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Towards Multifaceted Human-Centered AI

arXiv 2301.03656 ↗First posted 9 Jan 2023cs.DB cs.AI cs.HCcited by 0

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Abstract (arXiv)

Human-centered AI workflows involve stakeholders with multiple roles interacting with each other and automated agents to accomplish diverse tasks. In this paper, we call for a holistic view when designing support mechanisms, such as interaction paradigms, interfaces, and systems, for these multifaceted workflows.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 29 references that are in the map. First posted on arXiv 9 Jan 2023.

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References · 10

  1. 2018Model Cards for Model Reportingcited by 1,080
  2. 2019Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AIcited by 79
  3. 2020Human Factors in Model Interpretability: Industry Practices, Challenges, and Needscited by 74
  4. 2020How do Data Science Workers Collaborate? Roles, Workflows, and Toolscited by 64
  5. 2022Symphony: Composing Interactive Interfaces for Machine Learningcited by 18
  6. 2020Towards Scalable Dataframe Systemscited by 17
  7. 2020mage: Fluid Moves Between Code and Graphical Work in Computational Notebookscited by 13
  8. 2019Goals, Process, and Challenges of Exploratory Data Analysis: An Interview Studycited by 11
  9. 2020Data Vision: Learning to See Through Algorithmic Abstractioncited by 11
  10. 2020Leam: An Interactive System for In-situ Visual Text Analysiscited by 1

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