Since AI is advancing faster than regulation and innovation is outpacing understanding, two principles must rise above the noise: trust and responsible innovation. That was the central message for industry leaders during another day of SAS Innovate 2025. Show host and recently named Chief Operating Officer Gavin Day said this
What insurance companies need to know about the Fundamental Rights Impact Assessment (FRIA) The EU Artificial Intelligence Act (EU AI Act) is ushering in a new era of accountability and transparency for organizations deploying AI systems – particularly in high-impact sectors like insurance. The Fundamental Rights Impact Assessment (FRIA) is
Navigating the interpretability paradox of autonomous AI: Can we maintain trust and transparency without sacrificing performance? AI has rapidly evolved from simple, rule-based systems into sophisticated autonomous agents capable of making decisions without direct human oversight. These advanced systems, known as "agentic AI," go beyond basic automation to independently sense
AI has been largely reactive for years: following human commands, assisting with tasks and providing insights based on predefined rules. But 2025 is shaping up to be the year of agentic AI, where AI agents exist to not just respond to human input but act independently. In the movie I, Robot, there's
Ida B. Wells was a pioneer in the United States who used data collection and analysis methods well ahead of her time. Today, she might be called a data scientist, but I'll call her a boss! Her approach to documenting and analyzing lynchings back in the day (late 19th century)
Can tattooing's artistry survive the emergence of generative AI (GenAI) innovation? And what lessons does this hold for other industries navigating the blend of creativity and technology? When I was in Florida during my winter break, I got a new tattoo. After some conversation back and forth about the ideas
Every year, as Data Privacy Week sharpens the focus on protecting personal information, I’m reminded of a customer event a major North American bank hosted at SAS world headquarters. The bank’s chief data officer led a roundtable discussion on generative AI (GenAI) with a group of esteemed data and AI experts. The
After February 2, companies are legally required to ensure AI literacy for employees under the EU AI Act. The need for AI literacy goes beyond compliance. It is critical for cultivating innovation and competitiveness, aligning with industry best practices. AI will impact every industry, and employees need to be ready
AI is increasingly prevalent in our daily lives, and this trend is unlikely to change anytime soon. This comes with risks, but by understanding these risks, we can build AI systems that mitigate them.
The search to maximize our own productivity is never-ending. We all want to be more efficient in our work and carve out more time with loved ones. As you assess data and AI technology to automate processes and maximize efficiency, you may wonder if it’s truly possible for you or
Insurers are racing to adopt GenAI, despite concerns. See where the industry is headed.
Fearmongers would have you believe that AI will replace us, but I truly believe there has never been a better time in history to be a developer. With the advance of artificial intelligence into generative AI (GenAI) and enhanced computing power, we stand on the brink of a new era
As we move into 2025, AI continues to transform industries in unprecedented ways, driving efficiency, innovation, and productivity. But with this rapid advancement come critical ethical questions. How can we ensure that AI systems protect the rights and well-being of individuals? Manufacturing and agriculture are two essential industries where answering
AI is no longer a futuristic concept – it’s a mainstay in our daily lives, both personally and professionally. In the business world, AI is revolutionizing workflows, driving efficiency and speeding up processes. However, as organizations rush to benefit from this modern technology, they must prioritize the ethical and transparent
Ever since generative AI burst onto the scene, it has sparked a whirlwind of ethical concerns. Unlike traditional AI, which typically analyzes and makes predictions based on existing data, GenAI creates entirely new content – videos, text, audio, code and more. This creative power introduces a new level of risk,
A woman arrives at the emergency room with chest pain. She immediately receives an x-ray. While the radiologist looks at the image, her AI assistant flags anomalies in the patient’s lungs – invisible without the technology. The chest pain turns out to be benign, but sophisticated imaging reveals early-stage lung
We know that building trust in technology is a big deal. It’s no longer enough for AI to just work – we need to understand how it works, what it is doing and whether it’s performing as expected. That’s where model cards come in. If you remember from our previous
Model cards have been around for a few years now and while their purpose is clear – to increase machine learning transparency and to create a way to communicate usage, ethics-informed evaluation, and limitations – they're still evolving. Many companies have tried their hand at creating their own version of
Who has time to be a nutritionist between work deadlines and swim practice? Not this working mom! But my tiny human needs her fuel, you know? This is why I’m thankful for nutrition labels. A quick scan at the grocery store tells me if that cereal is all sugar bombs
AI governance is an all-encompassing strategy that establishes oversight, ensures compliance and develops consistent operations and infrastructure within an organization. It also fosters a human-centric culture. This strategy includes specific governance domains such as data governance and model governance, necessary for a unified AI approach. Why AI governance matters The
So now you're ready to make decisions with your models. You’ve asked many questions along the way and should now understand what’s all at play. But how can you ensure these decisions are trustworthy and ethical? Transparency is crucial. Sharing the reasoning behind our choices in relationships, whether at home
Generative AI (GenAI) is booming. It’s not just a trend; it’s produced a seismic shift in how we approach innovation and technology. SAS Innovate 2024 has moved on from Las Vegas and is now on tour across the world. If you want a recap of what happened in Vegas or
Deploying AI insights isn't just about pushing buttons and hoping for the best. The deployment phase is a pivotal moment where technology and ethics meet. When transitioning AI models from development to real-world use, prioritizing trustworthiness remains important. It’s not just about algorithms; it’s about how AI impacts people and
Have you ever heard the saying, “Products that don’t perform won’t be trusted, and products that can’t be trusted aren’t worth the machines they’re coded on”? Maybe not, because I just made it up. But at SAS, as we continue to create products that perform and change the world, we
They say trust is a delicate thing. It takes a long time to build trust. It’s easy to lose and hard to get back. Trust is built on consistent and ethical actions. Therefore, we must be intentional when creating AI models. It's crucial to ensure that trustworthiness is embedded
Trustworthy AI is dependent on a solid foundation of data. If you bake a cake with missing, expired or otherwise low-quality ingredients, it will result in a subpar dessert. The same holds for developing AI systems to handle large amounts of data. Data is at the heart of every AI
As organizations infuse trustworthy practices into the fabric of AI systems, remembering that trustworthiness should never be an afterthought is important. Pursuing trustworthy AI is not a distant destination but an ongoing journey that raises questions at every turn. For that, we have meticulously built an ethical and reliable AI
Black History Month seems like an opportune time to comment on the recent pullback of DEI initiatives, particularly in tech, as a reminder of a historical story. It’s a story of the perpetual dance between social progress and regression as America’s historically marginalized communities are concerned. However, the significance of
Large language models (LLMs) are at the forefront of today’s AI, not merely as technological marvels but as transformative agents reshaping how businesses innovate, operate and deliver value. Think of them as the wizards of words, capable of understanding language and transforming it in ways that benefit organizations. However, as
The National Institute of Standards and Technology (NIST) has released a set of standards and best practices within their AI Risk Management Framework for building responsible AI systems. NIST sits under the U.S. Department of Commerce and their mission is to promote innovation and industrial competitiveness. NIST offers a portfolio
