mlsanityData to 14 Oct 2026

Human-Centered AI for Data Science: A Systematic Approach

arXiv 2110.01108 ↗First posted 3 Oct 2021cs.LG cs.AI cs.HCcited by 2

Not on the map. Why?

Abstract (arXiv)

Human-Centered AI (HCAI) refers to the research effort that aims to design and implement AI techniques to support various human tasks, while taking human needs into consideration and preserving human control. In this short position paper, we illustrate how we approach HCAI using a series of research projects around Data Science (DS) works as a case study. The AI techniques built for supporting DS works are collectively referred to as AutoML systems, and their goals are to automate some parts of the DS workflow. We illustrate a three-step systematical research approach(i.e., explore, build, and integrate) and four practical ways of implementation for HCAI systems. We argue that our work is a cornerstone towards the ultimate future of Human-AI Collaboration for DS and beyond, where AI and humans can take complementary and indispensable roles to achieve a better outcome and experience.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 10 of its 17 references that are in the map. First posted on arXiv 3 Oct 2021.

Builds on

10 of 17 references.

References · 10

  1. 2020Beyond Accuracy: Behavioral Testing of NLP models with CheckListcited by 1,006
  2. 2019AutoML: A Survey of the State-of-the-Artcited by 390
  3. 2019Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AIcited by 79
  4. 2020How do Data Science Workers Collaborate? Roles, Workflows, and Toolscited by 64
  5. 2019An ADMM Based Framework for AutoML Pipeline Configurationcited by 47
  6. 2020Trust in AutoML: Exploring Information Needs for Establishing Trust in Automated Machine Learning Systemscited by 36
  7. 2021AutoDS: Towards Human-Centered Automation of Data Sciencecited by 32
  8. 2019How Data Scientists Work Together With Domain Experts in Scientific Collaborations: To Find The Right Answer Or To Ask The Right Question?cited by 30
  9. 2021Whither AutoML? Understanding the Role of Automation in Machine Learning Workflowscited by 22
  10. 2021How Much Automation Does a Data Scientist Want?cited by 20

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 1.

2 papers.