Introduction When it comes to artificial intelligence (AI) and machine learning (ML), the algorithms are only as good as the data used to create them. If data sets are flawed—or worse, biased—incorrect assumptions will be baked into every resulting decision. Sound farfetched? It’s not. Instances of data bias are well documented, but how big is the problem? And what, if anything, are businesses doing to correct

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