18 citations · 27 across the 4 of their papers we have counts for
4 papers
Fairness-Aware Data Valuation for Supervised Learning
José Pombal, Pedro Saleiro, Mário A. T. Figueiredo +1
Data valuation is a ML field that studies the value of training instances towards a given predictive task. Although data bias is one of the main sources of downstream model unfairn…
Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets for ML Evaluation
Sérgio Jesus, José Pombal, Duarte Alves +5
Evaluating new techniques on realistic datasets plays a crucial role in the development of ML research and its broader adoption by practitioners. In recent years, there has been a…
Understanding Unfairness in Fraud Detection through Model and Data Bias Interactions
José Pombal, André F. Cruz, João Bravo +3
In recent years, machine learning algorithms have become ubiquitous in a multitude of high-stakes decision-making applications. The unparalleled ability of machine learning algorit…
Prisoners of Their Own Devices: How Models Induce Data Bias in Performative Prediction
José Pombal, Pedro Saleiro, Mário A. T. Figueiredo +1
The unparalleled ability of machine learning algorithms to learn patterns from data also enables them to incorporate biases embedded within. A biased model can then make decisions…