no code implementations • 11 Mar 2024 • Jean V. Alves, Diogo Leitão, Sérgio Jesus, Marco O. P. Sampaio, Javier Liébana, Pedro Saleiro, Mário A. T. Figueiredo, Pedro Bizarro
Learning to defer (L2D) aims to improve human-AI collaboration systems by learning how to defer decisions to humans when they are more likely to be correct than an ML classifier.
1 code implementation • 20 Dec 2023 • Jean V. Alves, Diogo Leitão, Sérgio Jesus, Marco O. P. Sampaio, Pedro Saleiro, Mário A. T. Figueiredo, Pedro Bizarro
Financial fraud detection is a high-stakes setting where algorithms and human experts often work in tandem; however, there are no publicly available datasets for L2D concerning this important application of human-AI teaming.
no code implementations • 15 Feb 2023 • Ada Martin, Valerie Chen, Sérgio Jesus, Pedro Saleiro
We hope that this work motivates further study of when and how SimEvals should be used to aid in the design of real-world evaluations.
2 code implementations • 24 Nov 2022 • Sérgio Jesus, José Pombal, Duarte Alves, André Cruz, Pedro Saleiro, Rita P. Ribeiro, João Gama, Pedro Bizarro
The suite was generated by applying state-of-the-art tabular data generation techniques on an anonymized, real-world bank account opening fraud detection dataset.
1 code implementation • 16 Sep 2022 • André F Cruz, Catarina Belém, Sérgio Jesus, João Bravo, Pedro Saleiro, Pedro Bizarro
Tabular data is prevalent in many high-stakes domains, such as financial services or public policy.
no code implementations • 24 Jun 2022 • Kasun Amarasinghe, Kit T. Rodolfa, Sérgio Jesus, Valerie Chen, Vladimir Balayan, Pedro Saleiro, Pedro Bizarro, Ameet Talwalkar, Rayid Ghani
Most existing evaluations of explainable machine learning (ML) methods rely on simplifying assumptions or proxies that do not reflect real-world use cases; the handful of more robust evaluations on real-world settings have shortcomings in their design, resulting in limited conclusions of methods' real-world utility.
no code implementations • 21 Jan 2021 • Sérgio Jesus, Catarina Belém, Vladimir Balayan, João Bento, Pedro Saleiro, Pedro Bizarro, João Gama
We conducted an experiment following XAI Test to evaluate three popular post-hoc explanation methods -- LIME, SHAP, and TreeInterpreter -- on a real-world fraud detection task, with real data, a deployed ML model, and fraud analysts.
Decision Making Explainable Artificial Intelligence (XAI) +1