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Interpretable Machine Learning for Predictive Analytics in Employee Benefits Insurance
In recent years, many machine learning methods have been developed for predictive analytics and automated decision making. However, the lack of explanation resulted in both practical and ethical issues. In this project, we will employ and advance interpretable machine learning methods for various predictive analytics tasks in employee benefit insurance. The proposed methods can be used by the partner organization to improve transparency and hence trust in a wide range of applications that involve predictive analytics.
Faculty Supervisor:
Majid Komeili
Student:
Seyed Omid Davoudi;Mohammad Nokhbeh Zaeem
Partner:
Global IQX
Discipline:
Computer science
Sector:
Finance, insurance and business
University:
Carleton University
Program:
Accelerate
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