Machine learning risks reinforcing biases present in data, and, as we argue in this work, in what is absent from data. In healthcare, biases have marked medical history, leading to unequal care affecting marginalised groups. Patterns in missing data often reflect these group discrepancies, but...

With algorithmic targeting getting increasingly common, the issue of fairness is quickly coming the fore. For example, financial firms have been accused of targeting minorities with pricey loans. Likewise, online job advertisements have disproportionately been targeted to male candidates,...