290 citations · 469 across the 35 of their papers we have counts for
5 papers · 1 filter
Counterfactual Explanations via Locally-guided Sequential Algorithmic Recourse
Edward A. Small, Jeffrey N. Clark, Christopher J. McWilliams +4
Counterfactuals operationalised through algorithmic recourse have become a powerful tool to make artificial intelligence systems explainable. Conceptually, given an individual clas…
Capturing Requirements for a Data Annotation Tool for Intensive Care: Experimental User-Centered Design Study
Marceli Wac, Raul Santos-Rodriguez, Chris McWilliams +1
Intensive care units (ICUs) are complex and data-rich environments. Data routinely collected in the ICUs provides tremendous opportunities for machine learning, but their use comes…
TraCE: Trajectory Counterfactual Explanation Scores
Jeffrey N. Clark, Edward A. Small, Nawid Keshtmand +5
Counterfactual explanations, and their associated algorithmic recourse, are typically leveraged to understand, explain, and potentially alter a prediction coming from a black-box c…
Privacy in Multimodal Federated Human Activity Recognition
Alex Iacob, Pedro P. B. Gusmão, Nicholas D. Lane +5
Human Activity Recognition (HAR) training data is often privacy-sensitive or held by non-cooperative entities. Federated Learning (FL) addresses such concerns by training ML models…
What You Hear Is What You See: Audio Quality Metrics From Image Quality Metrics
Tashi Namgyal, Alexander Hepburn, Raul Santos-Rodriguez +2
In this study, we investigate the feasibility of utilizing state-of-the-art image perceptual metrics for evaluating audio signals by representing them as spectrograms. The encourag…