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Collective Intelligence Project @cip.org · 21/01/2026

New report drop! After seven rounds of Global Dialogues with more than 6000 people across 70 countries in 2025, we are releasing the 2025 Global Dialogues Index Report. blog.cip.org/2025gdindex

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Collective Intelligence Project @cip.org · 23/05/2025

9/10: We built a Github suite to systematically test and quantify these biases. It lets you:

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Collective Intelligence Project @cip.org · 23/05/2025

7/10: These aren't just minor quirks. LLMs lack the mechanistic precision of traditional software. Their architecture means system prompts and input material exist in the same context, leading to unpredictable interactions.

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Collective Intelligence Project @cip.org · 23/05/2025

6/10: Rubric-based scoring is also affected. We observed 'recency bias' where criteria scored later received lower averages. Holistic vs. isolated evaluation dramatically shifted scores too.

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Collective Intelligence Project @cip.org · 23/05/2025

5/10: For example, in pairwise choices, LLMs favored "Response B" 60-69% of the time, a significant deviation from random. Even explicit "de-biasing" prompts sometimes increased bias.

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Collective Intelligence Project @cip.org · 23/05/2025

4/10: LLMs exhibit cognitive biases similar to humans: serial position, framing, anchoring. Our tests across frontier models from Google, Mistral, Anthropic, and OpenAI consistently show these biases in judgment contexts.

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Collective Intelligence Project @cip.org · 23/05/2025

3/10: "Prompt engineering" often relies on untested folklore. We found even minor prompt changes, like "Response A" vs. "Response B" labeling, significantly bias LLM choices.

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Collective Intelligence Project @cip.org · 23/05/2025

2/10: This is important because LLMs are increasingly deployed for evaluation tasks, ranking, decision-making, and judgement in many critical domains.

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Collective Intelligence Project @cip.org · 23/05/2025

1/10: LLM Judges Are Unreliable. Our latest blog post from @j11y.io shows that positional preferences, order effects, and prompt sensitivity fundamentally undermine the reliability of LLM judges.

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Collective Intelligence Project @cip.org · 19/05/2025

Submissions will be judged by an amazing panel: @audreyt.org (Cyber Ambassador-at-large for Taiwan) @nabiha.bsky.social (Executive Director of @mozilla.org ) Zoe Hitzig (Research Scientist at OpenAI and Poet)

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Collective Intelligence Project @cip.org · 19/05/2025

We're officially launching the Global Dialogues Challenge!

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Collective Intelligence Project @cip.org · 23/12/2024

10/ We think they can be, but it will take rapid, concerted effort. We’re looking forward to tackling that challenge in 2025 together. We’ll see you there. 2024.cip.org

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Collective Intelligence Project @cip.org · 23/12/2024

5/ We published a Roadmap to Democratic AI. www.cip.org/research/ai-...

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Collective Intelligence Project @cip.org · 23/12/2024

3/ We built Community Models to experiment with plural alignment at different scales - giving people and communities the tools to directly shape AI models. cm.cip.org

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Collective Intelligence Project @cip.org · 23/12/2024

This week's Collections takes stock of 2024: a year when AI capabilities surged, while the need for collective intelligence became clearer than ever. For the full story of how we're building towards democratic AI futures: 2024.cip.org

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Collective Intelligence Project @cip.org · 25/11/2024

5/ Her proposed "Community Posts" mechanism shows how careful system design could help bridge polarizing divides - echoing our belief that good institutional design is key to collective intelligence.

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Collective Intelligence Project @cip.org · 25/11/2024

2/ Just as we build banking systems assuming tellers might make mistakes, we need to build robust, resilient AI ecosystems that assume models will fail - not just "ethical" AI at the individual model level.

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Collective Intelligence Project @cip.org · 22/11/2024

Individual AI safety ≠ system safety. Each response is blind to its broader context. This dynamic escalates with multi-agent systems, where "safe" AI agents interact. Seemingly innocent individual actions can combine into security breaches.

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Collective Intelligence Project @cip.org · 22/11/2024

Yet with AI, we're focusing on "aligned" models while ignoring system-level vulnerabilities. An AI model may be like a bank teller who follows protocol perfectly but can't see they're part of a larger fraud scheme.

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Collective Intelligence Project @cip.org · 22/11/2024

NEW ARTICLE from @j11y.io: "The AI Safety Paradox: When 'Safe' AI Makes Systems More Dangerous" Our obsession with making individual AI models safer might actually be making our systems more vulnerable.

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