1 Introduction  
2 Background: Machine Learning  
2.1 ML and Statistics, Supervised and Unsupervised Learning  
2.2 The Machine Learning Mindset, Procedural and Methodological Benefits, and a Comparison to Statistics  
2.3 Overarching Drivers and Goals of NSOs  
2.4 Applications and Tasks for ML in NSOs  
2.5 Compatibility  
3 Background: Fairness in Machine Learning  
3.1 Algorithmic (Un)Fairness: Sources, Concepts, and Metrics  
3.2 The Human Components  
4 Data Quality Framework Principles  
4.1 Explainability and Interpretability  
4.2 Cost-effectiveness  
4.3 Timeliness  
4.4 Robustness  
4.5 Reproducibility  
4.6 Accuracy  
5 Mapping Fairness to the Quality Dimensions of QF4SA  
5.1 Explainability & Interpretability  
5.2 Cost-effectiveness  
5.3 Timeliness  
5.4 Robustness  
5.5 Reproducibility  
6 Fairness Beyond the Quality Dimensions of QF4SA  
7 Discussion