Human-AI Collaboration in Data Science

TLDR

Rapid AI advancement is transforming many aspects of life, and data science is a key application domain where new AutoAI/AutoML techniques aim to automate data scientists’ work practices. The study seeks to understand how AutoAI will affect data science practice by examining current work practices and potential changes. The authors interviewed 20 data scientists at a multinational tech company, describing AutoAI systems that autonomously ingest, preprocess, engineer features, and score models based on objectives such as accuracy or runtime efficiency. Participants expressed mixed reactions, fearing job automation yet believing it inevitable, yet they remained optimistic that future data science will involve a human‑AI collaboration where both automation and expertise are essential.

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.