Captivating algorithms: Recommender systems as traps

By Nick Seaver. Journal of material culture, 2018

299 citations recorded by the source corpus.

Algorithmic recommender systems are a ubiquitous feature of contemporary cultural life online, suggesting music, movies, and other materials to their users. This article, drawing on fieldwork with developers of recommender systems in the US, describes a tendency among these systems’ makers to describe their purpose as ‘hooking’ people – enticing them into frequent or enduring usage. Inspired by steady references to capture in the field, the author considers recommender systems as traps, drawing on anthropological theories about animal trapping. The article charts the rise of ‘captivation metrics’ – measures of user retention – enabled by a set of transformations in recommenders’ epistemic, economic, and technical contexts. Traps prove useful for thinking about how such systems relate to broader infrastructural ecologies of knowledge and technology. As recommenders spread across online cultural infrastructures and become practically inescapable, thinking with traps offers an alternative to common ethical framings that oppose tropes of freedom and coercion.

Subfields: Information Systems, Sociology and Political Science, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/174ebfffe983ee2b14dc3ade9dbd4c03e5b4068c

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Citations in the corpus

Bringing the People Back In: Contesting Benchmark Machine Learning Datasets (2020, methodology): “…sociotechnical systems has focused on the importance of analyzing interrogating unspoken work practices of technical experts (Passi & Barocas, 2019; Seaver, 2019), subject matter experts in acts of data repair (Sachs, 2019), and crowd laborers (Irani & Silberman, 2013; Salehi et al., 2015; Gray &…”
Recommender systems and their ethical challenges (2020, methodology): “The methodologies that they adopt are more varied, ranging from ethnographic study (Seaver 2018b), to hermeneutics (de Vries 2010), decision theory (Burr et al. 2018), and economics (Abdollahpouri et al. 2017).”