Racing to succeed with GenAI: Key takeaways for insurers
Insurers are racing to adopt GenAI, despite concerns. See where the industry is headed.
Tag: data ethics
We’re in our developer era
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
Navigating AI ethics in manufacturing and agriculture: Balancing innovation with responsibility
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
Critical areas for productivity gains with data and AI
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
6 unique ethical challenges for generative AI in 2025
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,
Transforming cancer care with radiomics: The future depends on data sharing
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
5 keys to creating effective model cards
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
A plea for simple, easy-to-understand model cards
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
Model cards: The AI transparency label you need
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
Understanding AI governance
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
5 ethical questions to consider before making AI-driven decisions
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
Exploring generative AI's impact on ethics, data privacy and collaboration
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
Unleashing AI insights: 5 crucial questions for effective deployment
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
Focusing on trustworthy AI on day 2 of SAS Innovate 2024
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
5 key questions to ask when developing trustworthy AI models
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
Consider these questions when managing data during AI development
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
Charting ethical excellence: Navigating the question phase of AI development
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
Breaking the cycle: How sustainable DEI practices promote responsible AI innovation
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
3 organizational strategies for using large language models with confidence
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
Embedding responsible AI best practices within the model lifecycle
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
Can you date an AI?
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
3 attributes of human centricity in trustworthy AI development
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
A practical approach to creating, reading, updating and deleting data in 2024
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
3 industries set to benefit from ethical synthetic data generation in 2024
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
Talking AI in Washington, DC
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
Accountability in AI: A key ingredient for trust
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
Strategies for maintaining trust in our AI-driven world
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
The road to trustworthy AI: Strategies for robustness
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,
SAS leaders explore the latest in tech with theCUBE
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
Striking the balance: Navigating the pitfalls of AI technocentrism
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