Arjun Prakash

AI is generally accepted as necessary for organizations across private and public sectors to build (or maintain) a competitive advantage. However, a major challenge to adopting AI successfully is our ability to build reliable, predictable, and equitable solutions. A critical flaw with traditional approaches to developing AI is the reliance on hand-labeled training datasets and/or “pre-trained” black-box models that are effectively ungovernable and unauditable. In this article, we explore the motivations and challenges for Trustworthy AI that we’ve encountered and discuss how core tenants of Data-Centric AI, including programmatic labeling, help ameliorate them.

Using a data-centric approach to capture the best of rule-based systems and ML models for enterprise AI One of the biggest challenges to making AI practical for the enterprise is keeping the AI application relevant (and therefore valuable) in the face of ever-changing input data and evolving business objectives. Practitioners typically use one of two approaches to build these AI applications:…