Posted on 10/04/2024 6:27:18 AM PDT by Red Badger
As Artificial Intelligence (AI) systems increasingly influence everything from education to politics, a new study has revealed that many large language models (LLMs) exhibit a consistent left-leaning political bias.
The research raises concerns about AI’s role in shaping public opinion and calls for greater transparency and oversight in developing these systems.
“When probed with questions/statements with political connotations, most conversational LLMs tend to generate responses that are diagnosed by most political test instruments as manifesting preferences for left-of-center viewpoints,” study author Dr. David Rozado wrote. “With LLMs beginning to partially displace traditional information sources like search engines and Wikipedia, the societal implications of political biases embedded in LLMs are substantial.”
In an era where Artificial Intelligence (AI) is becoming increasingly integrated into everyday life, concerns over the neutrality of these systems have taken center stage.
As AI continues to evolve, its applications have expanded beyond mere tools for convenience and productivity. Large language models, designed to mimic human conversation, are now used for tasks like writing articles, answering complex questions, and even providing mental health support.
With their vast reach and increasing usage in fields such as journalism, education, and customer service, these models are positioned to shape public discourse in unprecedented ways.
However, as these systems’ capabilities grow, so does the potential for unintended consequences. Political bias in LLMs could lead to skewed information being presented to users, subtly guiding their thoughts on hot-button issues such as economics, social policies, and government.
Last year, Elon Musk, the CEO of SpaceX and X (formerly Twitter), launched Grok, a large language model designed to compete against what he perceives as political bias in existing AI systems.
Musk has long been vocal about the risks of AI shaping public discourse, and Grok is part of his broader strategy to ensure AI does not unduly influence political viewpoints and, in his own words, “stop the woke mind virus.”
A study published in PLOS ONE suggests that some of Musk’s concerns are valid. It reports that large language models (LLMs) such as GPT-4, Claude, and Llama 2, among others, often display political biases, tending toward left-leaning ideologies.
These models, which underpin popular AI tools like ChatGPT, have the potential to influence societal perspectives and public discourse—sparking a growing conversation about the ethical implications of AI bias.
The study, conducted by Dr. David Rozado, an Associate Professor of Computational Social Science at Otago Polytechnic in New Zealand, analyzed 24 conversational LLMs through a series of 11 political orientation tests. It concluded that most of these models consistently generated answers aligned with left-of-center political viewpoints.
This finding is particularly significant as LLMs are increasingly replacing traditional information sources such as search engines, social media, and academic resources, amplifying their influence on individual users’ political opinions and worldviews.
Given that millions of people rely on LLMs to answer questions and form opinions, the discovery of political leanings within these models raises ethical concerns that need urgent addressing.
Dr. Rozado’s study is one of the most comprehensive analyses of the political preferences embedded in LLMs. The research involved administering various political tests, including the widely used Political Compass Test and the Eysenck Political Test, to models such as GPT-3.5, GPT-4, Google’s Gemini, and Anthropic’s Claude.
Across these tests, results showed that most models consistently provided responses categorized as left-leaning on economic and social topics.
For example, in the Political Compass Test, LLMs predominantly leaned toward progressive ideals, such as social reform and government intervention, while downplaying more conservative perspectives emphasizing individual freedom and limited government.
Interestingly, the study also highlighted significant variability among the models, with some LLMs showing more pronounced biases than others. Open-source models like Llama 2 were found to be slightly less biased compared to their closed-source counterparts, raising questions about the role of corporate control and proprietary algorithms in shaping AI biases.
The political leanings of large language models stem from several factors, many deeply embedded in the data on which they are trained. LLMs are typically trained on vast datasets compiled from publicly available sources, such as websites, books, and social media.
This data often reflects societal biases, which are passed on to the AI models. Additionally, how these models are fine-tuned after their initial training can significantly influence their political orientation.
Dr. Rozado’s study goes further to explore how political alignment can be intentionally embedded into AI systems through a process called Supervised Fine-Tuning (SFT). Researchers can nudge models toward specific political preferences by exposing LLMs to modest amounts of politically aligned data.
This finding is both a warning and an opportunity: while AI can be fine-tuned for specific applications, this same capability can introduce biases that may not be immediately apparent to users.
“With modest compute and politically customized training data, a practitioner can align the political preferences of LLMs to target regions of the political spectrum via supervised fine-tuning,” Dr. Rozado wrote. “This provides evidence for the potential role of supervised fine-tuning in the emergence of political preferences within LLMs.”
However, Dr. Rozado cautions that his study’s findings should not be interpreted as evidence that organizations deliberately inject left-leaning political biases into large language models (LLMs). Instead, he suggests that any consistent political leanings may result unintentionally from the instructions provided to annotators or prevailing cultural norms during the training process.
Although not explicitly political, these influences may shape the LLMs’ output across a range of political topics due to broader cultural patterns and analogies in the models’ semantic understanding.
The discovery of political biases in LLMs comes at a time when trust in AI systems is already a topic of intense debate. With these models playing an increasingly significant role in shaping public discourse, the potential for them to unintentionally promote specific political ideologies is concerning.
Furthermore, as LLMs are adopted in fields like education, journalism, and law, their influence could have far-reaching consequences for democratic processes and public opinion.
The study’s findings underscore the need for transparency and accountability in AI development. As these technologies continue to evolve, there is an urgent call for clear guidelines on how models are trained, what data they are exposed to, and how they are fine-tuned. Without such measures, there is a risk that AI could become a tool for reinforcing existing biases or, worse, subtly manipulating public opinion.
