Ilmari Hirvonen

Facial analysis AI applications have recently come under heavy criticism. They have been accused, for instance, of being discriminative, racist, and even pseudoscientific. In this paper, we assess the latter accusation. We argue that, in problematic cases, facial analysis AI should be considered a form of social pseudotechnology, although it might not be pseudoscientific. We also propose a new two-part definition of pseudotechnology: Either it is not justified that the pseudotechnology could achieve its intended practical purpose better than random chance through its operation principles, or it can achieve its purpose, but it is not justified that this happens through the features claimed to be crucial for its functioning. Stricter definitions of pseudotechnology have difficulty capturing such phenomena as facial analysis AI. When our definition is extended to social technologies, it becomes clear that social pseudotechnologies are more common than previously suggested. Because social pseudotechnologies influence how people act and think, they are not often perceived as pseudotechnologies. This resembles the phenomenon of reactivity, where scientific classifications affect people’s self-perceptions and perceptions of others. We support our claims with case studies of facial analysis AI technologies and pseudotechnologies. We engage with the current computer, behavioural, and social scientific literature to assess the extent to which it is justified to use facial analysis AI systems in these cases.

This paper reviews how research on the demarcation problem has developed, starting from Popper’s criterion of falsifiability and ending with recent naturalistically oriented approaches. The main differences between traditional and contemporary approaches to the problem are explicated in terms of six postulates called the traditional assumptions. It is argued that all of the assumptions can be dismissed without giving up on the demarcation problem and that doing so might benefit further discussions on pseudoscience. Four present-day research movements on evaluating the boundaries of science are introduced: (1) philosophy of pseudoscience, (2) social epistemology of dissent, (3) agnotology, and (4) evaluation of expertise. Researchers working within these areas have abandoned some or all traditional assumptions.

This paper argues that the main global critiques of scientism lose their punch because they rely on an uncharitable definition of their target. It focuses on epistemological scientism and divides it into four categories in terms of how strong (science is the only source of knowledge) or weak (science is the best source of knowledge) and how narrow (only natural sciences) or broad (all sciences or at least not only the natural sciences) they are. Two central arguments against scientism, the (false) dilemma and self‐referential incoherence, are analysed. Of the four types of epistemological scientism, three can deal with these counterarguments by utilizing two methodological principles: epistemic evaluability of reliability and epistemic opportunism. One hopes that these considerations will steer the discussion on scientism to more fruitful pastures in the future. For example, there are interesting methodological considerations concerning what evaluability or reliability and epistemic opportunism entail.