Understanding Recommenders

Understanding Recommenders

Priyanjana Bengani

What is Media Diversity and Do Recommender Systems Have It?

— Priyanjana Bengani, Jonathan Stray, Luke Thorburn

11 min readNov 13, 2023

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What is Media Diversity and Do Recommender Systems Have It?

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Luke Thorburn

What’s the Difference Between Search and Recommendation?

— Luke Thorburn, Jonathan Stray, Priyanjana Bengani

10 min readOct 25, 2023

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What’s the Difference Between Search and Recommendation?

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Luke Thorburn

Making Amplification Measurable

— Luke Thorburn, Jonathan Stray, Priyanjana Bengani

9 min readMay 7, 2023

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Making Amplification Measurable

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Luke Thorburn

When You Hear “Filter Bubble”, “Echo Chamber”, or “Rabbit Hole” — Think “Feedback Loop”

— Luke Thorburn, Jonathan Stray, Priyanjana Bengani

17 min readMar 30, 2023

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When You Hear “Filter Bubble”, “Echo Chamber”, or “Rabbit Hole” — Think “Feedback Loop”

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Luke Thorburn

Is Optimizing for Engagement Changing Us?

— Luke Thorburn, Jonathan Stray, Priyanjana Bengani

12 min readOct 10, 2022

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Is Optimizing for Engagement Changing Us?

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Priyanjana Bengani

A Menu of Recommender Transparency Options

— Priyanjana Bengani, Jonathan Stray, Luke Thorburn

9 min readAug 17, 2022

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A Menu of Recommender Transparency Options

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Luke Thorburn

How to Measure the Causal Effects of Recommenders

The capabilities researchers need to understand how much societal harm is caused by recommender systems on social media.

12 min readJul 20, 2022

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How to Measure the Causal Effects of Recommenders

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Luke Thorburn

What Will “Amplification” Mean in Court?

— Luke Thorburn, Jonathan Stray, Priyanjana Bengani

13 min readMay 23, 2022

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What Will “Amplification” Mean in Court?

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Priyanjana Bengani

What’s Right and What’s Wrong with Optimizing for Engagement

— Priyanjana Bengani, Jonathan Stray, Luke Thorburn

9 min readApr 27, 2022

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What’s Right and What’s Wrong with Optimizing for Engagement

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Luke Thorburn

What Does it Mean to Give Someone What They Want? The Nature of Preferences in Recommender Systems

— Luke Thorburn, Jonathan Stray, Priyanjana Bengani

14 min readMar 11, 2022

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What Does it Mean to Give Someone What They Want? The Nature of Preferences in Recommender Systems

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Understanding Recommenders is a research-driven effort to demystify recommender systems and their impact on society. A project of the Center for Human-Compatible AI at the University of California, Berkeley.
Editors
Luke Thorburn
King's College London
Jonathan Stray
Working on better personalized news and information at @CHAI_Berkeley. Previously: computational journalism at , editor AP, made some software
Priyanjana Bengani

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