Artificial intelligence (AI) has attracted an increasing amount of attention, both positive and negative. Its potential applications in healthcare are indeed manifold and revolutionary, and within the realm of medical imaging and radiology (which will be the focus of this paper), significant increases in accuracy and speed, as well as significant savings in cost, stand to be gained through the adoption of this technology. Because of its novelty, a norm of keeping humans “in the loop” wherever AI mechanisms are deployed (...) has become synonymous with good ethical practice in some circles. It has been argued that keeping humans “in the loop” is important for reasons of safety, accountability, and the maintenance of institutional trust. However, as the application of machine learning for the detection of lumbar spinal stenosis (LSS) in this paper’s case study reveals, there are some scenarios where an insistence on keeping humans in the loop (or in other words, the resistance to automation) seems unwarranted and could possibly lead us to miss out on very real and important opportunities in healthcare—particularly in low-resource settings. It is important to acknowledge these opportunity costs of resisting automation in such contexts, where better options may be unavailable. Using an AI model based on convolutional neural networks developed by a team of researchers at NUH/NUS medical school in Singapore for automated detection and classification of the lumbar spinal canal, lateral recess, and neural foraminal narrowing in an MRI scan of the spine to diagnose LSS, we will aim to demonstrate that where certain criteria hold (e.g., the AI is as accurate or better than human experts, risks are low in the event of an error, the gain in wellbeing is significant, and the task being automated is not essentially or importantly human), it is both morally permissible and even desirable to kick the humans out of the loop. (shrink)

The current debate on artificial intelligence (AI) tends to associate AI imaginaries with the vision of a future technology capable of emulating or surpassing human intelligence. This article advocates for a more nuanced analysis of AI imaginaries, distinguishing “strong AI narratives,” i.e., narratives that envision futurable AI technologies that are virtually indistinguishable from humans, from "weak" AI narratives, i.e., narratives that discuss and make sense of the functioning and implications of existing AI technologies. Drawing on the academic literature on AI (...) narratives and imaginaries and examining examples drawn from the debate on Large Language Models and public policy, we underscore the critical role and interplay of weak and strong AI across public/private and fictional/non-fictional discourses. The resulting analytical framework aims to empower approaches that are more sensitive to the heterogeneity of AI narratives while also advocating normalising AI narratives, i.e., positioning weak AI narratives more firmly at the center stage of public debates about emerging technologies. (shrink)

Conversational agents (CA) are thought to be promising for psychotherapy because they give the impression of being able to engage in conversations with human users. However, given the high risk for therapy patients who are already in a vulnerable situation, there is a need to investigate the extent to which CA are able to contribute to therapy goals and to discuss CA’s limitations, especially in complex cases. In this paper, we understand psychotherapy as a way of dealing with existential situations (...) and position CAs in the context of the therapeutic experience of patients. This experience is determined by the patient’s unique personal context and specific therapy goals. We suggest that psychotherapy is a fundamentally dialogical activity, because it crucially involves work on the self and one’s self-narrative. This brings us to our central question: is it possible for CAs to engage in a productive therapeutic dialogue, given their limitations as epistemic agents? We will discuss several of those limitations, show how these undermine the possibility of engaging in a therapeutic dialogue, and illustrate those limitations through discussions of the cases of grief and abuse. (shrink)

Artificial intelligence and automation have become pervasive in news media, influencing journalism from news gathering to news distribution. As algorithms are increasingly determining editorial decisions, specific concerns have been raised with regard to the responsible and accountable use of AI-driven tools by news media, encompassing new regulatory and ethical questions. This contribution aims to analyze whether and to what extent the use of AI technology in news media and journalism is currently regulated and debated within the European Union and the (...) Council of Europe. Through a document analysis of official policy documents, combined with a data mining approach and an inductive thematic analysis, the study looks at how news media are dealt with, in particular regarding their responsibilities towards their users and society. The findings show that regulatory frameworks about AI rarely include media, but if they do, they associate them with issues such as disinformation, data, and AI literacy, as well as diversity, plurality, and social responsibility. (shrink)

When Lisanne Bainbridge wrote about counterintuitive consequences of the increasing human–machine interaction, she concentrated on the resulting issues for system performance, stability, and safety. Now, decades later, however, the automized work environment is substantially more pervasive, sophisticated, and interactive. Current advances in machine learning technologies reshape the value, meaning, and future of the human workforce. While the ‘human factor’ still challenges automation system architects, inconspicuously new ironic settings have evolved that only become distinctly evident from a human-centered perspective. This brief (...) essay discusses the role of the human workforce in human–machine interaction as machine learning continues to improve, and it points to the counterintuitive insight that although the demand for blue-collar workers may decrease, exactly this labor class increasingly enters more privileged working domains and establishes itself henceforth as ‘blue collar with tie.’. (shrink)