21 citations · 31 across the 6 of their papers we have counts for
7 papers
DiConStruct: Causal Concept-based Explanations through Black-Box Distillation
Ricardo Moreira, Jacopo Bono, Mário Cardoso +3
Model interpretability plays a central role in human-AI decision-making systems. Ideally, explanations should be expressed using human-interpretable semantic concepts. Moreover, th…
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…
A Case Study on Designing Evaluations of ML Explanations with Simulated User Studies
Ada Martin, Valerie Chen, Sérgio Jesus +1
When conducting user studies to ascertain the usefulness of model explanations in aiding human decision-making, it is important to use real-world use cases, data, and users. Howeve…
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…
Human-AI Collaboration in Decision-Making: Beyond Learning to Defer
Diogo Leitão, Pedro Saleiro, Mário A. T. Figueiredo +1
Human-AI collaboration (HAIC) in decision-making aims to create synergistic teaming between human decision-makers and AI systems. Learning to defer (L2D) has been presented as a pr…
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…