6 papers
The Illusion of Improvement: Reject Inference Strategies in Credit Scoring
Bruno Scarone, Ricardo Baeza-Yates
Reject inference methods are widely used to mitigate survival bias in credit scoring, yet their effectiveness remains poorly understood. We systematically evaluate several such met…
Improving Model Safety by Targeted Error Correction
Abolfazl Mohammadi-Seif, Ricardo Baeza-Yates
The widespread adoption of machine learning in critical applications demands techniques to mitigate high-consequence errors. Our method utilizes a dual-classifier GBDT pipeline to…
Risk-Calibrated Learning: Minimizing Fatal Errors in Medical AI
Abolfazl Mohammadi-Seif, Ricardo Baeza-Yates
Deep learning models often achieve expert-level accuracy in medical image classification but suffer from a critical flaw: semantic incoherence. These high-confidence mistakes that…
Face Density as a Proxy for Data Complexity: Quantifying the Hardness of Instance Count
Abolfazl Mohammadi-Seif, Ricardo Baeza-Yates
Machine learning progress has historically prioritized model-centric innovations, yet achievable performance is frequently capped by the intrinsic complexity of the data itself. In…
Beyond the Mean: Distribution-Aware Loss Functions for Bimodal Regression
Abolfazl Mohammadi-Seif, Carlos Soares, Rita P. Ribeiro +1
Despite the strong predictive performance achieved by machine learning models across many application domains, assessing their trustworthiness through reliable estimates of predict…
The Impact of Pseudo-Science in Financial Loans Risk Prediction
Bruno Scarone, Ricardo Baeza-Yates
We study the societal impact of pseudo-scientific assumptions for predicting the behavior of people in a straightforward application of machine learning to risk prediction in finan…