As algorithms and AI models are increasingly used to augment, or even replace, traditional human decision-making, there is a growing interest in ensuring that they treat (groups of) people fairly. While fairness is a relatively new design criterion in many areas of algorithmic decision-making (e.g., machine learning), it has a long history of study in social choice theory from microeconomics. In this talk, I will first survey some of the recent advances that boost fairness guarantees in traditional economic problems such as resource allocation, and then show how these can be adapted to many other decision-making paradigms ranging from classification and clustering to recommender systems and conference peer review.