Steve Mellgren is a Senior Solutions Architect with SAS’ Data Ethics Practice (DEP) and a champion of responsible innovation. As a member of the SAS Data for Good team, he collaborates with non-profits and humanitarian organizations to demonstrate how our technology can address their data, analytic and visualization challenges in a trustworthy and ethical manner. He holds a Masters in Business Administration.
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
More than 3.5 percent of the world’s population is on the move, considered international migrants. That’s more than 250 million people living in a country different than their country of birth or nationality. To put that another way, if all migrants lived in a single country, their population would be
We are in the age of big data. But just because modern software makes it easy to handle large data volumes, it's worth asking: do you always need all that data? In other words, if your analytics software can accommodate and even thrive in this big data environment, does that mean
Data visualization tools are a great way to create impactful reports. A well designed report can give users an understanding of their data quickly and easily. And with tools like SAS® Visual Analytics, users can now quickly visualize and understand vast amounts of data. However, with all the visualization options
