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.

Who Needs to Know? Minimal Knowledge for Optimal Coordination

07 Jul 2023

To optimally coordinate with others in cooperative games, it is often crucial to have information about one’s collaborators. However, not every feature of collaborators is strategically relevant.

Seventh annual CHAI Workshop

20 Jun 2023

From the 16th to the 18th of June, CHAI held its 7th annual workshop at Asilomar Conference Grounds in Pacific Grove, CA.

Harms from Increasingly Agentic Algorithmic Systems

29 May 2023

CHAI Micah Carroll and former CHAI interns Dmitrii Krasheninnikov, Lauro Langosco, and Yawen Duan published a paper titled Harms from Increasingly Agentic Algorithmic Systems for FAccT 2023.

Dealing with expert bias in collective decision-making

02 May 2023

In their paper published in the AI journal, CHAI Tom Lenaerts and Axel Abels argue that 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.