This week saw researchers gather in Malmö at the ninth AAAI / ACM Conference on Artificial Intelligence, Ethics, and Society (AIES) . We cast an eye over social media platforms to find out what participants got up to at the event. The 9th annual AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES-26) is underway in Malmö. We are looking forward to a robust program full of educational and networking opportunities. Today at #AIES2026 in Malmö I joined the panel on Participation, Sovereignty, and Power in AI Governance with The Mitigation Paradox, joint work with Mina Schütz, Martin Kappes and Marc-Oliver Pahl. 3 takeaways: https://doi.org/10.5281/zenodo.23302284

Our estimators assume that there is no interference between the units, i.e., that the treatment of one The first assumption is quite realistic as in most peer review systems the reviewers cannot see other reviews until they submit their own. The second assumption is important to understand, as there could be "batch effects": a Monte Carlo methods to tightly estimate these covariances. AAAI datasets, we sampled 1 million assignments and computed the empirical covariance. In our setting, small amounts of attrition (relative to the number of policy-induced positivity violations) mean that the fraction of data that is missing is not exactly known before assignment, but almost. To get more robust estimates of the performance, we repeat this process 10 times.

As a result, these and other large conferences must rely on automated systems to decide what members of the impaneled reviewer pool will review each paper.

In'00: Revised the Second International Conferenceon Computersand Games333-345, 2000.

We also develop a new auto-encoding algorithm that finds a latent space to disentangle latent input coordinates intowell-separated clusters, which isamenable tooursample complexityanalysis.

In many game settings, the game is not explicitly given but is only accessible byplaying it.

Message passing neural network (MPNN) has recently emerged as a successful framework by achieving state-of-the-art performances on many graph-based learning tasks [25].

Find out more about our session on Wednesday 21 January.

Find out more about our session on Wednesday 21 January.