Zobrazeno 1 - 10
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pro vyhledávání: '"HU, François"'
Autor:
Machado, Agathe Fernandes, Charpentier, Arthur, Flachaire, Emmanuel, Gallic, Ewen, Hu, François
In binary classification tasks, accurate representation of probabilistic predictions is essential for various real-world applications such as predicting payment defaults or assessing medical risks. The model must then be well-calibrated to ensure ali
Externí odkaz:
http://arxiv.org/abs/2408.03421
Autor:
Machado, Agathe Fernandes, Charpentier, Arthur, Flachaire, Emmanuel, Gallic, Ewen, Hu, François
The assessment of binary classifier performance traditionally centers on discriminative ability using metrics, such as accuracy. However, these metrics often disregard the model's inherent uncertainty, especially when dealing with sensitive decision-
Externí odkaz:
http://arxiv.org/abs/2402.07790
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. While enhancing prognostic performance, geospa
Externí odkaz:
http://arxiv.org/abs/2401.16197
Algorithmic fairness has gained prominence due to societal and regulatory concerns about biases in Machine Learning models. Common group fairness metrics like Equalized Odds for classification or Demographic Parity for both classification and regress
Externí odkaz:
http://arxiv.org/abs/2310.20508
In the standard use case of Algorithmic Fairness, the goal is to eliminate the relationship between a sensitive variable and a corresponding score. Throughout recent years, the scientific community has developed a host of definitions and tools to sol
Externí odkaz:
http://arxiv.org/abs/2309.06627
Ensuring trust and accountability in Artificial Intelligence systems demands explainability of its outcomes. Despite significant progress in Explainable AI, human biases still taint a substantial portion of its training data, raising concerns about u
Externí odkaz:
http://arxiv.org/abs/2308.11090
The insurance industry is heavily reliant on predictions of risks based on characteristics of potential customers. Although the use of said models is common, researchers have long pointed out that such practices perpetuate discrimination based on sen
Externí odkaz:
http://arxiv.org/abs/2306.12912
Algorithmic Fairness is an established field in machine learning that aims to reduce biases in data. Recent advances have proposed various methods to ensure fairness in a univariate environment, where the goal is to de-bias a single task. However, ex
Externí odkaz:
http://arxiv.org/abs/2306.10155
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. The initial challenge lies in effectively leveraging unlabeled data in in
Externí odkaz:
http://arxiv.org/abs/2112.09466
Algorithmic Fairness is an established area of machine learning, willing to reduce the influence of hidden bias in the data. Yet, despite its wide range of applications, very few works consider the multi-class classification setting from the fairness
Externí odkaz:
http://arxiv.org/abs/2109.13642