[lcrmorin]

Difficult to choose one this year (plenty of things happened + plenty of time to read things due to lock down).

"Equality of Opportunity in Supervised Learning" (https://arxiv.org/abs/1610.02413)

It explain the basic concept about fairness in ML. Very practical exemple in my domain knowledge that show the trade-off between fairness of an algo and overall performance (money). Really make you see what may go wrong with bias in ML. It shows, in my opinion, why we will have to regulate ML as corporation aren't really incentivized to deal with fairness. It also shows that there is different notions of fairness. So there will always be something that feel unfair and also doing something can always be interpreted as positive discrimination.