The call for abstract is open for the panel Human-AI feedback loops in platformized consumption at the 10th STS Italia Conference to be held at the Politecnico di Milano, 11 – 13 June, 2025!
You can find the call below, and you can apply following this link:
https://stsitalia.org/submission-2025 (Abstract ID: 45),
The deadline for applications is the 3 February 2025.
Call for Abstracts – Human-AI feedback loops in platformized consumption @10th STS Italia Conference (Politecnico di Milano, Milan)
Massimo Airoldi, Università degli Studi di Milano
Alessandro Caliandro, Università di Pavia
Alessandro Gandini, Università degli Studi di Milano
Gabriella Punziano, Università degli Studi di Napoli Federico II
Platform users absentmindedly interact with proprietary AI systems recommending a variety
of personalized content (e.g., social media posts, music, news, people, products). Scholars
have highlighted how such recursive and generally opaque human-machine interactions are
at the core of platforms’ extractive business model, and discussed their social and political
implications through conceptual lenses such as “filter bubble” (see Bruns, 2019),
algorithmic “power” and “resistance” (Bonini and Treré, 2024), “traps” (Seaver, 2022),
“hypernudging” (Yeung, 2017), “diversity” and “confinement” (Roth et al., 2020).
This panel encourages the study of platformized human-AI interactions in light of another
notion, that of “feedback loop”. From a cybernetic perspective, feedback mechanisms make
learning possible to both humans and machines. When platform users and recommender
systems interact, feedback-based learning regularly happens both ways: on the one hand, AI
recommendations expose users to selections of content they “may also like”, orchestrating
their digital consumption habits; on the other, based on users’ datafied behaviour, machine
learning systems iteratively update their parameters, aiming to better anticipate future
consumption desires. Hence, personalised recommendations end up shaping the very
behavioural data on which they are computed, producing a techno-social circuit raising big
sociological questions (Beer, 2022).
What are the effects of human-AI feedback loops on platformized consumption and
“consumer culture” more broadly (Caliandro et al., 2024)? How does the accelerated
temporality of online content consumption habits intersect with the predictive habits (and
habitus) of machines (Airoldi, 2022)? In what ways can we trace and interpret the recursive
interactions between the users of TikTok, Instagram, YouTube or Spotify, and the opaque
recommender algorithms at work within such data-intensive infrastructures? These are
some of the questions this panel aims to address by selecting theory-driven, empirically
sound and methodologically innovative contributions that are attentive to the social and
cultural dimensions of platformized feedback loops, beyond technologically deterministic
simplifications.
Contributions may cover, but are not limited to, the following topics:
- feedback loops, AI recommendations and the platformization of consumer culture;
- the interplay between platform personalization and (more-than-human) habits;
- how feedback loops vary across social categories and platformized cultures;
- market infrastructures and the engineering of feedback loops;
- platform-based feedback loops in music streaming and cultural consumption;
- cross-platform analyses of human-AI feedback loops;
- innovative methodological solutions in the study of platformized consumption.
Keywords: AI, consumer culture, feedback loop, platformization, recommendation systems
References
Airoldi, M. (2022). Machine habitus: Toward a sociology of algorithms. Polity.
Beer, D. (2022). The problem of researching a recursive society: Algorithms, data coils and
the looping of the social. Big Data & Society, 9(2), 205395172211049.
Bruns, A. (2019). Are filter bubbles real? Polity.
Caliandro, A., Gandini, A., Bainotti, L. and Anselmi, G. (2024). The Platformisation of
Consumer Culture: A Digital Methods Guide. Amsterdam University Press.
Roth, C., Mazières, A. and Menezes, T. (2020). Tubes and bubbles topological confinement
of YouTube recommendations. PLOS ONE, 15(4), e0231703.
Seaver, N. (2022). Computing Taste: Algorithms and The Makers of Music Recommendation.
University of Chicago Press.
Yeung, K. (2017). ‘Hypernudge’: Big Data as a mode of regulation by design. Information,
Communication and Society, 20(1), 118–136.