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Found 8 papers, 6 papers with code
From Uncertainty to Precision: Enhancing Binary Classifier Performance through Calibration
The assessment of binary classifier performance traditionally centers on discriminative ability using metrics, such as accuracy.
Geospatial Disparities: A Case Study on Real Estate Prices in Paris
Driven by an increasing prevalence of trackers, ever more IoT sensors, and the declining cost of computing power, geospatial information has come to play a pivotal role in contemporary predictive models.
Parametric Fairness with Statistical Guarantees
Algorithmic fairness has gained prominence due to societal and regulatory concerns about biases in Machine Learning models.
A Sequentially Fair Mechanism for Multiple Sensitive Attributes
Our approach seamlessly extends to approximate fairness, enveloping a framework accommodating the trade-off between risk and unfairness.
Fairness Explainability using Optimal Transport with Applications in Image Classification
Ensuring trust and accountability in Artificial Intelligence systems demands explainability of its outcomes.
Mitigating Discrimination in Insurance with Wasserstein Barycenters
Given that such discrimination can often be attributed to historical data biases, an elimination or at least mitigation is desirable.
Fairness in Multi-Task Learning via Wasserstein Barycenters
Algorithmic Fairness is an established field in machine learning that aims to reduce biases in data.
Fair Active Learning: Solving the Labeling Problem in Insurance
This paper addresses significant obstacles that arise from the widespread use of machine learning models in the insurance industry, with a specific focus on promoting fairness.
