Experiences with Improving the Transparency of AI Models and Services
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Abstract (arXiv)
AI models and services are used in a growing number of highstakes areas, resulting in a need for increased transparency. Consistent with this, several proposals for higher quality and more consistent documentation of AI data, models, and systems have emerged. Little is known, however, about the needs of those who would produce or consume these new forms of documentation. Through semi-structured developer interviews, and two document creation exercises, we have assembled a clearer picture of these needs and the various challenges faced in creating accurate and useful AI documentation. Based on the observations from this work, supplemented by feedback received during multiple design explorations and stakeholder conversations, we make recommendations for easing the collection and flexible presentation of AI facts to promote transparency.
Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Nov 2019.
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Citing papers per year: 2021: 4; 2022: 5; 2023: 3; 2024: 1; 2025: 1.
14 papers.
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- 2021Socially Responsible AI Algorithms: Issues, Purposes, and Challengescited by 56
- 2022Designing for Responsible Trust in AI Systems: A Communication Perspectivecited by 48
- 2022Investigating Explainability of Generative AI for Code through Scenario-based Designcited by 47
- 2021How AI Developers Overcome Communication Challenges in a Multidisciplinary Team: A Case Studycited by 29
- 2022Explainable Artificial Intelligence (XAI) from a user perspective- A synthesis of prior literature and problematizing avenues for future researchcited by 20
- 2023Designerly Understanding: Information Needs for Model Transparency to Support Design Ideation for AI-Powered User Experiencecited by 18
- 2023"It Felt Like Having a Second Mind": Investigating Human-AI Co-creativity in Prewriting with Large Language Modelscited by 15
- 2022Prescriptive and Descriptive Approaches to Machine-Learning Transparencycited by 7
- 2021Yes We Care! -- Certification for Machine Learning Methods through the Care Label Frameworkcited by 5
- 2023Users are the North Star for AI Transparencycited by 5
- 2021Know Your Model (KYM): Increasing Trust in AI and Machine Learningcited by 4
- 2022Evaluating a Methodology for Increasing AI Transparency: A Case Studycited by 3
2 more
- 2025The Pitfalls of "Security by Obscurity" And What They Mean for Transparent AI30 Jan 2025
- 2024Documentation Practices of Artificial Intelligence26 Jun 2024
- 2023"It Felt Like Having a Second Mind": Investigating Human-AI Co-creativity in Prewriting with Large Language Models20 Jul 2023
- 2023Users are the North Star for AI Transparency9 Mar 2023
- 2023Designerly Understanding: Information Needs for Model Transparency to Support Design Ideation for AI-Powered User Experience21 Feb 2023
- 2022Explainable Artificial Intelligence (XAI) from a user perspective- A synthesis of prior literature and problematizing avenues for future research24 Nov 2022
- 2022Designing for Responsible Trust in AI Systems: A Communication Perspective29 Apr 2022
- 2022Prescriptive and Descriptive Approaches to Machine-Learning Transparency27 Apr 2022
- 2022Investigating Explainability of Generative AI for Code through Scenario-based Design10 Feb 2022
- 2022Evaluating a Methodology for Increasing AI Transparency: A Case Study24 Jan 2022
- 2021Know Your Model (KYM): Increasing Trust in AI and Machine Learning31 May 2021
- 2021Yes We Care! -- Certification for Machine Learning Methods through the Care Label Framework21 May 2021
- 2021Socially Responsible AI Algorithms: Issues, Purposes, and Challengescited by 56
- 2022Designing for Responsible Trust in AI Systems: A Communication Perspectivecited by 48
- 2022Investigating Explainability of Generative AI for Code through Scenario-based Designcited by 47
- 2021How AI Developers Overcome Communication Challenges in a Multidisciplinary Team: A Case Studycited by 29
- 2022Explainable Artificial Intelligence (XAI) from a user perspective- A synthesis of prior literature and problematizing avenues for future researchcited by 20
- 2023Designerly Understanding: Information Needs for Model Transparency to Support Design Ideation for AI-Powered User Experiencecited by 18
- 2023"It Felt Like Having a Second Mind": Investigating Human-AI Co-creativity in Prewriting with Large Language Modelscited by 15
- 2022Prescriptive and Descriptive Approaches to Machine-Learning Transparencycited by 7
- 2021Yes We Care! -- Certification for Machine Learning Methods through the Care Label Frameworkcited by 5
- 2023Users are the North Star for AI Transparencycited by 5
- 2021Know Your Model (KYM): Increasing Trust in AI and Machine Learningcited by 4
- 2022Evaluating a Methodology for Increasing AI Transparency: A Case Studycited by 3
12 most cited of 14 All 14 (JSON)
- 2025The Pitfalls of "Security by Obscurity" And What They Mean for Transparent AI30 Jan 2025
- 2024Documentation Practices of Artificial Intelligence26 Jun 2024
- 2023"It Felt Like Having a Second Mind": Investigating Human-AI Co-creativity in Prewriting with Large Language Models20 Jul 2023
- 2023Users are the North Star for AI Transparency9 Mar 2023
- 2023Designerly Understanding: Information Needs for Model Transparency to Support Design Ideation for AI-Powered User Experience21 Feb 2023
- 2022Explainable Artificial Intelligence (XAI) from a user perspective- A synthesis of prior literature and problematizing avenues for future research24 Nov 2022
- 2022Designing for Responsible Trust in AI Systems: A Communication Perspective29 Apr 2022
- 2022Prescriptive and Descriptive Approaches to Machine-Learning Transparency27 Apr 2022
- 2022Investigating Explainability of Generative AI for Code through Scenario-based Design10 Feb 2022
- 2022Evaluating a Methodology for Increasing AI Transparency: A Case Study24 Jan 2022
- 2021Know Your Model (KYM): Increasing Trust in AI and Machine Learning31 May 2021
- 2021Yes We Care! -- Certification for Machine Learning Methods through the Care Label Framework21 May 2021
12 newest of 14 All 14 (JSON)