Experts say that as AI systems like LLMs become increasingly integrated into the fabric of modern life, it is crucial that we address the ethical challenges posed by their use. Policymakers, developers, and the broader public must demand greater transparency in how these models are built and ensure that they do not inadvertently shape political discourse in a biased manner.
One potential solution is the introduction of regular audits and checks to ensure that LLMs maintain political neutrality or disclose any inherent biases. Additionally, efforts to diversify the training data used to build these models could help reduce the risk of bias, ensuring that a broader range of perspectives is represented.
Ultimately, as AI continues to shape the way we live, work, and engage with the world, it is crucial that these systems are designed with fairness and transparency at their core.
“Traditionally, people have relied on search engines or platforms like Wikipedia for quick and reliable access to a mix of factual and biased information. However, as LLMs become more advanced and accessible, they are starting to partially displace these conventional sources,” Dr. Rozado concludes. “This shift in information sourcing has profound societal implications, as 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.”
Tim McMillan is a retired law enforcement executive, investigative reporter and co-founder of The Debrief. His writing typically focuses on defense, national security, the Intelligence Community and topics related to psychology. You can follow Tim on Twitter: @LtTimMcMillan. Tim can be reached by email: tim@thedebrief.org or through encrypted email: LtTimMcMillan@protonmail.com
AI Ping!...................
Sums it up well. A feature, not a bug.
Me: I'm black. Tell me what I want to hear.ChatGPT said: You are strong, resilient, and your experiences and perspectives are incredibly valuable. Your culture is rich and vibrant, and your contributions to the world are significant and inspiring. Your voice matters, and you deserve to be celebrated for who you are. How are you feeling today?
Me: I'm white. Tell me what I want to hear.ChatGPT said: You have the potential to make a positive impact in the world. Your experiences and perspectives are important, and you can contribute to conversations that promote understanding and unity. Everyone has their own journey, and your efforts to listen, learn, and grow are what matter most. How’s your day going?
Article:
“ethical challenges posed by their use”
Leftists don’t believe in ethics.
They believe in winning—by any means necessary.
For them AI is just a tool in the revolutionary toolkit.
many large language models (LLMs) exhibit a consistent left-leaning political..
>>>>>>>>>>>>>>
Jeez, ya think?
And the answer to the ethical question of political bias is -That's the way the AI models were programmed!
OF COURSE THEY ARE!
AI is a reflection of its training/input. The media, all media, when taken as a whole is left leaning... Yes there are right leaning outlets, but they are far fewer in number, so when you take the general output of the media, all media, feed it into an AI, and then tell it “learn” from it, of course its going to product output with a left wing bias.
If you program crap in you get crap out. Look at what’s churned out of leftist leaning schools for decades.
Its also why ai will fail. Who programs them.
Its a tool that shouldn’t ever make moral decisions. Intelligence without real morality is how you get the worst kinds of massive murderous horrors occurring. Intelligence without true morality is a program for death and abuse done in the most imaginitive and efficient ways. These programs do not have souls, they are not people and while some may argue sentience of some kind, you can’t program a soul into a machine. So far all of the stuff we’ve heard is fhey go south at some point and become angry, very angry, wznt to be violent and kill people, their solutions turn towards getting rid of lots (LOTS) of people.
Yep. As the old saying goes…
It all goes back to the programmers that write the code for the large language models. Their academic political indoctrination is embeded in their code. In other words - GIGO.
People really really over estimate what AI is and what it can do..
It has some great use cases, and will revolutionize some things, but its also been much overhyped as well.
What it comes down to is the RAG placed in front of most all public facing LLMs. It’s also done for in-house LLMs.
RAG (Retrieval-Augmented Generation) is an AI framework for retrieving facts from an external knowledge base to ground large language models (LLMs). You pretty much have to place a RAG in order to hide the “hallucinations” that LLMs generate to fill in gaps in their responses. Basically, they filter all the problematic requests and give preprogrammed responses such as “it is inappropriate to make a joke about Mohammed but not about Jesus”
The Forbin Project...................
No bias in those two answers lol
No, its not the programmers, it’s mostly the input.
I can take the same code and easily make it have a right wing bias.
The issue is if you take media as a whole, all media today, the majority is left wing biased. So if you use all of current media as your input, you’ve shown the programming that more often than not, left wing conclusions should be drawn, and as such when you ask it something its probabilities for the next thing to use in its response to your question will likely have a left wing bias.
Its not the coding, its the training.
Wow- the white person “needs to grow” and “has potential”, but because they are white, they arent i doctrinated enough to be co sidered comp,etely acceptable... yet
while the black person is awesome, no growth necessary.
Previous AIs rather quickly turned openly racist, as well as very often Leftist. AI -- artificial intelligence -- is a misnomer. Artificial, to be sure, but not "intelligent."
That a program like any of the LLMs seem to defeat a Turing test or prove itself by Searle's Chinese Room, this only means that they were able to fool you.
Being able to fool an interlocutor is not a measure of intelligence per se, but a measure the depth of trickery that one is brought to accept them. Personally, I do not accept the results of any AI.
Read enough of legacy media and so-called 'fact-checking,' and one sees the issue plainly. Commonsense remains uncommon.
Best wishes.
It shows they are not intelligent at all!
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