"For a nutritional label for AI, you need to understand how an AI came to a decision, what data that AI used, what the lineage and provenance is of that data and model, what bias existed in that model, what was done to correct that bias"

   — Seth Dobrin, The National

"You’re debiasing the model by changing its perspective on the data;  We’re not fixing the underlying data. We’re tuning the model."

   — Seth Dobrin, The Wall Street Journal

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"The goal was to create a diverse team of highly technical and highly talented individuals, not only because it’s the right thing to do, but because it drives better business outcomes."

   — Seth Dobrin, Forbes

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"Let me start by laying out what we see are the critical components for trustworthy AI. At a high level, there are three things. There's AI ethics, there's governed data and AI, and then there's an open and diverse ecosystem."

  —  Seth Dobrin, Eye on AI Podcast

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"Today, most companies aren't getting the maximum value out of their data. Data is being held captive, it's sitting in silos, and it's becoming increasingly more difficult for users to access it and make sense of it. Enterprise design thinking provides a clear method for helping organizations operationalize data and AI throughout their business by connecting every strategic data initiative to the defined business objectives of the organization."

  —  Seth Dobrin, Protocol

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"Let me start by laying out what we see are the critical components for trustworthy AI. At a high level, there are three things. There's AI ethics, there's governed data and AI, and then there's an open and diverse ecosystem."

  —  Seth Dobrin, Eye on AI Podcast