Tag: Data Science and Analytics Committee

An Actuarial View of Correlation and Causation—From Interpretation to Practice to Implications

Mary Pat Campbell

Link: https://www.actuary.org/sites/default/files/2022-07/Correlation.IB_.6.22_final.pdf

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Excerpt:

Examine the quality of the theory behind the correlated variables. Is there good
reason to believe, as validated by research, the variables would occur together? If such
validation does not exist, then the relationship may be spurious. For example, is there
any validation to the relationship between the number of driver deaths in railway
collisions by year (the horizontal axis), and the annual imports of Norwegian crude
oil by the U.S., as depicted below?36 This is an example of a spurious correlation. It is
not clear what a rational explanation would be for this relationship.

Author(s): Data Science and Analytics Committee

Publication Date: July 2022

Publication Site: American Academy of Actuaries

BIG DATA AND ALGORITHMS IN ACTUARIAL MODELING AND CONSUMER IMPACTS

Mary Pat Campbell

Link: https://www.actuary.org/sites/default/files/2022-08/IABAAug2022_Sandberg_Presentation.pdf

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Excerpt:

Systemic Influences and Socioeconomics
❑ Checking for and removing of systemic biases is difficult.
❑ Systemic biases can creep in at every step of the modeling process: data,
algorithms, and validation of results.
❑ Human involvement in designing and coding algorithms, where there is a lack of diversity
among coders
❑ Biases embedded in training datasets
❑ Use of variables that proxy for membership in a protected class
❑ Statistical discrimination profiling shopping behavior, such as price optimization
❑ Technology-facilitated advertising algorithms used in ad targeting and ad delivery

Author(s): David Sandberg, Data Science and Analytics Committee, AAA

Publication Date: August 2022

Publication Site: American Academy of Actuaries