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ChatGPT quickly found a home in the sophisticated echelons of investment banks and drug design firms. Now, the advanced artificial intelligence is coming to a huge workforce that's largely tech-illiterate and non-English speaking: India's domestic workers, waste recyclers and struggling farmers. In the crowded neighborhoods of Bangalore, ragpickers, cooks and cleaners are taking part in an AI trial aimed at helping some of the nation's poorest people access money from government anti-poverty programs without getting snarled in red tape and corruption. It's an opportunity for people such as Vijayalakshmi, who earns just $100 a month cooking for households in Jayanagar. She only uses her smartphone for basic purposes and speaks no English. Yet, that sweltering afternoon in April, she joined a gaggle of domestic workers to experiment with AI technology.

Today, I'm extremely happy to announce Amazon SageMaker Clarify, a new capability of Amazon SageMaker that helps customers detect bias in machine learning (ML) models, and increase transparency by helping explain model behavior to stakeholders and customers. As ML models are built by training algorithms that learn statistical patterns present in datasets, several questions immediately come to mind. First, can we ever hope to explain why our ML model comes up with a particular prediction? Second, what if our dataset doesn't faithfully describe the real-life problem we were trying to model? Could we even detect such issues?