Representative Ranking for Deliberation in the Public Sphere
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Abstract (arXiv)
Online comment sections, such as those on news sites or social media, have the potential to foster informal public deliberation, However, this potential is often undermined by the frequency of toxic or low-quality exchanges that occur in these settings. To combat this, platforms increasingly leverage algorithmic ranking to facilitate higher-quality discussions, e.g., by using civility classifiers or forms of prosocial ranking. Yet, these interventions may also inadvertently reduce the visibility of legitimate viewpoints, undermining another key aspect of deliberation: representation of diverse views. We seek to remedy this problem by introducing guarantees of representation into these methods. In particular, we adopt the notion of justified representation (JR) from the social choice literature and incorporate a JR constraint into the comment ranking setting. We find that enforcing JR leads to greater inclusion of diverse viewpoints while still being compatible with optimizing for user engagement or other measures of conversational quality.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 27 of its 62 references that are in the map. First posted on arXiv 19 Mar 2025.
Builds on
27 of 62 references.
References · 27
- 2017Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairnesscited by 381
- 2018Improving fairness in machine learning systems: What do industry practitioners need?cited by 262
- 2022A New Generation of Perspective API: Efficient Multilingual Character-level Transformerscited by 181
- 2024Collective Constitutional AI: Aligning a Language Model with Public Inputcited by 120
- 2023Generative Social Choicecited by 43
- 2014Justified Representation in Approval-Based Committee Votingcited by 42
- 2023A Survey on Intersectional Fairness in Machine Learning: Notions, Mitigation, and Challengescited by 41
- 2016Proportional Justified Representationcited by 25
- 2023Embedding Democratic Values into Social Media AIs via Societal Objective Functionscited by 25
- 2023Robust and Verifiable Proportionality Axioms for Multiwinner Votingcited by 23
- 2023Fairness and Diversity in Recommender Systems: A Surveycited by 22
- 2019Proportionality and the Limits of Welfarismcited by 21
15 more
- 2021Representativeness in Statistics, Politics, and Machine Learningcited by 17
- 2023Bridging Systems: Open Problems for Countering Destructive Divisiveness across Ranking, Recommenders, and Governancecited by 17
- 2023Democratic Policy Development using Collective Dialogues and AIcited by 16
- 2018Efficiently Learning Mixtures of Mallows Modelscited by 13
- 2022Representation with Incomplete Votescited by 13
- 2023'Generative CI' through Collective Response Systemscited by 13
- 2016Proportional Rankingscited by 10
- 2020Classifying Constructive Commentscited by 8
- 2024Reranking partisan animosity in algorithmic social media feeds alters affective polarizationcited by 7
- 2021Dynamic Proportional Rankingscited by 4
- 2021The Price of Justified Representationcited by 4
- 2021Justifying Groups in Multiwinner Approval Votingcited by 3
- 2024Re-Ranking News Comments by Constructiveness and Curiosity Significantly Increases Perceived Respect, Trustworthiness, and Interestcited by 3
- 2022Robustness of Greedy Approval Rulescited by 2
- 2024Combining Voting and Abstract Argumentation to Understand Online Discussionscited by 2
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.