé CZou J(2024)Model ChangeLists: Characterizing Updates to ML ModelsProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3659047(2432-2453)Online publication date: 3-Jun-2024
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Index Terms

Interactive Model Cards: A Human-Centered Approach to Model Documentation
1. Computing methodologies
  1. Machine learning
2. Human-centered computing
  1. Human computer interaction (HCI)

Index terms have been assigned to the content through auto-classification.

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Cited By

Raftopoulos M(2024)Designing Tiny Robots: Engaging Stakeholders in Collaborative AI System DesignProceedings of the 27th International Academic Mindtrek Conference10.1145/3681716.3681722(121-132)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3681716.3681722
Mickel J(2024)Racial/Ethnic Categories in AI and Algorithmic Fairness: Why They Matter and What They RepresentProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3659050(2484-2494)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3659050
Eyuboglu SGoel KDesai AChen LMonfort MRé CZou J(2024)Model ChangeLists: Characterizing Updates to ML ModelsProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3659047(2432-2453)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3659047
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