ΑΙhub.org
 

Stuart J. Russell wins 2025 AAAI Award for Artificial Intelligence for the Benefit of Humanity


by
04 February 2025



share this:

The AAAI Award for Artificial Intelligence for the Benefit of Humanity recognizes positive impacts of artificial intelligence to protect, enhance, and improve human life in meaningful ways with long-lived effects. The award is given annually at the conference for the Association for the Advancement of Artificial Intelligence (AAAI).

This year, the AAAI Awards Committee has announced that the 2025 recipient of the award and $25,000 prize is Stuart J. Russell, “for his work on the conceptual and theoretical foundations of provably beneficial AI and his leadership in creating the field of AI safety”.

Stuart will give an invited talk at AAAI 2025 entitled “Can AI Benefit Humanity?”

About Stuart

Stuart J. Russell is a Distinguished Professor of Computer Science at the University of California, Berkeley, and holds the Michael H. Smith and Lotfi A. Zadeh Chair in Engineering. He is also a Distinguished Professor of Computational Precision Health at UCSF. His research covers a wide range of topics in artificial intelligence including machine learning, probabilistic reasoning, knowledge representation, planning, real-time decision making, multitarget tracking, computer vision, computational physiology, and philosophical foundations. He has also worked with the United Nations to create a new global seismic monitoring system for the Comprehensive Nuclear-Test-Ban Treaty. His current concerns include the threat of autonomous weapons and the long-term future of artificial intelligence and its relation to humanity.

Read our content featuring previous winners of the award



tags: , ,


AIhub is dedicated to free high-quality information about AI.
AIhub is dedicated to free high-quality information about AI.

            AUAI is supported by:



Subscribe to AIhub newsletter on substack



Related posts :

How much can fair budget-division rules resist manipulation?

The authors write about their award-winning IJCAI-ECAI paper: "Approximate Strategyproofness in Approval-based Budget Division".

AI dives into a sea of data, from plankton to pollution

  16 Sep 2026
“Faster and cheaper monitoring means problems like plankton decline, litter accumulation, oil spills and coral degradation can be picked up and acted on sooner."

Interview with Yash Saxena: how is external knowledge used in AI systems?

  15 Sep 2026
What happens to information from external sources as it moves through an AI system?

When AI art has no author: Study finds generated images often can’t be traced to training data

  14 Sep 2026
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.

AI in nature conservation: powerful tool or dangerous shortcut?

  11 Sep 2026
AI provides opportunity for future biodiversity conservation but introduces risks .

Improving the process for large-scale recommender systems: an interview with Haruka Kiyohara

  10 Sep 2026
Credit-assigned policy gradient for early stage retrieval in two-stage ranking.

The Machine Ethics podcast: Data Collective with E.M. Lewis-Jong

Ben chats to E.M. Lewis-Jong about the promise of AI and making human connection easier, speech recognition and supporting linguistic diversity, making useful technologies that have a purpose, and more.

AI for ethology: an interview with Isla Duporge

  08 Sep 2026
Deep learning is becoming a powerful tool for understanding animal behaviour and tracking populations.



AUAI is supported by:







Subscribe to AIhub newsletter on substack




 















©2026.05 - Association for the Understanding of Artificial Intelligence