The Centers and Margins of Modeling Humans in Well-being Technologies: A Decentering Approach

2025-03-24

AI Summary

This paper critically exposes the implicit bodily normativity embedded in machine learning (ML) modeling for wellbeing technologies—such as assumptions of bodily regularity and rigid health/disease binaries—and their exclusionary consequences for design and practice. Method: Integrating agential realism with critical technical practice, the study conducts a tripartite analysis of disruptive case studies across wellbeing technologies and undertakes reflexive modeling experiments. Contribution/Results: It pioneers the systematic integration of posthumanist, decentered theoretical frameworks into computational modeling of the body and wellbeing. Challenging anthropocentric ML paradigms, the work proposes a modeling approach that acknowledges bodily irregularity, ongoing human–system entanglement, and ontological uncertainty in state transitions. From this, it derives empirically grounded, ethically informed inclusive design principles. The study advances a novel, post-anthropocentric ML paradigm for wellbeing technologies—one that is both methodologically innovative and ethically responsive.

Technology Category

• Humans and AI: Human-in-the-loop Machine Learning
• Machine Learning: Ethics, Bias, and Fairness
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

This paper critically examines the machine learning (ML) modeling of humans in three case studies of well-being technologies. Through a critical technical approach, it examines how these apps were experienced in daily life (technology in use) to surface breakdowns and to identify the assumptions about the"human"body entrenched in the ML models (technology design). To address these issues, this paper applies agential realism to decenter foundational assumptions, such as body regularity and health/illness binaries, and speculates more inclusive design and ML modeling paths that acknowledge irregularity, human-system entanglements, and uncertain transitions. This work is among the first to explore the implications of decentering theories in computational modeling of human bodies and well-being, offering insights for more inclusive technologies and speculations toward posthuman-centered ML modeling.

Problem

• Examines ML modeling assumptions in well-being technologies
• Addresses body regularity and health/illness binary issues
• Proposes inclusive design for human-system entanglements

Innovation

• Applies agential realism to decenter assumptions
• Explores inclusive design acknowledging irregularity
• Speculates posthuman-centered ML modeling paths