Artificial intelligence and machine learning are increasingly being seen as a holy grail for enabling regulatory compliance, particularly for larger companies. However AI and ML rely on huge amounts of data to operate effectively and what happens if the data used to train the machine includes bias? This session will talk about the dangers of conscious and unconscious assumptions about race and gender and other concepts creeping into data sets and how this risk can be mitigated.

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