Modeling needs user modeling.
AI assistance
human-centric artificial intelligence
human–AI collaboration
human–AI interaction
machine learning
probabilistic modeling
user modeling
Journal
Frontiers in artificial intelligence
ISSN: 2624-8212
Titre abrégé: Front Artif Intell
Pays: Switzerland
ID NLM: 101770551
Informations de publication
Date de publication:
2023
2023
Historique:
received:
14
11
2022
accepted:
24
03
2023
medline:
24
4
2023
pubmed:
24
4
2023
entrez:
24
04
2023
Statut:
epublish
Résumé
Modeling has actively tried to take the human out of the loop, originally for objectivity and recently also for automation. We argue that an unnecessary side effect has been that modeling workflows and machine learning pipelines have become restricted to only well-specified problems. Putting the humans back into the models would enable modeling a broader set of problems, through iterative modeling processes in which AI can offer collaborative assistance. However, this requires advances in how we scope our modeling problems, and in the user models. In this perspective article, we characterize the required user models and the challenges ahead for realizing this vision, which would enable new interactive modeling workflows, and human-centric or human-compatible machine learning pipelines.
Identifiants
pubmed: 37091302
doi: 10.3389/frai.2023.1097891
pmc: PMC10116056
doi:
Types de publication
Journal Article
Langues
eng
Pagination
1097891Informations de copyright
Copyright © 2023 Çelikok, Murena and Kaski.
Déclaration de conflit d'intérêts
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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