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"Harry Collins is a highly respected and well-known sociologist of science and one of the leaders in the founding of that field. In a project that befits his stature in science studies and in the broader field of sociology, he has turned to a more general reflection on how he did what he has done, and to drawing lessons from his own experiences over many years--lessons about how to do the kind of thing he has so successfully done himself. In particular, he has thought intensively about methods, about the way he did the work that he is justly renowned for, and especially about the methodological issues that have stirred up so much passionate discussion in sociology. In this book he uses the materials he has produced over so many years of research to draw some basic lessons about how to go about studying collective activities, and especially about what bases our belief that we are learning something useful when we gather whatever kind of data we gather. The goal then here is to provide a comprehensive, critical, and reflexive introduction to interpretative qualitative social science methodology, based on an entire career's worth of professional experience, and told in a similarly accessible style to Collins's other books"--

Startling successes in machine intelligence using 'deep learning' have dramatically raised the stakes in the rise of AI. However, Harry Collins argues that it is still impossible to foresee a time when machines will be sufficiently embedded in society to be independent of human input or when we cannot distinguish between humans and computers--

Much of what humans know we cannot say. And much of what we do we cannot describe. For example, how do we know how to ride a bike when we can't explain how we do it? Abilities like this were called "tacit knowledge" by physical chemist and philosopher Michael Polanyi, but here Harry Collins analyzes the term, and the behavior, in much greater detail, often departing from Polanyi's treatment.In Tacit and Explicit Knowledge, Collins develops a common conceptual language to bridge the concept's disparate domains by explaining explicit knowledge and classifying tacit knowledge. Collins then teases

AbstractRecent developments in artificial intelligence based on neural nets—deep learning and large language models which together I refer to as NEWAI—have resulted in startling improvements in language handling and the potential to keep up with changing human knowledge by learning from the internet. Nevertheless, examples such as ChatGPT, which is a 'large language model', have proved to have no moral compass: they answer queries with fabrications with the same fluency as they provide facts. I try to explain why this is, basing the argument on the sociology of knowledge, particularly social studies of science, notably 'studies of expertise and experience' and the 'fractal model' of society. Learning from the internet is not the same as socialisation: NEWAI has no primary socialisation such as provides the foundations of human moral understanding. Instead, large language models are retrospectively socialised by human intervention in an attempt to align them with societally accepted ethics. Perhaps, as technology advances, large language models could come to understand speech and recognise objects sufficiently well to acquire the equivalent of primary socialisation. In the meantime, we must be vigilant about who is socialising them and be aware of the danger of their socialising us to align with them rather than vice-versa, an eventuality that would lead to the further erosion of the distinction between the true and the false giving further support to populism and fascism.

AbstractThe role of scientific values has taken on new urgency with recent changes in the politics of Western societies. The threat is the erosion of the distinction between true and false in political circles. This could rapidly lead to democracy sliding into populism thence fascism. In the light of this, philosophy and sociology of science should themselves re-examine their role. The main point of the paper is to argue that science could and should push against the erosion of truth in society. Sociological thinking has sometimes tended to erode the difference between science and ordinary thought but it should no longer ignore the political consequences and should, instead, start to take scientific values as a positive resource in society. The philosophical analysis of scientific values, which I will refer to as 'scientific value analysis, or 'SVA', has championed the impact of societal values on science but should also look at the way scientific values could positively affect societal values.

AbstractHere I try to improve on the available answers to certain long-debated questions and set out some consequences for the answers. Are there limits to the extent to which we can understand the conceptual worlds of other human communities and of non-human creatures? How does this question relate to our ability to engage in other cultures' practices and languages? What is meant by 'the body' and what is meant by 'the brain' and how do different meanings bear on the questions? The central answer developed here is that it is possible, given the right circumstances, for a competent human from any human group to understand the culture of any other human group without engaging in their practices though there are barriers when it comes to communication across species. This answer has important social and political consequences and consequences for the debate about artificial intelligence.