Machine learning has enabled significant advancements in AI, but it is not possible to train machine learning models without vast amounts of high-quality data. Bias in a training data set—whether inherent in the data or introduced into the training data set—will result in a machine learning model that reflects that bias.

This webinar explores the risks of inadvertent algorithmic bias and strategies for addressing legal issues surrounding the use of data to train machines.