Data documentation best practices in preparation for the EU AI Act
Evidence-based decision making is the best option available to us today. But error in data collection, labelling and analysis is commonplace. What are the pitfalls we should be looking out for? How do we avoid corrupting evidence-based insight with false assumptions around the data process? In this webinar, we look at some common errors and how to fix them. Quality insight is only as good as the person examining the numbers.