The rise of machine-learning applications in domains with critical end-user impact has led to a growing concern about the fairness of learned models, with the goal of avoiding biases that negatively impact specific demographic groups. Most existing bias-mitigation strategies adapt the importance of ...
Explainable AI for Bioinformatics: Methods, Tools and Applications Artificial intelligence (AI) systems utilizing deep neural networks and machine learning (ML) algorithms are widely used for solving critical problems in b... KM Rezaul,I Tanhim,Shajalal MdBeyan OyaLange ChristophCochez MichaelRebholz-...
Would an example of what you're explaining here be somebody submitting a job application to a company that uses a machine learning algorithm to sort applications? That person wouldn't necessarily know if there's a machine learning algorithm sorting these applications, so they certainly wouldn't k...
We invite contributions on all aspects of Artificial Intelligence, including but not limited to: Applications of AI Ethics and AI Knowledge representation Machine learning Other topics in AI Authors of papers and extended abstracts received by 15 September will receive a noti...
As the use of machine learning has increased in areas such ascriminal justice, hiring,health care deliveryand social service interventions, concerns have grown over whether such applications introduce new or amplify existing inequities, especially among racial minorities and people with economic disadvantag...
Here, the intuitive idea is to use fair representation learning from machine learning to train a classifier with a sensitive attribute predictor from the user side to satisfy the fairness goal. However, such fair machine learning models assume entity independence, which differs greatly from CF ...
In the recent years, I developed new tools for applications of optimal transport in machine learning and statistics, including new tests for classification using Wasserstein distance and statistical properties of Fréchet means of distributions seen as Wasserstein Barycenters. This work has important develo...
Statistics - Machine LearningNew social and economic activities massively exploit big data and machine learning algorithms to do inference on people's lives. Applications include automatic curricula evaluation, wage determination, and risk assessment for credits and loans. Recently, many governments and ...
Here, the intuitive idea is to use fair representation learning from machine learning to train a classifier with a sensitive attribute predictor from the user side to satisfy the fairness goal. However, such fair machine learning models assume entity independence, which differs greatly from CF ...
In International conference on machine learning, (pp. 37977–38012). PMLR. Yeh, I. .-C., & Lien, C. .-h. (2009). The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients. Expert Systems with Applications, 36(2), 2473–2480...