# Reason Against the Machine: Future Directions for Mass Online Deliberation

arXiv: https://arxiv.org/abs/2107.12711 · HTML: https://mlsanity.com/paper/2107.12711/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 154 references that are in the map.
- First posted on arXiv 27 Jul 2021.

## Builds on (most cited first, 6 of 6)

- [Ex Machina: Personal Attacks Seen at Scale](https://mlsanity.com/paper/1610.08914/) (2016)
- [The Wisdom of Polarized Crowds](https://mlsanity.com/paper/1712.06414/) (2017)
- [A Longitudinal Analysis of YouTube's Promotion of Conspiracy Videos](https://mlsanity.com/paper/2003.03318/) (2020)
- [Effects of Moderation and Opinion Heterogeneity on Attitude towards the Online Deliberation Experience](https://mlsanity.com/paper/1901.10720/) (2019)
- [An Online Environment for Democratic Deliberation: Motivations, Principles, and Design](https://mlsanity.com/paper/1302.3912/) (2013)
- [Online Deliberation Design: Choices, Criteria, and Evidence](https://mlsanity.com/paper/1302.5177/) (2013)

## Built on by (most cited first, 12 of 12)

- [Do Differences in Values Influence Disagreements in Online Discussions?](https://mlsanity.com/paper/2310.15757/) (2023)
- [An Empirical Analysis of Diversity in Argument Summarization](https://mlsanity.com/paper/2402.01535/) (2024)
- [Morality is Non-Binary: Building a Pluralist Moral Sentence Embedding Space using Contrastive Learning](https://mlsanity.com/paper/2401.17228/) (2024)
- [Value Preferences Estimation and Disambiguation in Hybrid Participatory Systems](https://mlsanity.com/paper/2402.16751/) (2024)
- [A Hybrid Intelligence Method for Argument Mining](https://mlsanity.com/paper/2403.09713/) (2024)
- [Argument Quality Assessment in the Age of Instruction-Following Large Language Models](https://mlsanity.com/paper/2403.16084/) (2024)
- [Bridging Voting and Deliberation with Algorithms: Field Insights from vTaiwan and Kultur Komitee](https://mlsanity.com/paper/2502.05017/) (2025)
- [Facilitating Opinion Diversity through Hybrid NLP Approaches](https://mlsanity.com/paper/2405.09439/) (2024)
- [Supporting Online Discussions: Integrating AI Into the adhocracy+ Participation Platform To Enhance Deliberation](https://mlsanity.com/paper/2409.07780/) (2024)
- [Human/AI Collective Intelligence for Deliberative Democracy: A Human-Centred Design Approach](https://mlsanity.com/paper/2603.16260/) (2026)
- [CogNarr Ecosystem: Facilitating Group Cognition at Scale](https://mlsanity.com/paper/2407.18945/) (2024)
- [AI as a deliberative partner fosters intercultural empathy for Americans but fails for Latin American participants](https://mlsanity.com/paper/2504.13887/) (2025)

## Abstract (arXiv)

Designers of online deliberative platforms aim to counter the degrading quality of online debates. Support technologies such as machine learning and natural language processing open avenues for widening the circle of people involved in deliberation, moving from small groups to "crowd" scale. Numerous design features of large-scale online discussion systems allow larger numbers of people to discuss shared problems, enhance critical thinking, and formulate solutions. We review the transdisciplinary literature on the design of digital mass deliberation platforms and examine the commonly featured design aspects (e.g., argumentation support, automated facilitation, and gamification) that attempt to facilitate scaling up. We find that the literature is largely focused on developing technical fixes for scaling up deliberation, but may neglect the more nuanced requirements of high quality deliberation. Current design research is carried out with a small, atypical segment of the world's population, and much research is still needed on how to facilitate and accommodate different genders or cultures in deliberation, how to deal with the implications of pre-existing social inequalities, how to build motivation and self-efficacy in certain groups, and how to deal with differences in cognitive abilities and cultural or linguistic differences. Few studies bridge disciplines between deliberative theory, design and engineering. As a result, scaling up deliberation will likely advance in separate systemic siloes. We make design and process recommendations to correct this course and suggest avenues for future research