The broad objective of this proposal is to develop a theoretically grounded approach for robust fairness in practical and large-scale allocation markets through the integration of Artificial Intelligence (AI), economics, and computation. The proposed research aims at making advances in the following interconnected directions: (i) Matching and allocation under uncertain/noisy preferences, that aims at devising fair solutions that are robust to uncertainty or noise in preference information, (ii) Fairness and diversity in dynamic matching, that combines techniques from online matching and fair division to develop solutions that provide robust fairness and diversity guarantees in dynamic matching markets, and (iii) Incentives and fairness in twosided markets, that provides a systematic approach to blend techniques from stable matching markets with recent advances in fair division to design robust solutions in the presence of strategic behavior.