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Data Challenges in Building a Facial Recognition Model and How to Mitigate Them
Data Challenges in Building ... ................................................ 17 References ................................... ... 14 x 1 4 x 13 6 7 x 7 x 23 2 17 x 7 x 3 84 15 36 Classifier Input ...- Authors: Society of Actuaries
- Date: Jan 2023
- Competency: External Forces & Industry Knowledge
- Topics: Technology & Applications; Technology & Applications>Artificial intelligence & machine learning
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Using Interpretable Machine Learning Methods: An Application to Health Insurance Fraud Detection
................................................ 17 4.2.1 Theory and Description .................. ... ................................................ 17 Section 5: Main Effects.......................- Date: Jan 2024
- Topics: Health & Disability; Health & Disability>Health insurance; Technology & Applications; Technology & Applications>Artificial intelligence & machine learning
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Avoiding Unfair Bias in Insurance Applications of AI Models
................................................ 17 5.1 Value Scoping ............................. ... implications on ethical issues such as unfair bias, 17 David Leslie, “Understanding Artificial Intelligence ...- Authors: Society of Actuaries
- Date: Aug 2022
- Competency: Results-Oriented Solutions
- Topics: Technology & Applications; Technology & Applications>Artificial intelligence & machine learning
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Literature Review: Artificial Intelligence and Its Use in Actuarial Work
which have been shown to be models better 17 Copyright © 2019 Society of Actuaries suited ... learning methods like the denoising autoencoders (DAE)17, ANN, and Light Gradient Boosting Machine (LightGBM)18 ...- Authors: Nicholas Yeo, Min Jyeh Ooi , Jie Yin Liew
- Date: Dec 2019
- Competency: External Forces & Industry Knowledge
- Topics: Technology & Applications; Technology & Applications>Artificial intelligence & machine learning