I highly recommend Ryan’s latest blog on how Retrieval Augmented Generation (RAG) can revolutionize your enterprise data strategy. He explains how AI leverages RAG to enhance data access and productivity, blending the power of Large Language Models (LLMs) with your organization’s unique information. It’s a great read if you’re looking to unlock the full potential of your data while maintaining security and efficiency. Check it out here: Read the blog.
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It is interesting that the requirement for logging is not at this stage driven by regulation. Should a full audit be required, having this capability already embedded in the function should not be overlooked. I think the question is does this function stand the test should it need to be evidenced in a court of law?
thanks
Chetan Visrolia
I am not a lawyer, but I’m not surprised if the law lags here. In the past, if an application was accused of being discriminatory, the author can produce the source code that defines why the output is the way it is. But today, the source code doesn’t define the output. The source data does. And AI is designed specifically to be non-deterministic. Meaning that the same input data can create different output data for each person or each request. The whole process is just so different, I suspect it will take time for the legal system to adapt. It’s not clear to me at all what that future system will look like, but I’d love to hear more speculation.
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I guess the industry will plough on like the social media folk until legislation is adopted. There is no reason to make an investment where there are no material returns.
Seems as though the Enterprise, Financials as an example will lead requiremnents here. Columnar level and even cell level lineage required