Human-Centered AI for Data Science: A Systematic Approach

TL;DR

This paper presents a systematic approach to Human-Centered AI in Data Science, focusing on designing AI tools that support human tasks while maintaining human control, through research projects on AutoML systems.

Contribution

It introduces a three-step research methodology and practical implementation strategies for Human-Centered AI in Data Science, emphasizing collaboration between humans and AI.

Findings

Proposes a systematic explore-build-integrate approach.

Develops practical methods for HCAI system implementation.

Highlights the importance of human-AI collaboration in Data Science.

Abstract

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.