# Tournesol: A quest for a large, secure and trustworthy database of reliable human judgments

arXiv: https://arxiv.org/abs/2107.07334 · HTML: https://mlsanity.com/paper/2107.07334/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 94 references that are in the map.
- First posted on arXiv 29 May 2021.

## Builds on (most cited first, 11 of 11)

- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity](https://mlsanity.com/paper/2101.03961/) (2021)
- [Extracting Training Data from Large Language Models](https://mlsanity.com/paper/2012.07805/) (2020)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems](https://mlsanity.com/paper/1905.00537/) (2019)
- [Persistent Anti-Muslim Bias in Large Language Models](https://mlsanity.com/paper/2101.05783/) (2021)
- [The Hidden Vulnerability of Distributed Learning in Byzantium](https://mlsanity.com/paper/1802.07927/) (2018)
- [The Design and Implementation of XiaoIce, an Empathetic Social Chatbot](https://mlsanity.com/paper/1812.08989/) (2018)
- [The Radicalization Risks of GPT-3 and Advanced Neural Language Models](https://mlsanity.com/paper/2009.06807/) (2020)
- [Auditing Radicalization Pathways on YouTube](https://mlsanity.com/paper/1908.08313/) (2019)
- [Distributed Momentum for Byzantine-resilient Learning](https://mlsanity.com/paper/2003.00010/) (2020)

## Built on by (most cited first, 11 of 11)

- [On the Impossible Safety of Large AI Models](https://mlsanity.com/paper/2209.15259/) (2022)
- [Inverting Gradient Attacks Makes Powerful Data Poisoning](https://mlsanity.com/paper/2410.21453/) (2024)
- [On the Byzantine Fault Tolerance of signSGD with Majority Vote](https://mlsanity.com/paper/2502.19170/) (2025)
- [With a Little Help From My Friends: Collective Manipulation in Risk-Controlling Recommender Systems](https://mlsanity.com/paper/2603.28476/) (2026)
- [Strategyproof Learning: Building Trustworthy User-Generated Datasets](https://mlsanity.com/paper/2106.02398/) (2021)
- [Identify ambiguous tasks combining crowdsourced labels by weighting Areas Under the Margin](https://mlsanity.com/paper/2209.15380/) (2022)
- [Generalizing while preserving monotonicity in comparison-based preference learning models](https://mlsanity.com/paper/2506.08616/) (2025)
- [Byzantine Machine Learning: MultiKrum and an optimal notion of robustness](https://mlsanity.com/paper/2602.03899/) (2026)
- [Approaching the Harm of Gradient Attacks While Only Flipping Labels](https://mlsanity.com/paper/2503.00140/) (2025)
- [A Case for Specialisation in Non-Human Entities](https://mlsanity.com/paper/2503.04742/) (2025)
- [On Monotonicity in AI Alignment](https://mlsanity.com/paper/2506.08998/) (2025)

## Abstract (arXiv)

Today's large-scale algorithms have become immensely influential, as they recommend and moderate the content that billions of humans are exposed to on a daily basis. They are the de-facto regulators of our societies' information diet, from shaping opinions on public health to organizing groups for social movements. This creates serious concerns, but also great opportunities to promote quality information. Addressing the concerns and seizing the opportunities is a challenging, enormous and fabulous endeavor, as intuitively appealing ideas often come with unwanted it side effects, and as it requires us to think about what we deeply prefer. Understanding how today's large-scale algorithms are built is critical to determine what interventions will be most effective. Given that these algorithms rely heavily on it machine learning, we make the following key observation: any algorithm trained on uncontrolled data must not be trusted. Indeed, a malicious entity could take control over the data, poison it with dangerously manipulative fabricated inputs, and thereby make the trained algorithm extremely unsafe. We thus argue that the first step towards safe and ethical large-scale algorithms must be the collection of a large, secure and trustworthy dataset of reliable human judgments. To achieve this, we introduce Tournesol, an open source platform available at urlhttps://tournesol.app. Tournesol aims to collect a large database of human judgments on what algorithms ought to widely recommend (and what they ought to stop widely recommending). We outline the structure of the Tournesol database, the key features of the Tournesol platform and the main hurdles that must be overcome to make it a successful project. Most importantly, we argue that, if successful, Tournesol may then serve as the essential foundation for any safe and ethical large-scale algorithm.