Dr. Lance B. Eliot is a world-renowned expert on artificial intelligence with over 8.1+ million amassed views of his AI columns and been featured on CBS 60 Minutes. As a CIO/CTO seasoned executive and high-tech entrepreneur, he combines practical industry experience with deep academic research.
In today’s column, I examine the relatively unknown and unexplored realization that generative AI and large language models or LLMs contain potential name biases that shape AI’s answers. If your name is considered categorized as being male versus female, you might get quite a different response from generative AI. The same applies to other factors including race.
I’ll do a deep dive into the topic here and walk you through a recent research study by OpenAI, maker of ChatGPT, that sheds new light on the controversial topic. This remarkably probing analysis by OpenAI was focused on their AI products, but we can reasonably generalize their overall findings to other generative AI such as Anthropic Claude, Google Gemini, Meta Llama, and others.This analysis of an innovative proposition is part of my ongoing Forbes.
“Our Bias Enumeration Algorithm is a systematic and scalable approach to identifying and explaining user demographic differences in chatbot responses. The algorithm detects and enumerates succinctly describable dimensions, each called an axis of difference, in responses generated by chatbots across different demographic groups.”
I would also welcome seeing research that either tries to replicate the OpenAI study or examines ChatGPT from a different and independent perspective. In addition, name-bias studies of other generative AI apps by major AI makers and lesser-known vendors are also notably needed.
Large Language Models LLM Generative AI Openai Chatgpt GPT-4O O1 Anthropic Claude Google Gemini Meta Llama Name Biases Biased Gender Race AI Ethics AI Law
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