activity
20182022
most citedFAT Forensics: A Python Toolbox for Implementing and Deploying Fairness, Accountability and Transparency Algorithms in Predictive Systems

37 citations · 53 across the 7 of their papers we have counts for

collaborators

11 papers

cs.LG202237 cited

FAT Forensics: A Python Toolbox for Implementing and Deploying Fairness, Accountability and Transparency Algorithms in Predictive Systems

Kacper Sokol, Alexander Hepburn, Rafael Poyiadzi +3

Predictive systems, in particular machine learning algorithms, can take important, and sometimes legally binding, decisions about our everyday life. In most cases, however, these s…

cs.LG20221 cited

The Weak Supervision Landscape

Rafael Poyiadzi, Daniel Bacaicoa-Barber, Jesus Cid-Sueiro +3

Many ways of annotating a dataset for machine learning classification tasks that go beyond the usual class labels exist in practice. These are of interest as they can simplify or f…

cs.CY20222 cited

Equitable Ability Estimation in Neurodivergent Student Populations with Zero-Inflated Learner Models

Niall Twomey, Sarah McMullan, Anat Elhalal +2

At present, the educational data mining community lacks many tools needed for ensuring equitable ability estimation for Neurodivergent (ND) learners. On one hand, most learner mode…

cs.CV2021

Domain Generalisation for Apparent Emotional Facial Expression Recognition across Age-Groups

Rafael Poyiadzi, Jie Shen, Stavros Petridis +2

Apparent emotional facial expression recognition has attracted a lot of research attention recently. However, the majority of approaches ignore age differences and train a generic…

cs.LG20213 cited

Understanding surrogate explanations: the interplay between complexity, fidelity and coverage

Rafael Poyiadzi, Xavier Renard, Thibault Laugel +2

This paper analyses the fundamental ingredients behind surrogate explanations to provide a better understanding of their inner workings. We start our exposition by considering glob…

cs.LG20213 cited

On the overlooked issue of defining explanation objectives for local-surrogate explainers

Rafael Poyiadzi, Xavier Renard, Thibault Laugel +2

Local surrogate approaches for explaining machine learning model predictions have appealing properties, such as being model-agnostic and flexible in their modelling. Several method…