Data Science Toolkit
DSAC’s Data Science Toolkit offers a series of resources related to data science, including defining big data, data bias, correlation and causation, emphasizing how they may affect actuarial modeling, and consumer experience.
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Data Science Toolkit Resources
Issue Brief
Natural Experiments
The Data Science and Analytics Committee (DSAC) has released an issue paper, Natural Experiments. The paper discusses an approach to establish causation that approximates the construction of a…
Issue Brief
Defining Big Data
The Data Science and Analytics Committee released an issue paper, Defining Big Data. The paper is intended to build a shared language and concepts of big data and associated themes for the actuarial…
Issue Brief
Discrimination: Considerations for Machine Learning, AI Models, and Underlying Data
This issue brief defines discrimination (including distinguishing between discrimination, unfair discrimination, and unjust discrimination); presents practical methods for testing and monitoring…
Issue Brief
An Actuarial View of Data Bias: Definitions, Impacts and Considerations
This issue brief examines the key types of data bias that actuaries may encounter and focuses on the kinds of biases found in modeling data and the implications for algorithmic outcomes.
Issue Brief
Big Data and Algorithms in Actuarial Modeling and Consumer Impacts
The Data Science and Analytics Committee (DSAC) released an issue brief, Big Data and Algorithms in Actuarial Modeling and Consumer Impacts. It outlines big data issues confronting actuaries and…
Issue Brief