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DTSTART;TZID=UTC:20240129T090000
DTEND;TZID=UTC:20240205T121500
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CREATED:20231019T102431Z
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SUMMARY:Biases\, Discrimination\, and Fairness\, Arthur Charpentier (Université du Québec à Montréal)
DESCRIPTION:  \n\n\n\n  \n  \nSCHEDULE\n  \nMonday\n  \n29th January 2024 \n5th February 2024\n  \nFrom 9:00 to 12:15\n  \nRoom 2033\n\n\n  \nThursday\n  \n1st February 2024\n  \nFrom 9:00 to 12:15\n  \nRoom 2033\n\n\n\nAims and objectives\nThis course will provide a state-of-the-art\, on fairness and discrimination\, in the context of insurance pricing (and more generally\, predictive models). As explained by Avraham et al. (2014) “‘insurance companies are in the business of discrimination. Insurers attempt to segregate insureds into separate risk pools based on the differences in their risk profiles\, first\, so that different premiums can be charged to the different groups based on their differing risks and\, second\, to incentivize risk reduction by insureds. This is why we let insurers discriminate. There are limits\, however\, to the types of discrimination that are permissible for insurers. But what exactly are those limits and how are they justified?”. First\, we will come back to the specificities of predictive models in insurance. We will come back to the different places where a potential discrimination can intervene\, by insisting on the possible biases in the data\, in the models. We will present in particular the regulations in Europe and North America. In a second step\, we will see how to quantify a possible discrimination\, insisting on the main measures of “group-fairness”\, before discussing the individual approach\, in particular in relation with the causal approaches. Indeed\, the central question of discrimination is “would the price have been different if this person had been a man instead of a woman”. We will see how to build a counterfactual allowing to quantify a possible discrimination. Finally\, we will see how to correct a discrimination\, insisting on the in-processing (throught penalized models) and post-processing approaches (using optimal transport). \n
URL:https://crest.science/event/biases-discrimination-and-fairness-arthur-charpentier-universite-du-quebec-a-montreal/
LOCATION:2033
CATEGORIES:Actuarial Science
ORGANIZER;CN="Christian-Yann%20Robert":MAILTO:christian-yann.robert@ensae.fr
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