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.

2,364 searchable experts 2,966 tracked across all sources
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Showing developments with attributable Concern & critique reactions.

New Network reaction maps

See how experts are reacting—not just what they shared.

Posts are grouped by conversation and tone. Select a lens to filter the stream; these are never permanent labels on people.

Concern & critique
1 live reaction map in this view Jump to the evidence ↓
Shared emphasis AI is more likely than humans to form biases when hiring Concern & critique · 2 attributable reactions
Showing signals surfaced by Critical AI https://read.dukeupress.edu/critical-ai/issue ×
New & accelerating

What is moving across the network now

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

New AI field signal Signal 10h ago

AI_Toll_Road_v1.pptx

1 directory member surfaced this signal.

Why this matches Concern & critique reaction 1 attributable expert contribution · Critical AI https://read.dukeupress.edu/critical-ai/issue
“docs.google.com/presentation... Check out this new teaching resource by @drewuniversity.bsky.social's #JeremeyBlatter and #CriticalAI intern #AJMannino!” evidence ↗
1 expert 1 community 1 sources clustered
Critical AI https://read.dukeupress.edu/critical-ai/issue

“docs.google.com/presentation... Check out this new teaching resource by @drewuniversity.bsky.social's #JeremeyBlatter and #CriticalAI intern #AJMannino!”

Primary source docs.google.com ↗
Developing Responsible AI Development 1d ago
⚡ 174 h early

AI is more likely than humans to form biases when hiring

2 directory members surfaced this signal.

Why this matches Concern & critique reaction 2 attributable expert contributions · Data & Society, Critical AI https://read.dukeupress.edu/critical-ai/issue
“New research suggests that LLMs can develop their own biases from experience, separate from their training data. This can lead them to stereotype job applicants even more than humans do. www.technologyreview.com/2026/07/20/1...” evidence ↗
2 experts 1 community 1 sources clustered
Data & Society Critical AI https://read.dukeupress.edu/critical-ai/issue
How the network is reacting 2 experts are emphasizing concern & critique.
Shared emphasis

Concern & critique

2 experts

Risks, limits and unintended consequences.

“New research suggests that LLMs can develop their own biases from experience, separate from their training data. This can lead them to stereotype job applicants even more than humans do. www.technologyreview.com/2026/07/20/1...”

Primary source technologyreview.com ↗