  Fairness in machine learning is of considerable interest in recent years
owing to the propensity of algorithms trained on historical data to amplify and
perpetuate historical biases. In this paper, we argue for a formal
reconstruction of fairness definitions, not so much to replace existing
definitions but to ground their application in an epistemic setting and allow
for rich environmental modelling. Consequently we look into three notions:
fairness through unawareness, demographic parity and counterfactual fairness,
and formalise these in the epistemic situation calculus.
