# Towards Multifaceted Human-Centered AI

arXiv: https://arxiv.org/abs/2301.03656 · HTML: https://mlsanity.com/paper/2301.03656/

## 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.

## Builds on (most cited first, 10 of 10)

- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AI](https://mlsanity.com/paper/1909.02309/) (2019)
- [Human Factors in Model Interpretability: Industry Practices, Challenges, and Needs](https://mlsanity.com/paper/2004.11440/) (2020)
- [How do Data Science Workers Collaborate? Roles, Workflows, and Tools](https://mlsanity.com/paper/2001.06684/) (2020)
- [Symphony: Composing Interactive Interfaces for Machine Learning](https://mlsanity.com/paper/2202.08946/) (2022)
- [Towards Scalable Dataframe Systems](https://mlsanity.com/paper/2001.00888/) (2020)
- [mage: Fluid Moves Between Code and Graphical Work in Computational Notebooks](https://mlsanity.com/paper/2009.10643/) (2020)
- [Goals, Process, and Challenges of Exploratory Data Analysis: An Interview Study](https://mlsanity.com/paper/1911.00568/) (2019)
- [Data Vision: Learning to See Through Algorithmic Abstraction](https://mlsanity.com/paper/2002.03387/) (2020)
- [Leam: An Interactive System for In-situ Visual Text Analysis](https://mlsanity.com/paper/2009.03520/) (2020)

## 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.