Decisions derived from automated systems, e.g. machine learning models increasingly affect our lives. Ensuring that those systems behave fairly, and e.g. do not discriminate against majorities is an important endeavour. In the talk, I would like to give a brief intro to the field of algorithmic fairness. This includes harms that might arise from the use of biased ML models and some intuition regarding how "un-" fairness could be measured along with approaches towards how we might be able to mitigate biases in such systems.