Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AI

TL;DR

This study explores data scientists' perceptions of AutoAI, revealing mixed feelings about automation's impact but optimism about human-AI collaboration shaping future data science practices.

Contribution

It provides qualitative insights into data scientists' attitudes towards AutoAI and highlights the potential for collaborative human-AI workflows in data science.

Findings

Data scientists have mixed feelings about AutoAI automation.

They believe collaboration between humans and AI is essential.

Most see automation as inevitable but manageable.

Abstract

The rapid advancement of artificial intelligence (AI) is changing our lives in many ways. One application domain is data science. New techniques in automating the creation of AI, known as AutoAI or AutoML, aim to automate the work practices of data scientists. AutoAI systems are capable of autonomously ingesting and pre-processing data, engineering new features, and creating and scoring models based on a target objectives (e.g. accuracy or run-time efficiency). Though not yet widely adopted, we are interested in understanding how AutoAI will impact the practice of data science. We conducted interviews with 20 data scientists who work at a large, multinational technology company and practice data science in various business settings. Our goal is to understand their current work practices and how these practices might change with AutoAI. Reactions were mixed: while informants expressed concerns about the trend of automating their jobs, they also strongly felt it was inevitable. Despite these concerns, they remained optimistic about their future job security due to a view that the future of data science work will be a collaboration between humans and AI systems, in which both automation and human expertise are indispensable.