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
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
In 2024, we will witness the proliferation of synthetic data across industries. In 2023, companies experimented with foundational models, and this trend will continue. Organizations see it as an emerging force to reshape industries and change lives. However, the ethical implications can't be overlooked. Let’s explore some industries I think
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
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
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
In today's world, data-driven systems make significant decisions across industries. While these systems can bring many benefits, they can also foster distrust by obscuring how decisions are made. Therefore, transparency within data driven systems is critical to responsible innovation. Transparency requires clear, explainable communication. Since transparency helps people understand how
Generative AI (GenAI) is a category of AI that can create new content, including video, audio, images and text. GenAI has the potential to change the way we approach content creation. It’s gotten much attention lately. Take ChatGPT for example. The AI chatbot has captivated the public’s imagination with clever
In this post, Ajay Agrawal, professor at Toronto's Rotman School of Management, discusses the challenges of unlocking the full potential of AI and ML for businesses and banks. Agrawal explains how the taxi industry in London, UK provides a cautionary tale of the potential impediments to driving value from AI,
Responsible innovation is critical because technology does not exist in a vacuum. It affects us all in unexpected ways. We know analytics has an undeniable impact on society. For example, analytics can help hospitals manage their inventories for essential items like wheelchairs and bladder scanners, help sports teams curate a
One of the reasons I got involved with the trustworthy AI movement is because automated systems enabled by our past will hurt people – at scale – if we aren’t careful. Worse yet, and from a personal perspective, it concerned me that if such systems were deployed in justice and
If you're a marketer, you've likely heard the words "customer experience" tossed about for years. All the buzz is for a good reason: Positive customer experience (CX) increases profitability and improves employee engagement. But amid the spotlight on CX, many marketers are missing a key strategic element necessary to make
I’ve spent months traveling and speaking to business leaders worldwide about trustworthy AI and responsible innovation. On the nights I laid awake in unfamiliar hotel rooms, wishing my body clock would adjust faster than it was, I found joy in watching local television in local languages. While I don’t understand
It’s hard to get through a day in analytics now without hearing the words interpretability and explainability. These terms have become important in a world where machine learning and artificial intelligence (AI) models are becoming more ubiquitous. However, what do the two terms mean—and more importantly, why do they matter?
As head of the SAS Data Ethics Practice, I spend a lot of time contemplating the social implications of AI. Considering its benefits like augmenting medical decisions and pitfalls, making decisions based on biased data results in dire consequences for patients. Such implications have the potential to impact society in a variety
Students at North Carolina State University completed design projects yielding striking visuals, purpose and functionality without unethical design characteristics. If you were to design the ultimate vacation home, you would most certainly consider options and features that speak to your individual preferences and style. It turns out that same inclination
We hear a lot about responsible AI or AI ethics in the marketplace today. At SAS, we believe there should be a larger conversation about responsible innovation. In reality, the decisions made by AI are the outcome of algorithms, data and business processes. This means ethical considerations must be applied
In my last three posts on data ethics, I explored a few of the ethical dilemmas in our data-driven world. From examining the ethical practices of free internet service providers to the problem of high-frequency trading, I’ve come to realize the depth and complexity of these issues. Anyone who's aware of these
Imagine if your ability to feed your family depended upon how fast you could run. Imagine the aisles of your grocery store as lanes on a running track. If you can outrun your fellow shoppers, grab food off the shelves and race through the checkout at the finish line, then
