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 mitigate this risk?
These are the questions Progress sought to answer in an extensive study of AI and ML data bias. As a company focused on helping its customers make smart use of data to drive business outcomes, bias is a key consideration that can’t be ignored. This study can be essential for businesses struggling to understand the issue and the associated risks of letting it go unaddressed.
Some initial findings include:
Download the whitepaper today to access the full report and get the actionable advice your organization needs to prevent data bias.