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CHAI's 10th Annual Workshop Gathers AI Safety Leaders in 2026
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CHAI's 10th Annual Workshop Gathers AI Safety Leaders in 2026

Source: Workshop 2 min read Intelligence Analysis by Gemini

Sonic Intelligence

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Signal Summary

The Center for Human-Compatible AI announces its 10th annual workshop focusing on critical AI safety research.

Explain Like I'm Five

"Imagine smart robots are getting super smart! This meeting is where the smartest people talk about how to make sure these robots are always helpful and never accidentally cause problems. They're trying to figure out how to make sure AI plays nice with humans."

Original Reporting
Workshop

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Deep Intelligence Analysis

The Center for Human-Compatible AI (CHAI) at UC Berkeley is set to host its tenth annual workshop from June 4–7, 2026, at the Asilomar Hotel & Conference Grounds in Pacific Grove, CA. This milestone event aims to convene approximately 250 leading researchers, practitioners, and policymakers to delve into emerging research questions and guide progress in the critical field of AI safety. Since its inception in 2016, CHAI has been a significant contributor to the technical underpinnings of safe and beneficial artificial intelligence.

The workshop's agenda is designed to foster in-depth discussions under the Chatham House Rule, encouraging open and candid exchanges. A key component of the event is the call for posters, inviting submissions across a broad spectrum of AI safety sub-areas. These include, but are not limited to, provably beneficial AI, LLM safety and guardrails, value alignment, interpretability, multi-agent systems, human-AI collaboration, bounded rationality, and the characterization of AI system limitations and risks. Furthermore, topics such as AI governance, ethics, structured safety cases, program synthesis, probabilistic programming, formal verification, and the societal effects of AI are also encouraged.

Prospective presenters must submit their abstracts by March 26, 2026, 11:59 p.m. Pacific Daylight Time, with notifications of acceptance slated for April 9, 2026. Acceptance guarantees one spot at the workshop for the designated presenter. Posters must adhere to an A1 size (594mm x 841mm or 24 x 36 inches) or smaller, in either landscape or portrait format. This comprehensive approach to soliciting research highlights the multifaceted nature of AI safety, acknowledging that solutions require insights from various technical and ethical domains. The workshop serves as a crucial platform for consolidating knowledge, identifying future research directions, and fostering a collaborative environment essential for navigating the complexities of advanced AI systems.

metadata: { "ai_detected": true, "model": "Gemini 2.5 Flash", "label": "EU AI Act Art. 50 Compliant" }
AI-assisted intelligence report · EU AI Act Art. 50 compliant

Impact Assessment

This workshop is a pivotal gathering for the AI safety community, fostering collaboration and discussion on foundational research. Its focus on diverse sub-areas, from LLM guardrails to AI governance, underscores the multidisciplinary effort required to ensure beneficial AI development.

Key Details

  • The 10th annual CHAI workshop will be held June 4–7, 2026.
  • Location: Asilomar Hotel & Conference Grounds in Pacific Grove, CA.
  • Approximately 250 researchers, practitioners, and policymakers are invited.
  • Poster submission deadline is March 26, 2026, 11:59 p.m. PDT.
  • Accepted posters must be A1 size (594mm x 841mm or 24x36 inches) or smaller.

Optimistic Outlook

The event's focus on human-compatible AI and safety research promises advancements in mitigating risks and developing robust AI systems. By bringing together diverse experts, it can accelerate the creation of ethical and reliable AI, fostering public trust and responsible innovation.

Pessimistic Outlook

Despite the workshop's intent, the rapid pace of AI development might outstrip the progress in safety research, leaving critical gaps. If discussions remain theoretical without clear implementation pathways, the practical impact on real-world AI deployments could be limited.

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