Can Implicit Theory Influence Construal Level?

Beliefs about stability and change are captured by individuals’ implicit theories. Incremental‐theorists believe that human traits and world‐dispositions are malleable and can change through effort, whereas entity‐theorists believe that human traits and world‐dispositions are fixed. In this research we find that the implicit theory an individual holds influences an important aspect of the cognitive process, namely, the level of construal at which information is processed. In three studies we demonstrate that, compared to entity‐theorists, incremental‐theorists adopt a more abstract level of information construal, and the increased cognitive flexibility afforded by an incremental‐theory mindset explains this effect.     

“Alexa,who am I?”: Voice Assistants and Hermeneutic Lemniscate as the Technologically Mediated Sense-Making

Human Studies - In this paper, I argue that AI-powered voice assistants, just as all technologies, actively mediate our interpretative structures, including values. I show this by explaining the...     

Succession and survival in psychotherapy organizations

Abstract: This paper examines the world of psychotherapy by applying a systemic and psychodynamic understanding of the family business as a way of understanding the dilemmas and challenges of leadership succession. Oedipal factors are explored as an important theme within the succession process. This exploration is set within the context of what function psychotherapy has performed in society over the last thirty years. The hypothesis is that the first generation of leaders aimed to provide containment for the individual citizen at a time of failed dependency in society. The suggestion is that this gave way to the primary task for the second generation, which has been to focus on the therapist in training. The challenge for the third generation is to develop a meaningful role for psychotherapy today and to ensure survival at a time when other shorter therapies such as CBT are gaining ascendancy over longer term psychoanalytic psychotherapy.     

What is morally at stake when using algorithms to make medical diagnoses? Expanding the discussion beyond risks and harms

In this paper, we examine the qualitative moral impact of machine learning-based clinical decision support systems in the process of medical diagnosis. To date, discussions about machine learning in this context have focused on problems that can be measured and assessed quantitatively, such as by estimating the extent of potential harm or calculating incurred risks. We maintain that such discussions neglect the qualitative moral impact of these technologies. Drawing on the philosophical approaches of technomoral change and technological mediation theory, which explore the interplay between technologies and morality, we present an analysis of concerns related to the adoption of machine learning-aided medical diagnosis. We analyze anticipated moral issues that machine learning systems pose for different stakeholders, such as bias and opacity in the way that models are trained to produce diagnoses, changes to how health care providers, patients, and developers understand their roles and professions, and challenges to existing forms of medical legislation. Albeit preliminary in nature, the insights offered by the technomoral change and the technological mediation approaches expand and enrich the current discussion about machine learning in diagnostic practices, bringing distinct and currently underexplored areas of concern to the forefront. These insights can contribute to a more encompassing and better informed decision-making process when adapting machine learning techniques to medical diagnosis, while acknowledging the interests of multiple stakeholders and the active role that technologies play in generating, perpetuating, and modifying ethical concerns in health care.