Session: How Open Standards Can Heal the Open Wound of AI
The concentration of power within tech oligopolies is well documented. Our personal data has been turned against us and used as power over us, whether it’s a nation-state issue (i.e., TikTok) or a more commercial one (Meta/Amazon). Similarly, we’re witnessing the iteration of large language models (LLMs) at a pace that’s so unprecedented, even AI proponents are suggesting we tap the brakes.
What has been lost in the conversation is the convergence of these phenomena. LLMs that are trained on the data from our lives by those same oligopolies in addition to scraping the web for code, ideas, novels, and blog posts presents a largely uncontemplated threat. And there are no antidotes to this very real and rapidly emergent problem. Lots of people are chattering about how the new “AI overlords” are trained in secret, or about how TikTok is spying on us, but in this talk we’ll explore the connections between the two, and how both are rapidly becoming the same thing.
Scary, right? But open source and open standards can help solve the problem. Rather than governments flailing independently to develop regulatory frameworks that vary by geography and industry, the concept of open standards can be used to assure that not only are regulatory frameworks harmonized and consistent, but that the consumers impacted will have a clear and open voice that cannot be ignored or silenced.
In this session we’ll start by looking at the current stumbling mish mash of potential government remedies being suggested and how they totally miss the point, failing to address the underlying cause and therefore, threat. Then, we’ll examine some ideas for how open source and open standards might guide us all to a conclusion in which AI regulation is at once predictable and systemic for industry and reliable for the end users who need to understand how their data is being used.
Come to this session if you’re concerned about how the AI arms race is threatening individual freedom, and if you want to leverage the power of open source and open standards to build an AI future that serves us, not the other way around.
