Summary of Can Probabilistic Feedback Drive User Impacts in Online Platforms?, by Jessica Dai et al.
Can Probabilistic Feedback Drive User Impacts in Online Platforms?
by Jessica Dai, Bailey Flanigan, Nika Haghtalab, Meena Jagadeesan, Chara Podimata
First submitted to arxiv on: 10 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper investigates how content recommender systems can have negative effects on users, even when the platform’s objective is aligned with user welfare. The researchers show that the issue lies not just in misaligned objectives, but also in the way learning algorithms process feedback information from users. They find that different pieces of content can generate feedback at varying rates, which can lead to unintended consequences such as promoting content that is controversial or demographics-influenced. By analyzing various no-regret algorithms using the multi-armed bandit framework, they demonstrate that these algorithms can exhibit diverse dependencies between feedback rates and engagement with individual arms. The study highlights the importance of considering not just regret, but also the nature of an algorithm’s interaction with different content types and their resulting downstream effects. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how online platforms can accidentally cause problems for users. They show that it’s not just about what the platform wants to achieve, but also how the algorithms learn from user reactions. The researchers find that some content gets more or less attention because of how people react to it, which can lead to issues like promoting controversial content. They study different ways that algorithms learn and interact with different types of content, showing that these interactions can be very complex. This study is important because it reminds us that we need to think about not just what the algorithm does, but also why it does it. |
Keywords
* Artificial intelligence * Attention
