AI Summary: This article examines the epistemic tension between the "felt self," rooted in personal experience, and the "datafied self," shaped by algorithmic modeling within healthcare digital twin environments. Utilizing Jung's concepts of the Self and the Shadow, the authors argue that these digital systems can obscure important aspects of health and identity while also revealing uncomfortable truths about individual health behaviors. The study highlights the dual potential of digital twins to either enhance self-awareness through data insights or exacerbate self-surveillance and undermine personal autonomy, emphasizing the importance of design and governance in shaping these outcomes. By framing healthcare digital twins as complex socio-technical systems, the authors contribute to ongoing discussions about identity and agency in the context of AI and healthcare.
Drivers of trust in AI across the domains of finance, law, and healthcare: a conjoint study
AI Summary: A conjoint analysis of trust in AI-supported decision-making across healthcare, legal, and financial domains reveals that while all attributes influence trust, their relative importance varies significantly by domain. In healthcare, precision and responsibility are paramount, reflecting the critical nature of clinical decisions, while transparency is also valued, and explainability ranks lowest. Conversely, in the legal domain, procedural attributes such as responsibility, transparency, and explainability dominate, emphasizing the importance of due process and human involvement in decision-making. In finance, trust is shaped by a balance of responsibility, voluntariness, and precision, highlighting the need for human accountability and discretion in financial outcomes.