Data scarcity, privacy and bias are just a few reasons why synthetic data is becoming increasingly important. In this Q&A, Brett Wujek, Senior Manager of Product Strategy at SAS, explains why synthetic data will redefine data management and speed up the production of AI and machine learning models while cutting
Investment in AI is an obvious target for the insurance sector. Insurers have always been interested in technology that helps detect and prevent fraud and improve underwriting efficiency while speeding processes and reducing – or at least not increasing – costs. But what is the reality in this highly regulated
The health care community is recognizing the importance of addressing cancer not just as a biological disease but as a multifaceted issue influenced by various social, environmental and economic factors. By incorporating social determinants of health (SDOH) into risk stratification models and utilizing trustworthy AI to eliminate bias, health care providers can
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
Stop bias in its tracks – learn about the value of synthetic data for insurance.
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 (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
The days of one-size-fits-all messaging in the pharmaceutical industry are fading. Today's patients and health care providers (HCPs) expect personalized content across a variety of channels. This is where generative AI (GenAI) can really help execute cross-channel marketing. Reaching the right audience with the right message Imagine a world where
There's a lot to gain for insurers that move fast enough to adopt promising applications of trustworthy AI.
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
Data quality is a cornerstone for integrating large language models (LLMs) into organizations. The adage "garbage in, garbage out" holds particularly true here. High-quality data is the lifeblood that ensures the accuracy, relevance, and reliability of the model's outputs. In a business context, this translates to insights and decisions that
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
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
많은 사람들이 AI의 엄청난 잠재력에 대해 듣고 있으며 AI의 활용에 대해 높은 관심을 가지고 있습니다. 하지만, 최근 들어 AI에 대한 부정적 보도들이 많아지고 있으며, AI를 통한 의사결정에 대한 우려도 커지고 있습니다. AI를 도입하고자 하는 조직의 입장에서는 잘못된 AI의 적용으로 회사의 이름이 뉴스 헤드라인을 장식하는 것을 원하지 않습니다. 또한 차별이나 불공정한
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,
