Recent McKinsey research cites the top five limitations that prevent companies from adopting AI technology. Training data strategy is a common thread. Companies face challenges obtaining enough AI training data, developing strategies for robust data quality and ensuring that bias does not occur. In this presentation, we’ll explore training data strategies that avoid bias in the data and that consider legal and ethical factors. You will learn about common types of bias, how bias can creep into datasets, the impact of bias, how to avoid bias and how to test your model for bias. We’ll discuss legal and ethical considerations in data sourcing, including real cases where legal and ethical complications can arise, the impact of these complications, and best practices for avoiding or mitigating them.