AI Transparency
and the race to regulation
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Friends,
When it comes to AI, especially to a layman like me, transparency around language models and what was used to train them is a little bit of a black box. It’s not just me. Firms generally don’t release this kind of data - no-one really knows how they are created - or the capability.
We worry about what AI can do, and so regulation and monitoring are important. These models and the processes behind them will help shape current and future tools and processes. Many firms don’t share what they are doing because of competition, because they want to maintain pace and not be forced to speed up - and ultimately because of copyright. On that last point, there are already lawsuits in play from publishers and authors against some of the big AI companies citing that they do not have the rights to train their models using their content.
I believe that language is critical in the history of humanity, of communication of law. If a model become powerful in the nuance of communication, it becomes part of the cultural landscape in defining the future. I digress.
The Atlantic summarised the recent findings undertaken by a new report carried out by Stanford:
From the Atlantic:
“Now we have a way to measure just how bad AI’s secrecy problem actually is. Yesterday, Stanford University’s Center for Research on Foundation Models launched a new index that tracks the transparency of 10 major AI companies, including OpenAI, Google, and Anthropic. The researchers graded each company’s flagship model based on whether its developers publicly disclosed 100 different pieces of information—such as what data it was trained on, the wages paid to the data and content-moderation workers who were involved in its development, and when the model should not be used. One point was awarded for each disclosure. Among the 10 companies, the highest-scoring barely got more than 50 out of the 100 possible points; the average is 37. Every company, in other words, gets a resounding F.”
Digging deeper, you can see that it’s as dire as it looks:
The lack of transparency in commercial foundation models (FMs) presents significant challenges across various domains. It hinders other businesses from determining if they can safely build applications using these models, restricts academic research relying on them, complicates policymaking for regulating this technology, and limits consumers' ability to understand model limitations and seek redress for potential harms.
Regulation is a critical phase in where we are. The EU are considering and AI act, but there is quite some swirl on what on what happens when. In the mean time, industries and companies define their own regulations by building the future ahead of what the regulators define. It’s a race.
Stay Curious - and don’t forget to be amazing,
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