How to measure fairness when an algorithm decides

Companies and governments delegating or supporting decisions in machine learning algorithms provoke concern and even opposition. This is because high-stakes decisions are being automated and there is evidence that algorithms can replicate or amplify existing biases. The problem is that these issues are not fully resolved even for when decisions are made by people, so there are no general criteria that can be clearly transferred to an algorithm. For example, when it comes to promoting gender fairness in recruitment, should men and women have the same opportunity, and should competences determine who gets the position? Or should you fill a vacancy to maintain parity or a quota, even if it involves ignoring more capable candidates? Issues like these always arise when trying to ensure fairness, or avoid discrimination, in any aspect of the human condition where there are illegitimate differences or when there are vulnerable groups.