2024
Upcoming
Learning representations from high-level structures
Vine copula-based regression models
AI, scarce resources and climate transition
Effector: A Python package for regional explanations
Making AI sustainable
Previous
Reinforcement learning for respondent-driven sampling
Explaining Bayesian optimization by Shapley values
The Bayesian learning rule
All models are wrong, and sometimes, the optimiser is wrong too!
This seminar is part of the seminar series on Integreat's objective Fair, Explainable and Trustworthy Machine Learning.
Dr. Rami Mochaourab research leader of Research Institutes of Sweden RISE gives a brief overview of trustworthy AI and delves into aspects of trustworthiness: accuracy, privacy, and explainability.