TRACE: Governing AI-Assisted Research with Principles from Computer Science

Abstract

Artificial intelligence (AI) makes it easier than ever to produce research outputs. The pressing question is how researchers should organize their work with this technology. The question is not new: software engineering and security research have long studied how teams of people and machines keep control of a shared body of work with many contributors. We distil principles from the practices these literatures report and transpose them to AI-assisted research. The principles form TRACE: Track records, Review against sources, Accept independently, Control transitions, and Evaluate corrections. Together, the five principles give researchers a common basis for remaining in control while integrating AI into their workflows. By bounding units of work and attending to how each is shaped, they help researchers pace AI-assisted work to human attention rather than to the speed at which AI produces content. We also introduce TRACE-OS, an open source operating system for AI-assisted research projects. It puts the principles into practice through shared instructions, tools, and records that fit researchers' existing workflows. The shared standard can contribute to building trust, which allows teams to delegate and realize the productivity that AI promises. More broadly, we aim to open a conversation about the governing principles through which the research community can benefit from AI.