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Found 3 papers, 2 papers with code
On the Standardization of Behavioral Use Clauses and Their Adoption for Responsible Licensing of AI
As of the end of 2023, on the order of 40, 000 software and model repositories have adopted responsible AI licenses licenses.
Explanation Strategies as an Empirical-Analytical Lens for Socio-Technical Contextualization of Machine Learning Interpretability
During a research project in which we developed a machine learning (ML) driven visualization system for non-ML experts, we reflected on interpretability research in ML, computer-supported collaborative work and human-computer interaction.
FedAUX: Leveraging Unlabeled Auxiliary Data in Federated Learning
Federated Distillation (FD) is a popular novel algorithmic paradigm for Federated Learning, which achieves training performance competitive to prior parameter averaging based methods, while additionally allowing the clients to train different model architectures, by distilling the client predictions on an unlabeled auxiliary set of data into a student model.
