California Insider

“What makes these fires bigger has a lot to do with how we’ve mismanaged the landscapes for the last 100 years. Fire suppression, changes in grazing patterns, loss of wetlands. We allow shrub lands and grasses to build up underneath it, add more fuel to the fire, and you have more urban development, just the more asphalt, the more cement you’re raising temperatures,” says professor Jim Steele, Director emeritus of San Francisco State University’s Sierra Nevada Field Campus.

Siyamak sits down with ecologist and author Jim Steele, who has studied the factors behind the increasing wildfires in California, and Ian Faloona, an associate professor and Biomicrometeorologist at the University of California Davis, who has been researching how wildfires impact air quality in the state.

“It’s a different type of chemical environment. This new source, not only has it become visible, it’s growing tremendous. It’s a new era of how we think about air pollution,” professor Faloona says.
*Views expressed in this video/article are opinions of the author and do not necessarily reflect the views of California Insider.

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By Ross Pomeroy

Large language models (LLMs) are increasingly integrating into everyday life – as chatbots, digital assistants, and internet search guides, for example. These artificial intelligence (AI) 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. LLMs’ 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 LLMs, 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.

This raises a key question: why are LLMs 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 datasets 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 LLM 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.”

Source: Rozado D (2024) The political preferences of LLMs. PLOS ONE 19(7): e0306621. https://doi.org/10.1371/journal.pone.0306621

This article was originally published by RealClearScience and made available via RealClearWire.