  Was it fair that Harry was hired but not Barry? Was it fair that Pam was
fired instead of Sam? How can one ensure fairness when an intelligent algorithm
takes these decisions instead of a human? How can one ensure that the decisions
were taken based on merit and not on protected attributes like race or sex?
These are the questions that must be answered now that many decisions in real
life can be made through machine learning. However research in fairness of
algorithms has focused on the counterfactual questions "what if?" or "why?",
whereas in real life most subjective questions of consequence are contrastive:
"why this but not that?". We introduce concepts and mathematical tools using
causal inference to address contrastive fairness in algorithmic decision-making
with illustrative examples.
