News
CS Student at UC Berkeley Develops Tech to Combat Social Media Harms
06 Nov 2023
Sana Pandey, who is an intern at CHAI, was featured on CBS News in the Bay Area for her work in recommender system alignment. She discussed what drove her to enter the world of recommender systems and her ongoing work with Jonathan Stray on integrating alternatives to engagement into optimization frameworks. The interview also featured Mark Nitzberg who explained the real-world applications and relevance of the project.
Prominent AI Scientists from China and the West Propose Joint Strategy to Mitigate Risks from AI
31 Oct 2023
Ahead of the highly anticipated AI Safety Summit, leading AI scientists from the US, the PRC, the UK and other countries agreed on the importance of global cooperation and jointly called for research and policies to prevent unacceptable risks from advanced AI.
Managing AI Risks in an Era of Rapid Progress
24 Oct 2023
In this short consensus paper, the authors outline risks from upcoming, advanced AI systems. They examine large-scale social harms and malicious uses, as well as an irreversible loss of human control over autonomous AI systems. In light of rapid and continuing AI progress, they propose urgent priorities for AI R&D and governance.
Announcement of Working Group on AI
03 Oct 2023
The Partnership on Information and Democracy have acknowledged the pressing need to develop democratic principles and rules to govern AI in the information space. Democracy and our democratic institutions must decide the ethical use and safeguards of the development, deployment and use of AI. This cannot be left to the private sector who are currently setting the rules of the game. The history of social media illustrates the danger of allowing tech companies to set the rules and ethical uses. Countries must act to safeguard a democratic and trustworthy information space.
ACROCPoLis: A Descriptive Framework for Making Sense of Fairness
26 Sep 2023
Fairness is central to the ethical and responsible development and use of AI systems, with a large number of frameworks and formal notions of algorithmic fairness being available. However, many of the fairness solutions proposed revolve around technical considerations and not the needs of and consequences for the most impacted communities.