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.
Guests and Hosts
- Ayodele Odubela of @CometML is an ML instructor, founder, and author. Connect with Ayodele on Twitter at @DataSciBae
- Chris Grundemann a Gigaom Analyst and VP of Client Success at Myriad360. Connect with Chris on ChrisGrundemann.com on Twitter at @ChrisGrundemann
- Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett
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