Vrushali Sawant is a data scientist with SAS's Data Ethics Practice (DEP), steering the practical implementation of fairness and trustworthy principles into the SAS platform. She regularly writes and speaks about practical strategies for implementing trustworthy AI systems. With a background in analytical consulting, data management and data visualization she has been helping customers make data driven decisions for a decade. She holds a Masters in Data Science and Masters in Business Administration Degree.
AI agents are no longer confined to labs and prototypes. They’re shaping how we live, work and make decisions. From customer service bots and self-driving cars to robotic surgical assistants and virtual companions, these systems now influence real outcomes in society. But with this growing influence comes an urgent question:
Developers are trying to help AI to speak all the world’s languages, but it's a complicated journey. While there are around 7,000 languages spoken globally, digital technology primarily revolves around just a few. English dominates over half of all written content online, even though only around 16% of the global
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,
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
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
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
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
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
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
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
