  Since the beginning of their history, insurers have been known to use data to
classify and price risks. As such, they were confronted early on with the
problem of fairness and discrimination associated with data. This issue is
becoming increasingly important with access to more granular and behavioural
data, and is evolving to reflect current technologies and societal concerns. By
looking into earlier debates on discrimination, we show that some algorithmic
biases are a renewed version of older ones, while others show a reversal of the
previous order. Paradoxically, while the insurance practice has not deeply
changed nor are most of these biases new, the machine learning era still deeply
shakes the conception of insurance fairness.
