View source for Brooker2023b

{{BibEntry
|BibType=INCOLLECTION
|Author(s)=Phillip Brooker; Michael Mair;
|Title=Researching Algorithms and Artificial Intelligence
|Editor(s)=William Housley; Adam Edwards; Roser Beneito-Montagut; Richard Fitzgerald;
|Tag(s)=EMCA; AI Reference List; Algorithms; Artificial Intelligence; Ethnomethodology; Programming; Programming-as-Social-Science
|Key=Brooker2023b
|Publisher=SAGE
|Year=2023
|Language=English
|Address=London
|Booktitle=The SAGE Handbook of Digital Society
|Pages=228-246
|URL=https://sk.sagepub.com/reference/the-sage-handbook-of-digital-society/i1879.xml
|DOI=10.4135/9781529783193
|Abstract=Algorithms and artificial intelligence technologies are increasingly commonplace in everyday life - they recommend products to us, classify our photo albums, provide tech support, and more. Confronted with the diffusion of algorithms through our lives, social scientific studies have tended to go in one of two ways. On the one hand, concentrating on pernicious applications of these technologies in big and broad ways; e.g., the power they exert in state surveillance, governance issues around self-driving car disasters or stock market ‘flash crashes’. On the other hand, researchers have put these technologies to use as research tools, leveraging computer power to sift large volume digital datasets, help make predictions and generate new forms of knowledge. These are not necessarily unimportant questions or nonsensical applications. However, focussing our attention thusly risks losing a sense of the ‘haecceities’ or specificities of the technologies themselves in terms of what they comprise, how their designs are implemented and how they operate. To unravel these aspects, this chapter suggests that rather than viewing them from the outside we stand to learn a great deal by familiarising ourselves with algorithms and AI applications ‘from within’ (Garfinkel 1967: 3). This requires that we, social researchers, become adept with the design, development and deployment of such technologies ourselves, by learning to code. Though this is a potentially daunting addition to the social science methodological toolkit, this chapter will argue that incorporating these skills into our repertoire has the potential to deepen our understanding of how these technologies feature in the situated contexts of our everyday lives and the activities we engage in within them.
}}