Conference Agenda

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Session Overview
RT: Algorithms 2
Friday, 04/Nov/2022:
3:30pm - 5:00pm

Location: EQ-208

60 seats flexible

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Highly Recommended

Tarleton Gillespie1, Sophie Bishop2, Jean Burgess3, Blake Hallinan4, Nick Seaver5

1Microsoft Research, United States of America; 2University of Sheffield, United Kingdom; 3Queensland University of technology, Australia; 4The Hebrew University of Jerusalem, Israel; 5Tufts University, United States of of America

Recommendation algorithms structure what we encounter online, how platforms feel to their users, and what counts as speech and reach in a modern age. Critics worry about filter bubbles and rabbit holes, while social media companies dispute their responsibility for the content they amplify. Influencers struggle for visibility, a battle waged with the recommendation systems and their oblique calculus of engagement. Platforms now use recommendation as a way to reduce risky or problematic content, while users who suspect that they’ve been shadowbanned or demoted struggle to provide evidence of the interventions made against them.

This roundtable will examine recommendation as a profitable industrial exercise, an assertion of value, a sociotechnical accomplishment, and a terrain for public contest. What values drive recommendation on social media and content intermediaries? What do we make of the shifting mix of human labor and software that performs recommendation? What values animate recommendation engines, and what happens when these values are in conflict? How do we now understand personalization, given public concerns about its aggregate effects? What new tactics are users and other cultural intermediaries adopting to grapple with, exploit, evade, or challenge current recommendation systems? What do users think recommendations signify, and how does that shape their efforts to find and be found? Are there progressive alternatives to how recommendations currently work, and where might they be pursued? What responsibility adheres to the act of recommending?

We will also discuss methodological and aspirational concerns: How much must we understand about the technical workings of recommender systems to make useful sociological critique of them? What techniques help in studying what is recommended across many users and how that matters? How has recommendation worked in traditional media, and what might that historical vantage point offer? Where is a comparative analysis of our encounters with recommendations most useful?

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