The field of machine learning is moving towards a greater emphasis on fairness and transparency, with a focus on developing methods that can provide strong guarantees on the fairness of the learned models. Recent work has explored the use of Lagrangian duality and PAC-Bayes to address constrained learning problems and provide generalization guarantees. There is also a growing recognition of the importance of high-quality datasets and robust evaluation methodologies in ensuring the reliability and reproducibility of fair machine learning research. Notable papers in this area include: AL-CoLe, which establishes strong duality results for Augmented Lagrangian methods in non-convex settings. Fair Representation Learning with Controllable High Confidence Guarantees via Adversarial Inference, which proposes a framework for learning representations that achieve high-confidence fairness guarantees. Bias Begins with Data: The FairGround Corpus, which presents a unified framework and dataset corpus for advancing reproducible research in fair machine learning classification.