AI Safety Summit by UK Government

26 Oct 2023

As Artificial Intelligence rapidly advances, so do the opportunities and the risks.

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.

Expertise Trees Resolve Knowledge Limitations in Collective Decision-Making

12 Oct 2023

Experts advising decision-makers are likely to display expertise which varies as a function of the problem instance. In practice, this may lead to sub-optimal or discriminatory decisions against minority cases.

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.

100 Most Influential People in AI

18 Sep 2023

On September 7th, 2023, global media platform and magazine TIME published an article spotlighting TIME100 Most Influential People in AI.

Conditional Abstraction Trees for Sample-Efficient Reinforcement Learning

31 Aug 2023

In many real-world problems, the learning agent needs to learn a problem’s abstractions and solution simultaneously. However, most such abstractions need to be designed and refined by hand for different problems and domains of application.

Who Needs to Know? Minimal Knowledge for Optimal Coordination

17 Aug 2023

It is often crucial to have information about one’s collaborators. However, not every feature of collaborators is strategically relevant.

SMCP3: Sequential Monte Carlo with Probabilistic Program Proposals

21 Jul 2023

This paper introduces SMCP3, a new family of sequential Bayesian inference algorithms.

Dealing with expert bias in collective decision-making

12 Jul 2023

Quite some real-world problems can be formulated as decision-making problems wherein one must repeatedly make an appropriate choice from a set of alternatives.