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Building Transparency and Fighting Bias in AI with Ayodele Odubela | Utilizing AI: 2×09

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.

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About the author

Stephen Foskett

Stephen Foskett is an active participant in the world of enterprise information technology, currently focusing on enterprise storage, server virtualization, networking, and cloud computing. He organizes the popular Tech Field Day event series for Gestalt IT and runs Foskett Services. A long-time voice in the storage industry, Stephen has authored numerous articles for industry publications, and is a popular presenter at industry events. He can be found online at,, and on Twitter at @SFoskett.

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