Human-in-the-loop or AI-in-the-loop? Automate or Collaborate?
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866916532421918720 |
|---|---|
| author | Natarajan, Sriraam Mathur, Saurabh Sidheekh, Sahil Stammer, Wolfgang Kersting, Kristian |
| author_facet | Natarajan, Sriraam Mathur, Saurabh Sidheekh, Sahil Stammer, Wolfgang Kersting, Kristian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_14232 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Human-in-the-loop or AI-in-the-loop? Automate or Collaborate? Natarajan, Sriraam Mathur, Saurabh Sidheekh, Sahil Stammer, Wolfgang Kersting, Kristian Human-Computer Interaction 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. |
| title | Human-in-the-loop or AI-in-the-loop? Automate or Collaborate? |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2412.14232 |