Binding the Person-Specific Approach to Modern AI in the Human Screenome Project: Moving past Generalizability to Transferability.

Journal: Multivariate behavioral research
Published Date:

Abstract

Advances in ability to comprehensively record individuals' digital lives and in AI modeling of those data facilitate new possibilities for describing, predicting, and generating a wide variety of behavioral processes. In this paper, we consider these advances from a person-specific perspective, including whether the pervasive concerns about of results might be productively reframed with respect to of models, and how self-supervision and new deep neural network architectures that facilitate transfer learning can be applied in a person-specific way to the super-intensive longitudinal data arriving in the Human Screenome Project. In developing the possibilities, we suggest Molenaar add a statement to the person-specific Manifesto - "In short, the concerns about commonly leveled at the person-specific paradigm are unfounded and can be fully and completely replaced with discussion and demonstrations of ."

Authors

  • Nilam Ram
    Pennsylvania State University, University Park, PA, USA.
  • Nick Haber
    School of Education, Stanford University, Stanford, California.
  • Thomas N Robinson
    Stanford University, Stanford, CA, USA.
  • Byron Reeves
    Stanford University, Stanford, CA, USA.