# Can Probabilistic Feedback Drive User Impacts in Online Platforms?

arXiv: https://arxiv.org/abs/2401.05304 · HTML: https://mlsanity.com/paper/2401.05304/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 44 references that are in the map.
- First posted on arXiv 10 Jan 2024.

## Builds on (most cited first, 9 of 9)

- [Fairness in Learning: Classic and Contextual Bandits](https://mlsanity.com/paper/1605.07139/) (2016)
- [Modeling Content Creator Incentives on Algorithm-Curated Platforms](https://mlsanity.com/paper/2206.13102/) (2022)
- [Supply-Side Equilibria in Recommender Systems](https://mlsanity.com/paper/2206.13489/) (2022)
- [What are you optimizing for? Aligning Recommender Systems with Human Values](https://mlsanity.com/paper/2107.10939/) (2021)
- [Estimating and Penalizing Induced Preference Shifts in Recommender Systems](https://mlsanity.com/paper/2204.11966/) (2022)
- [Building Human Values into Recommender Systems: An Interdisciplinary Synthesis](https://mlsanity.com/paper/2207.10192/) (2022)
- [Learning on the Edge: Online Learning with Stochastic Feedback Graphs](https://mlsanity.com/paper/2210.04229/) (2022)
- [Bandits Meet Mechanism Design to Combat Clickbait in Online Recommendation](https://mlsanity.com/paper/2311.15647/) (2023)
- [Risk Preferences of Learning Algorithms](https://mlsanity.com/paper/2205.04619/) (2022)

## Built on by (most cited first, 5 of 5)

- [How to Strategize Human Content Creation in the Era of GenAI?](https://mlsanity.com/paper/2406.05187/) (2024)
- [Pure Exploration with Feedback Graphs](https://mlsanity.com/paper/2503.07824/) (2025)
- [The Burden of Interactive Alignment with Inconsistent Preferences](https://mlsanity.com/paper/2510.16368/) (2025)
- [Bandits with Probing: Optimal Regret and the Limits of Winner Feedback](https://mlsanity.com/paper/2609.15248/) (2026)
- [CAVEAT: Towards Robust Computer-Use Agents in Incentive-Misaligned Environments](https://mlsanity.com/paper/2609.27273/) (2026)

## Abstract (arXiv)

A common explanation for negative user impacts of content recommender systems is misalignment between the platform's objective and user welfare. In this work, we show that misalignment in the platform's objective is not the only potential cause of unintended impacts on users: even when the platform's objective is fully aligned with user welfare, the platform's learning algorithm can induce negative downstream impacts on users. The source of these user impacts is that different pieces of content may generate observable user reactions (feedback information) at different rates; these feedback rates may correlate with content properties, such as controversiality or demographic similarity of the creator, that affect the user experience. Since differences in feedback rates can impact how often the learning algorithm engages with different content, the learning algorithm may inadvertently promote content with certain such properties. Using the multi-armed bandit framework with probabilistic feedback, we examine the relationship between feedback rates and a learning algorithm's engagement with individual arms for different no-regret algorithms. We prove that no-regret algorithms can exhibit a wide range of dependencies: if the feedback rate of an arm increases, some no-regret algorithms engage with the arm more, some no-regret algorithms engage with the arm less, and other no-regret algorithms engage with the arm approximately the same number of times. From a platform design perspective, our results highlight the importance of looking beyond regret when measuring an algorithm's performance, and assessing the nature of a learning algorithm's engagement with different types of content as well as their resulting downstream impacts.