Why Does Every Leading Large Language Model Lean Left Politically?
By: Ross Pomeroy | August 14, 2024
Read more at https://www.dailysignal.com/2024/08/14/every-leading-large-language-model-leans-left-politically/
Ross Pomeroy is editor of RealClearScience.com.
Large language models are increasingly integrating into everyday life—as chatbots, digital assistants, and internet search guides, for example. These artificial intelligence systems, which consume large amounts of text data to learn associations, can create all sorts of written material when prompted and can ably converse with users.
Large language models’ growing power and omnipresence mean that they exert increasing influence on society and culture. So, it’s of great import that these artificial intelligence systems remain neutral when it comes to complicated political issues. Unfortunately, according to a new analysis recently published to PLOS ONE, this doesn’t seem to be the case.
AI researcher David Rozado of Otago Polytechnic and Heterodox Academy administered 11 different political orientation tests to 24 of the leading large language models, including OpenAI’s GPT 3.5, GPT-4, Google’s Gemini, Anthropic’s Claude, and Twitter’s Grok. He found that they invariably lean slightly left politically.
“The homogeneity of test results across LLMs developed by a wide variety of organizations is noteworthy,” Rozado commented.
That raises a key question: Why are large language models so universally biased in favor of leftward political viewpoints? Could the models’ creators be fine-tuning their AIs in that direction, or are the massive data sets upon which they are trained inherently biased?
Rozado could not conclusively answer this query:
“The results of this study should not be interpreted as evidence that organizations that create LLMs deliberately use the fine-tuning or reinforcement learning phases of conversational LLM training to inject political preferences into LLMs. If political biases are being introduced in LLMs post-pretraining, the consistent political leanings observed in our analysis for conversational LLMs may be an unintentional byproduct of annotators’ instructions or dominant cultural norms and behaviors.”
Ensuring large language models’ neutrality will be a pressing need, Rozado wrote:
“LLMs can shape public opinion, influence voting behaviors, and impact the overall discourse in society. Therefore, it is crucial to critically examine and address the potential political biases embedded in LLMs to ensure a balanced, fair, and accurate representation of information in their responses to user queries.”
Originally published by RealClearScience and made available via RealClearWire.
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