We endorse policymakers’ efforts to address the negative consequences of the attention economy’s technology but add that these approaches are often limited in their criticism of the systemic context of human attention. Starting from Buddhist philosophy, we advocate a broader approach: an ‘ecology of attending’ that centers on conceptualizing, designing, and using attention (1) in an embedded way and (2) focused on the alleviating of suffering. With ‘embedded’ we mean that attention is not a neutral, isolated mechanism but a meaning-engendering part of an ‘ecology’ of bodily, sociotechnical and moral frameworks. With ‘focused on the alleviation of suffering’ we mean that we explicitly move away from the (often implicit) conception of attention as a tool for gratifying desires. We analyze existing inquiries in these directions and urge them to be intensified and integrated. As to the design and function of our technological environment, we propose three questions for further research: How can technology help to acknowledge us as ‘ecological’ beings, rather than as self-sufficient individuals? How can technology help to raise awareness of our moral framework? And how can technology increase the conditions for ‘attending’ to the alleviation of suffering, by substituting our covert self-driven moral framework with an ecologically attending one? We believe in the urgency of transforming the inhumane attention economy sociotechnical system into a humane ecology of attending, and in our ability to contribute to it.

This workshop summary on natural language processing (NLP) markers for psychosis and other psychiatric disorders presents some of the clinical and research issues that NLP markers might address and some of the activities needed to move in that direction. We propose that the optimal development of NLP markers would occur in the context of research efforts to map out the underlying mechanisms of psychosis and other disorders. In this workshop, we identified some of the challenges to be addressed in developing and implementing NLP markers-based Clinical Decision Support Systems (CDSSs) in psychiatric practice, especially with respect to psychosis. Of note, a CDSS is meant to enhance decision-making by clinicians by providing additional relevant information primarily through software (although CDSSs are not without risks). In psychiatry, a field that relies on subjective clinical ratings that condense rich temporal behavioral information, the inclusion of computational quantitative NLP markers can plausibly lead to operationalized decision models in place of idiosyncratic ones, although ethical issues must always be paramount.