Publikation
Human-in-the-loop or AI-in-the-loop? Automate or Collaborate?
Sriraam Natarajan; Saurabh Mathur; Sahil Sidheekh; Wolfgang Stammer; Kristian Kersting
In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2412.14232, Pages 1-7, arXiv, 2024.
Zusammenfassung
Human-in-the-loop (HIL) systems have emerged as a promis-
ing approach for combining the strengths of data-driven ma-
chine 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) com-
ponent’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 com-
prehensive systems that faithfully model the intricate inter-
play between the human and machine components, leading
to more effective and robust AI systems.