Food assistance programs like SNAP are lifelines for millions of households. Yet, ensuring their accuracy is an ongoing challenge for state agencies managing them. Even small errors in eligibility decisions can quickly add up, costing states billions, straining resources and undermining trust in the program. The question isn’t whether these
AI systems have problems. They don’t always work right. They hallucinate, lie, or simply forget the all-important closing bracket in a JSON payload. AI systems need accountability, safeguards and oversight. But what should that look like? Human-in-the-loop (HITL) and its companion human-on-the-loop are in a nascent stage. These strategies, designed
Generative AI has stormed into the enterprise toolkit. According to the IDC Data and AI Impact Report: The Trust Imperative, commissioned by SAS, eight in ten organizations are already using it. It’s become a social phenomenon, too. Many have used GenAI tools like Copilot and ChatGPT, talked about their prompts
I've seen firsthand how companies often treat AI governance as a necessary evil – something bolted on after innovation, like installing smoke detectors in a finished building. As both a data scientist building models and an advocate pushing for responsible AI practices, I experienced this dynamic daily. The companies winning
Reinforcement learning is a framework that allows AI systems to learn from experience and make decisions in dynamic environments. One way to explore its potential is by training an AI agent to play a video game in real time. This controlled environment demonstrates principles that apply to everyday business challenges.
Leaders are increasingly preparing their organizations for the latest AI technology. The problem? They aren’t ready and struggle to implement and show ROI. Despite widespread recognition of AI’s potential, a critical trust gap has emerged in the enterprise landscape. A report by the Boston Consulting Group (BCG) reveals that 74%
Learn how parametric insurance works and why synthetic data could make models more accurate.
Clinical trials are the engine behind every new treatment we see – from life-saving cancer drugs to vaccines that help us fight global pandemics. But behind the scenes, running these trials involves juggling massive amounts of complex, sensitive data. For pharmaceutical companies, outdated technology systems can slow everything down, delaying
If you’ve ever heard someone mention a “SAS User Group” and thought, “What does that actually mean?” you’re not alone. These groups are a cornerstone of the SAS ecosystem – but unless you’ve attended a meeting or conference and seen them in action, they can feel a little mysterious. Let’s break
Imagine you're embarking on a road trip to your favorite vacation destination. Like most travelers today, you rely on a GPS to guide you. But not all GPS systems work the same way. Some provide a rigid, pre-planned route that doesn’t account for real-time changes. Others dynamically adapt and reroute
