Read more about author Mark Cooper. One of the most significant areas of data ethics concern is bias in generative AI models.  Concerns about algorithmic bias are not new. Associative model (i.e., neural network) bias is not new. Even the ceding of consequential decisions to these models is not new. Just a few examples: AI is used to screen resumes and evaluate candidates. It is used in loan origination, underwriting, and…

Read more about author Mohan Varthakavi. As agentic AI and autonomous systems transform the enterprise landscape, organizations face a new imperative: Fundamentally reimagining data architecture is no longer optional; it’s required for AI success. Many enterprises are coming to the realization that traditional data architectures, which are built for structured data and deterministic workloads, are ill-equipped to support agentic AI’s demands for diverse, unstructured data and probable reasoning. Traditional data…

Read more about author Dev Nag. The federal government’s proposal to impose a 10-year freeze on state-level AI regulation isn’t happening in a vacuum but in direct response to California. The state’s AI Accountability Act (SB 1047) has been making waves for its ambition to hold developers of powerful AI models accountable through mandatory safety testing, public disclosures, and the creation of a new regulatory body.  For some, California is showing leadership. For others,…

Read more about author Subasini Periyakaruppan. In today’s data-driven business environment, the ability to leverage external information sources has become a critical differentiator between market leaders and laggards. Organizations that successfully harness external data don’t just gather more information – they transform how they understand their customers, anticipate market shifts, and identify growth opportunities. However, the path from recognizing the need for external data to successfully implementing it is fraught with…