I had the privilege of joining the Dean’s Speaker Series at UNC Kenan-Flagler Business School, hosted in partnership with the Kenan Institute of Private Enterprise. My sincere thanks to Dean Mary Margaret Frank, the Kenan Institute team and all who made this event possible. It was an honor to share
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
AI agents take on more responsibility in our most sensitive systems – from finance to public safety to health care – one thing is clear: trust can’t just be designed in. It has to be sustained. Continuously. In the first two parts of this series, we explored the ethical foundations
Agentic AI is more than just the next step in automation. It’s a shift toward systems that don’t just respond to commands – they reason, decide and act with a blend of analytics and embedded governance. This is the second blog post in a series exploring how to design AI
Model governance has moved from "nice to have" to a "non-negotiable". As organizations deploy AI across industries like health care, banking and government, the demand for transparency, trust and accountability is louder than ever. SAS experts Briana Ullman, Product Marketing Manager and Vrushali Sawant, Data Scientist, discussed what that looks
AI agents are no longer confined to labs and prototypes. They’re shaping how we live, work and make decisions. From customer service bots and self-driving cars to robotic surgical assistants and virtual companions, these systems now influence real outcomes in society. This is the first blog post in a series
A few years ago, writing emails was a breeze. For someone like me, who loves to write, I didn’t use much effort to craft the right response. It was a part of my daily rhythm. Fast-forward to now and even a simple email gives me pause. I’m often fighting the
Developers are trying to help AI to speak all the world’s languages, but it's a complicated journey. While there are around 7,000 languages spoken globally, digital technology primarily revolves around just a few. English dominates over half of all written content online, even though only around 16% of the global
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)
Fast-paced technology like AI can have barriers to entry. Whether infrastructure, data limitations, talent gaps or complying with rapidly changing regulations. Organizations from health care to manufacturing and the public sector are often stymied by these obstacles that can slow AI adoption and use. Ready-made AI models can bypass the
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
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
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
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,
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
What sets the SAS Model Card apart from previous model cards is the use of descriptive visuals, to make model cards accessible to all personas involved in the analytics process, including data scientists, data engineers, MLOPs engineers, managers, executives, risk managers, business analytics, end-users, and any other stakeholder with access to the SAS Viya environment.
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
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
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
I saw a fascinating Reddit thread titled: "What would you do if your son told you he’s dating an AI?" Here's the post verbatim: "My son (20M) just told my wife and I that he’s been in a relationship with a replika for the past few months. He claims that it’s
AI tools should, ideally, prioritize human well-being, agency and equity, steering clear of harmful consequences. Across various industries, AI is instrumental in solving many challenging problems, such as enhancing tumor assessments in cancer treatment or utilizing natural language processing in banking for customer-centric transformation. The application of AI is also
AI became the unofficial word of 2023 and the craze is likely to continue into 2024 as new creative applications and uses of AI emerge across industries and sectors. But before organizations invest too many resources into foundational AI models, leadership should ensure that the organization has a firm grasp
