Zobrazeno 1 - 10
of 3 727
pro vyhledávání: '"Bizarro A"'
Autor:
Silva, Inês Oliveira e, Jesus, Sérgio, Ferreira, Hugo, Saleiro, Pedro, Sousa, Inês, Bizarro, Pedro, Soares, Carlos
Data used by automated decision-making systems, such as Machine Learning models, often reflects discriminatory behavior that occurred in the past. These biases in the training data are sometimes related to label noise, such as in COMPAS, where more A
Externí odkaz:
http://arxiv.org/abs/2410.06214
Analyzing and finding anomalies in multi-dimensional datasets is a cumbersome but vital task across different domains. In the context of financial fraud detection, analysts must quickly identify suspicious activity among transactional data. This is a
Externí odkaz:
http://arxiv.org/abs/2410.00727
Financial fraud is the cause of multi-billion dollar losses annually. Traditionally, fraud detection systems rely on rules due to their transparency and interpretability, key features in domains where decisions need to be explained. However, rule sys
Externí odkaz:
http://arxiv.org/abs/2408.12989
Autor:
Eddin, Ahmad Naser, Bono, Jacopo, Aparício, David, Ferreira, Hugo, Ribeiro, Pedro, Bizarro, Pedro
Continuous-time dynamic graphs (CTDGs) are essential for modeling interconnected, evolving systems. Traditional methods for extracting knowledge from these graphs often depend on feature engineering or deep learning. Feature engineering is limited by
Externí odkaz:
http://arxiv.org/abs/2407.07712
Autor:
Jesus, Sérgio, Saleiro, Pedro, Silva, Inês Oliveira e, Jorge, Beatriz M., Ribeiro, Rita P., Gama, João, Bizarro, Pedro, Ghani, Rayid
Aequitas Flow is an open-source framework and toolkit for end-to-end Fair Machine Learning (ML) experimentation, and benchmarking in Python. This package fills integration gaps that exist in other fair ML packages. In addition to the existing audit c
Externí odkaz:
http://arxiv.org/abs/2405.05809
Autor:
Alves, Jean V., Leitão, Diogo, Jesus, Sérgio, Sampaio, Marco O. P., Liébana, Javier, Saleiro, Pedro, Figueiredo, Mário A. T., Bizarro, Pedro
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. Existing research in L2D overlooks key real-world aspects that impede i
Externí odkaz:
http://arxiv.org/abs/2403.06906
Autor:
Moreira, Ricardo, Bono, Jacopo, Cardoso, Mário, Saleiro, Pedro, Figueiredo, Mário A. T., Bizarro, Pedro
Model interpretability plays a central role in human-AI decision-making systems. Ideally, explanations should be expressed using human-interpretable semantic concepts. Moreover, the causal relations between these concepts should be captured by the ex
Externí odkaz:
http://arxiv.org/abs/2401.08534
Autor:
Alves, Jean V., Leitão, Diogo, Jesus, Sérgio, Sampaio, Marco O. P., Saleiro, Pedro, Figueiredo, Mário A. T., Bizarro, Pedro
Public dataset limitations have significantly hindered the development and benchmarking of learning to defer (L2D) algorithms, which aim to optimally combine human and AI capabilities in hybrid decision-making systems. In such systems, human availabi
Externí odkaz:
http://arxiv.org/abs/2312.13218
Autor:
Julia H. Majolo, João I. B. Gonçalves, Renata P. Souza, Laura C. González, Nathalia Sperotto, Maiele D. Silveira, Sílvia D. Oliveira, Cristiano V. Bizarro, Pablo Machado, Luiz A. Basso, Ana P. D. Souza, Jarbas R. Oliveira, Carlos A. S. Ferreira
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-10 (2024)
Abstract Background: The COVID-19 pandemic has posed significant challenges to global healthcare systems, particularly impacting individuals with pre-existing conditions like hypertension. This study sought to assess the impact of the antihypertensiv
Externí odkaz:
https://doaj.org/article/425aca9cc6a24981817d5262d5dba8ab
Autor:
Jesús Alejandro Aldana López, María del Rocío Serrano Sánchez, Nicolás Páez Venegas, Ana Victoria Chávez Sánchez, Alicia Denisse Flores Bizarro, Jorge Antonio Blanco Sierra, Carlos Alejandro Jarero González, Jaime Carmona Huerta
Publikováno v:
BMC Medical Education, Vol 24, Iss 1, Pp 1-11 (2024)
Abstract Background The World Health Organization’s (WHO) Mental Health Gap Action Programme (mhGAP) aims to provide evidence-based guidelines for the management of mental, neurological, and substance use disorders in non-specialized healthcare set
Externí odkaz:
https://doaj.org/article/bf82a2835b7e4a6fa6b294926cf9736a