mlsanityData to 7 Oct 2026

How AI Developers Overcome Communication Challenges in a Multidisciplinary Team: A Case Study

arXiv 2101.06098 ↗First posted 13 Jan 2021cs.CY cs.AIcited by 29

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 29 papers in the map (data through 7 Oct 2026). Builds on 14 of its 73 references that are in the map. First posted on arXiv 13 Jan 2021.

Builds on

14 of 73 references.

References · 14

  1. 2018Datasheets for Datasetscited by 1,371
  2. 2018Model Cards for Model Reportingcited by 1,068
  3. 2020Learning to Complement Humanscited by 123
  4. 2019Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AIcited by 79
  5. 2020How do Data Science Workers Collaborate? Roles, Workflows, and Toolscited by 64
  6. 2019Unremarkable AI: Fitting Intelligent Decision Support into Critical, Clinical Decision-Making Processescited by 53
  7. 2018Software Engineering Challenges of Deep Learningcited by 41
  8. 2019Studying Software Engineering Patterns for Designing Machine Learning Systemscited by 24
  9. 2021How Much Automation Does a Data Scientist Want?cited by 20
  10. 2019Experiences with Improving the Transparency of AI Models and Servicescited by 14
  11. 2014DataHub: Collaborative Data Science & Dataset Version Management at Scalecited by 10
  12. 2019Machine Teaching by Domain Experts: Towards More Humane,Inclusive, and Intelligent Machine Learning Systemscited by 5
2 more
  1. 2017The Many Faces of Data-centric Workflow Optimization: A Surveycited by 3
  2. 2019Preliminary Systematic Literature Review of Machine Learning System Development Processcited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 5; 2022: 6; 2023: 10; 2024: 1; 2025: 4; 2026: 3.

29 papers.

Sort:

By area:

  1. 2021Assessing the Fairness of AI Systems: AI Practitioners' Processes, Challenges, and Needs for Supportcited by 61
  2. 2021Collaboration Challenges in Building ML-Enabled Systems: Communication, Documentation, Engineering, and Processcited by 56
  3. 2025On the Use of Agentic Coding: An Empirical Study of Pull Requests on GitHubcited by 53
  4. 2021D2S: Document-to-Slide Generation Via Query-Based Text Summarizationcited by 48
  5. 2022Investigating Explainability of Generative AI for Code through Scenario-based Designcited by 47
  6. 2023A Meta-Summary of Challenges in Building Products with ML Components -- Collecting Experiences from 4758+ Practitionerscited by 22
  7. 2023Model Compression in Practice: Lessons Learned from Practitioners Creating On-device Machine Learning Experiencescited by 21
  8. 2022Telling Stories from Computational Notebooks: AI-Assisted Presentation Slides Creation for Presenting Data Science Workcited by 15
  9. 2023Model Sketching: Centering Concepts in Early-Stage Machine Learning Model Designcited by 15
  10. 2023Investigating Practices and Opportunities for Cross-functional Collaboration around AI Fairness in Industry Practicecited by 15
  11. 2021Facilitating Knowledge Sharing from Domain Experts to Data Scientists for Building NLP Modelscited by 12
  12. 2021Model LineUpper: Supporting Interactive Model Comparison at Multiple Levels for AutoMLcited by 12
17 more
  1. 2022A.I. Robustness: a Human-Centered Perspective on Technological Challenges and Opportunitiescited by 8
  2. 2022Requirements Engineering for Machine Learning: A Review and Reflectioncited by 7
  3. 2023Why is AI not a Panacea for Data Workers? An Interview Study on Human-AI Collaboration in Data Storytellingcited by 7
  4. 2023Is a Seat at the Table Enough? Engaging Teachers and Students in Dataset Specification for ML in Educationcited by 5
  5. 2022Evaluating a Methodology for Increasing AI Transparency: A Case Studycited by 3
  6. 2023The Value-Sensitive Conversational Agent Co-Design Frameworkcited by 3
  7. 2023Bridging MDE and AI: A Systematic Review of Domain-Specific Languages and Model-Driven Practices in AI Software Systems Engineeringcited by 2
  8. 2024Developing and Evaluating a Design Method for Positive Artificial Intelligencecited by 2
  9. 2022Attention is All They Need: Exploring the Media Archaeology of the Computer Vision Research Papercited by 1
  10. 2025AI LEGO: Scaffolding Cross-Functional Collaboration in Industrial Responsible AI Practices during Early Design Stagescited by 1
  11. 2023A Case Study on AI Engineering Practices: Developing an Autonomous Stock Trading Systemcited by 0
  12. 2023"We'll have to see how it works": An interview study to understand collaborative practices in interdisciplinary artificial intelligence and healthcare researchcited by 0
  13. 2025Bridging the Communication Gap: Evaluating AI Labeling Practices for Trustworthy AI Developmentcited by 0
  14. 2025Is PMBOK Guide the Right Fit for AI? Re-evaluating Project Management in the Face of Artificial Intelligence Projectscited by 0
  15. 2026LLM-Powered Workflow Optimization for Multidisciplinary Software Development: An Automotive Industry Case Studycited by 0
  16. 2026Exploring CoCo Challenges in ML Engineering Teams: Insights From the Semiconductor Industrycited by 0
  17. 2026Detecting Soft Skills in ML Engineering Roles CVscited by 0