Human-in-the-loop or AI-in-the-loop? Automate or Collaborate?

TL;DR

This paper redefines human-in-the-loop systems as AI-in-the-loop systems, emphasizing the active role of humans in controlling AI-supported systems and proposing a new evaluation perspective that better captures human-machine collaboration.

Contribution

It introduces the AI-in-the-loop perspective, challenging existing evaluation methods, and advocates for more comprehensive models of human-AI interaction.

Findings

Existing evaluations overemphasize machine performance

Humans actively influence system outcomes

AI-in-the-loop perspective improves system robustness

Abstract

Human-in-the-loop (HIL) systems have emerged as a promising approach for combining the strengths of data-driven machine learning models with the contextual understanding of human experts. However, a deeper look into several of these systems reveals that calling them HIL would be a misnomer, as they are quite the opposite, namely AI-in-the-loop ($AI^2L$) systems, where the human is in control of the system, while the AI is there to support the human. We argue that existing evaluation methods often overemphasize the machine (learning) component's performance, neglecting the human expert's critical role. Consequently, we propose an $AI^2L$ perspective, which recognizes that the human expert is an active participant in the system, significantly influencing its overall performance. By adopting an $AI^2L$ approach, we can develop more comprehensive systems that faithfully model the intricate interplay between the human and machine components, leading to more effective and robust AI systems.