When it comes to AI, it’s garbage in, garbage out: A model is only as good as the data used. In this episode of Utilizing AI, Ayodele Odubela joins Chris Grundemann and Stephen Foskett to discuss practical ways companies can eliminate bias in AI. Data scientists have to focus on building statistical parity to ensure that their data sets are representative of the data to be used in applications. We consider the sociological implications for data modeling, using lending and policing as examples for biased data sets that can lead to errors in modeling. Rather than just believing the answers, we must consider whether the data and the model are unbiased.