Expert attention map

The Who's Who of AI

What credible people across AI noticed, why it matters, and where the field is converging or disagreeing.

2,397 searchable experts 4,280 tracked across all sources
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Showing signals surfaced by Luis Villa ×
New & accelerating

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One card per development. Sources are clustered; reaction bundles describe these posts, never the people behind them.

Developing AI field signal Analysis 1d ago
AI bot midterm fact-checking lessons

I Built an AI Bot to Fact-Check the Midterms. Here's What I've Learned.

3 experts across 2 network communities independently surfaced this.

3 experts 2 communities 1 sources clustered
Renee DiResta Eryk Salvaggio Luis Villa

“I built an AI Community Notewriter. And here's why I think X's Community Notes is going to fail when election rumors about "rigged machines" and "illegal voters" start flying. agentsofinfluence.substack.com/p/i-built-an...”

“Really interesting hands-on digging from @noupside.bsky.social on some ways Notes has gone sideways on X: agentsofinfluence.substack.com/p/i-built-an...”

Primary source agentsofinfluence.substack.com ↗
Established Evaluation & benchmarks Signal 3d ago
⚡ 5 h early

GitHub - pardissz/fairstress: Code for "Asymmetrically Unfair Decision Making and Reasoning in LLMs" (FairStress benchmark, scoring, debiasing methods, figures)

2 directory members surfaced this signal.

2 experts 1 community 1 sources clustered
Ai2 Luis Villa

“Today we’re introducing FairStress, a benchmark for testing demographic fairness beyond a model’s final answer: how readily it changes a well-supported decision & how it explains its choice. 🧵 buff.ly/lWsW77r”

Primary source github.com ↗
Established Evaluation & benchmarks Signal 3d ago

GitHub - pardissz/fairstress: Code for "Asymmetrically Unfair Decision Making and Reasoning in LLMs" (FairStress benchmark, scoring, debiasing methods, figures)

2 directory members surfaced this signal.

2 experts 1 community 1 sources clustered
Ai2 Luis Villa

“Today we’re introducing FairStress, a benchmark for testing demographic fairness beyond a model’s final answer: how readily it changes a well-supported decision & how it explains its choice. 🧵 buff.ly/lWsW77r”

Primary source buff.ly ↗