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As machine learning and artificial intelligence (AI) usher in the Fourth Industrial Revolution, it seems like everyone wants to get in on the action. And who can blame them? However, just like humans, AI systems are not exempt from making unfair decisions; sometimes this comes with serious consequences. For example, Amazon created a hiring model to select candidates to job offer, but it was shown that this system was making unfair decisions between men and women, giving a serious advantage to men applying, even when they have the same qualifications. Consequently, an increasing number of regulations are imposed to organisations to ensure model bias and fairness are measured and mitigated. This talk will cover how to appropriately identify and measure model bias and fairness, how to understand potential sources of bias and finally how to mitigate bias.