9 papers
Counterfactual Methods for Detecting Unfairness in Anti-Money Laundering Algorithms
Lea Multerer, Michele Inchingolo, David Kletz +3
The application of machine learning-based predictive algorithms to Anti-Money Laundering (AML) has grown rapidly, driven by the vast volume of financial transaction data available…
Automatic Prompt Optimization for Dataset-Level Feature Discovery
Adrian Cosma, Oleg Szehr, David Kletz +2
Feature extraction from unstructured text is a critical step in many downstream classification pipelines, yet current approaches largely rely on hand-crafted prompts or fixed featu…
Coordinated Strategies in Realistic Air Combat by Hierarchical Multi-Agent Reinforcement Learning
Ardian Selmonaj, Giacomo Del Rio, Adrian Schneider +1
Achieving mission objectives in a realistic simulation of aerial combat is highly challenging due to imperfect situational awareness and nonlinear flight dynamics. In this work, we…
Towards Human Engagement with Realistic AI Combat Pilots
Ardian Selmonaj, Giacomo Del Rio, Adrian Schneider +1
We present a system that enables real-time interaction between human users and agents trained to control fighter jets in simulated 3D air combat scenarios. The agents are trained i…
Causal Understanding by LLMs: The Role of Uncertainty
Oscar Lithgow-Serrano, Vani Kanjirangat, Alessandro Antonucci
Recent papers show LLMs achieve near-random accuracy in causal relation classification, raising questions about whether such failures arise from limited pretraining exposure or dee…
On the Correlation between Individual Fairness and Predictive Accuracy in Probabilistic Models
Alessandro Antonucci, Eric Rossetto, Ivan Duvnjak
We investigate individual fairness in generative probabilistic classifiers by analysing the robustness of posterior inferences to perturbations in private features. Building on est…