Bias and Fairness in Large Language Models

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Bias and Fairness in Large Language Models

June 2, 2026
mike@standardsmichigan.com

https://thebrandhopper.com/2020/11/13/marketing-concept-diffusion-of-innovation/
In the early stages of a new technology, innovation is fluid, experimental, and highly uncertain. Multiple competing designs, architectures, and approaches coexist as inventors, startups, and firms explore possibilities. Without established standards, there is no dominant design—products vary widely in features, interfaces, and performance.  This “pre-standard” or “ferment” phase fuels rapid, radical innovation.  Engineers iterate quickly, creativity thrives, and breakthroughs emerge through trial-and-error.

 

However, fragmentation creates compatibility issues, high risk for adopters, and market confusion. Investment is speculative, and many early solutions eventually fail. Only after a dominant design or technical standard wins (through market forces, regulation, or consensus) does the industry stabilize. Innovation then shifts from product architecture to incremental improvements, manufacturing efficiency, and complementary services. The early chaotic period, though messy, is essential—it determines which technologies shape the future.

 

Today we sort through the literature on the stabilization of American English as the de-facto “Language of the Internet” and the Artificial Intelligence zietgeist

Readings: Bias and Fairness in Large Language Models


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