Çelikok Mustafa Mert, Murena Pierre-Alexandre, Kaski Samuel
Department of Computer Science, Aalto University, Espoo, Finland.
Department of Computer Science, University of Manchester, Manchester, United Kingdom.
Front Artif Intell. 2023 Apr 6;6:1097891. doi: 10.3389/frai.2023.1097891. eCollection 2023.
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.
