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
Customer analytics is becoming imperative for organizations that desire to create and provide personalized and satisfying customer experiences. To understand and anticipate customer needs, preferences and behaviors in a fast-moving marketplace, organizations must make sense of unstructured data, apply industry-specific data and analytics techniques, and optimize every customer-level decision and
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
A recent SAS survey revealed that 80% of business leaders are anxious about GenAI's data privacy and security implications Owing to these and other concerns, Wells Fargo Chief Data Officer Brian Gibbons and an esteemed panel of AI experts, hosted by Wells Fargo Technology Banking, addressed an audience of 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 models have existed since the 1950s, but only in recent years have their application in marketing gained significant attention and media coverage. The impressive abilities of generative AI, particularly in content generation, have sparked excitement within the industry. However, the larger question that arises is: How can generative
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
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
Let me ask: did you believe Facebook CEO Mark Zuckerberg really boasted about the company’s power in a 2019 video, saying, “Imagine this for a second: One man, with total control of billions of people’s stolen data, all their secrets, their lives, their futures.” The video of Zuckerberg purportedly saying
Whether for better or for worse, many people agree that generative AI is a game changer that will revolutionize the way we live and work. Optimists believe that generative AI is an opportunity to improve and expand our technological knowledge. At the same time, catastrophists fear that AI in general
Artificial intelligence (AI) is revolutionizing the way medical professionals deliver care and manage patient data. By harnessing the power of advanced algorithms and machine learning, AI applications contribute to more accurate diagnostics, personalized treatment plans and streamlined administrative processes. The integration of AI into health care is helping create a
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
The significance of upholding trustworthy AI standards transcends multiple industries. Within retail, AI can potentially wield considerable influence in driving customer experiences, optimizing operations, and shaping business strategies. However, it is crucial to ensure that AI technologies are developed and deployed in an ethical manner, prioritizing human well-being and reflecting
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
I recently had two incredible opportunities: to visit the White House for a landmark executive order signing and to make remarks at a US Senate AI Insight Forum. The AI Insight Forum was part of a bipartisan Congressional effort to develop guardrails that ensure artificial intelligence is both transformative and
The relationship between trust and accountability is taking center stage in the global conversations around AI. Accountability and trust are two sides of the same coin. In a relationship – whether romantic, platonic or business, we trust each other to be honest and considerate. Trust is fueled by actions that showcase
In this era of technology dominated by AI and rapid advancements, trust has emerged as a critical pillar of our interconnected world. As Reggie Townsend, Vice President of the Data Ethics Practice at SAS, explains, we must understand that trust is essential for meaningful relationships and the functioning of civil
Most of us have experienced the annoyance of finding an important email in the spam folder of our inbox. If you check the spam folder regularly, you might get annoyed by the incorrect filtering, but at least you’ve probably avoided significant harm. But if you didn’t know to check spam,
When tech experts want to hear directly from thought leaders in the industry, they turn to theCUBE for professionally produced interviews from wherever the global enterprise tech community is gathering. During SAS Explore in Las Vegas, theCUBE stopped by the Innovation Hub to interview a few SAS leaders and get their take
Embracing AI is wonderful. From a practical business perspective, though, there are limits. This issue is broader than AI. However, I’ll constrain the conversation to that for now, given the attention AI is getting these days. Yes, some processes are undoubtedly good candidates for automation, but avoiding “technocentrism” is critical to
AI has captured the general public's imagination, so it was no surprise that it was nearly the only topic of conversation among data professionals at this year’s Chief Data and Analytics Officer (CDAO) conference in London. Of course, AI and machine learning are not new concepts for those working in
AI – just like humans – can carry biases. Unchecked bias can perpetuate power imbalances and marginalize vulnerable communities. Recognizing the potential for bias is one of the first steps toward responsible innovation. Doing so allows users to include diverse needs and perspectives in building inclusive and robust products. Through
As organizations embrace AI, they often handle large volumes of data that power AI systems. Handling data appropriately includes implementing adequate privacy policies and security measures to protect it. Doing so prevents accidental exposure and ensures ethical data use. AI technology often uses sensitive data for creating, training and utilizing models.
As AI rapidly advances over the next several years, I’ve been fortunate to have an active role in helping to guide a responsible path forward when it comes to technology’s impact on our daily lives. Currently, this role includes serving as Vice President for the SAS Data Ethics Practice, as an
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What do you get when you mix leaders from every industry, a hall-of-fame basketball coach and a best-selling author together in three days? SAS Innovate in Orlando, of course. As organizations navigate this changing economic landscape, many are turning to analytics and AI to help them stay ahead of the
Who is responsible for ensuring that new AI technologies are fair and ethical? Does that responsibility land on AI developers? On innovators? On CEOs? Or is the responsibility more widespread? At SAS, we believe that it is everyone’s duty to innovate responsibly with AI. We believe that adhering to trustworthy
I see the term resilience in a lot of business literature these days. Intuitively, it makes sense. After a pandemic, global supply chain disruptions and resulting economic fragility, executives understandably consider adaptability, durability and how best to operate with a strength of character – all attributes that define resilience. Many
