Human-in-the-loop or AI-in-the-loop? Automate or Collaborate?
Sriraam Natarajan, Saurabh Mathur, Sahil Sidheekh, Wolfgang Stammer, Kristian Kersting
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 (AI2L) systems: 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 AI2L perspective, which recognizes that the human expert is an active participant in the system, significantly influencing its overall performance. By adopting an AI2L 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.
BibTeX
@article{Natarajan_Mathur_Sidheekh_Stammer_Kersting_2025, title={Human-in-the-loop or AI-in-the-loop? Automate or Collaborate?}, volume={39}, url={https://ojs.aaai.org/index.php/AAAI/article/view/35083}, DOI={10.1609/aaai.v39i27.35083}, abstractNote={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 (AI2L) systems: 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 AI2L perspective, which recognizes that the human expert is an active participant in the system, significantly influencing its overall performance. By adopting an AI2L 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.}, number={27}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Natarajan, Sriraam and Mathur, Saurabh and Sidheekh, Sahil and Stammer, Wolfgang and Kersting, Kristian}, year={2025}, month={Apr.}, pages={28594-28600} }