How AI Developers Overcome Communication Challenges in a Multidisciplinary Team: A Case Study
David Piorkowski, Soya Park, April Yi Wang, Dakuo Wang, Michael J. Muller, Felix Portnoy
Abstract
The development of AI applications is a multidisciplinary effort, involving multiple roles collaborating with the AI developers, an umbrella term we use to include data scientists and other AI-adjacent roles on the same team. During these collaborations, there is a knowledge mismatch between AI developers, who are skilled in data science, and external stakeholders who are typically not. This difference leads to communication gaps, and the onus falls on AI developers to explain data science concepts to their collaborators. In this paper, we report on a study including analyses of both interviews with AI developers and artifacts they produced for communication.
Using the analytic lens of shared mental models, we report on the types of communication gaps that AI developers face, how AI developers communicate across disciplinary and organizational boundaries, and how they simultaneously manage issues regarding trust and expectations.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 23e4e5f4-0828-47b2-a99a-6d31d0c5fc4cCited by top-tier papers32
- Assessing the Fairness of AI Systems: AI Practitioners' Processes, Challenges, and Needs for SupportMichael Madaio, Lisa Egede, Hariharan Subramonyam, Jennifer Wortman Vaughan et al.CSCW 2022 · 149 citations
- Collaboration Challenges in Building ML-Enabled Systems: Communication, Documentation, Engineering, and ProcessNadia Nahar, Shurui Zhou, Grace A. Lewis, Christian KästnerICSE 2022 · 122 citations
- Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI GuidebookNur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg et al.CHI 2023 · 103 citations
- Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI ChallengesQiaosi Wang, Michael Madaio, Shaun K. Kane, Shivani Kapania et al.CHI 2023 · 90 citations
- Multimodal Healthcare AI: Identifying and Designing Clinically Relevant Vision-Language Applications for RadiologyNur Yildirim, Hannah Richardson, Maria Teodora Wetscherek, Junaid Bajwa et al.CHI 2024 · 81 citations
Builds on3
- How do Data Science Workers Collaborate? Roles, Workflows, and ToolsAmy X. Zhang, Michael J. Muller, Dakuo WangCSCW 2020 · 260 citations
- AutoDS: Towards Human-Centered Automation of Data ScienceDakuo Wang, Josh Andres, Justin D. Weisz, Erick Oduor et al.CHI 2021 · 77 citations
- Callisto: Capturing the "Why" by Connecting Conversations with Computational NarrativesApril Yi Wang, Zihan Wu, Christopher Brooks, Steve OneyCHI 2020 · 44 citations
Related papers
- Analyzing Collaborative Challenges and Needs of UX Practitioners when Designing with AI/MLMeena Devii Muralikumar, David W. McDonaldCSCW 2024 · 6 citations
- Coordination Mechanisms in AI Development: Practitioner Experiences on Integrating UX ActivitiesAnders Bruun, Niels van Berkel, Dimitrios Raptis, Effie L.-C. LawCHI 2025 · 2 citations
- Human Factors in Model Interpretability: Industry Practices, Challenges, and NeedsSungsoo Ray Hong, Jessica Hullman, Enrico BertiniCSCW 2020 · 219 citations
- Solving Separation-of-Concerns Problems in Collaborative Design of Human-AI Systems through Leaky AbstractionsHariharan Subramonyam, Jane Im, Colleen M. Seifert, Eytan AdarCHI 2022 · 55 citations
- Bridging Knowledge Gaps in Clinical AI: An Activity Theory Perspective on Interdisciplinary Data Work for TelehealthBingsheng Yao, Yao Du, Yue Fu, Xuhai Xu et al.CSCW 2026
