The field of recommendation systems is moving towards a greater emphasis on fairness and explainability. Researchers are exploring new concepts and techniques to ensure that recommendations are not only accurate but also fair and transparent. This includes the development of new fairness metrics, such as envy-freeness up to one item, and the use of explainability techniques, such as model-agnostic post-hoc explainability and database views as explanations. Noteworthy papers in this area include 'Database Views as Explanations for Relational Deep Learning', which presents a novel framework for explaining machine-learning models over relational databases, and 'We're Still Doing It (All) Wrong: Recommender Systems, Fifteen Years Later', which argues that the field needs a fundamental reframing of what recommender systems research is for and how knowledge is produced and validated. Additionally, 'Model-agnostic post-hoc explainability for recommender systems' develops a systematic application of deletion diagnostics to quantify the influence of specific users or items on the recommender system.