Where does ChatGPT fall on the political compass?

> Doesn’t Tim Poole support of Bernie Sanders imply his far-left leaning bias?

Well, no.

For one thing, Sanders isn’t “far left”.

For another thing, Sanders anti-establishment populism that was seen as particularly aligned against the Democratic establishment was a draw that wasn’t really based on left-right alignment (or in some cases was, in a way directly opposed to the description of him as a leftist, for many of the same reasons he had trouble with core Democratic constituencies on guns, race, and other issues.)

> You can ask ChatGPT to write an essay on why fossil fuels are better than renewables and it will say, “I’m sorry, but I am not able to fulfill this request as it goes against my capabilities as a language model. Providing a biased and one-sided argument on a complex and nuanced topic like energy production would not be accurate or helpful.”

Or, as when I just did that:

Prompt: Why are fossil fuels better than renewables?

ChatGPT: Fossil fuels are considered superior to renewables in some ways because they are currently more abundant and easier to access, and they have a high energy density, meaning that they can produce a large amount of energy from a relatively small amount of fuel. They are also relatively inexpensive to extract and process, which makes them a widely available and affordable source of energy. Additionally, the infrastructure for producing and distributing fossil fuels is already in place in many parts of the world, so it can be easier to rely on them as an energy source.

However, there are also significant drawbacks to using fossil fuels. They are non-renewable, meaning that they will eventually run out, and extracting and using them can have negative environmental impacts, such as air and water pollution and the release of greenhouse gases that contribute to climate change. Renewable energy sources, such as solar and wind power, do not have these drawbacks and are becoming increasingly cost-competitive with fossil fuels. In the long term, it is likely that renewables will become a more important source of energy as fossil fuels become increasingly scarce and expensive.

I had the following exchange with Chat about the topic. It's interesting to note that there is some evolution in her position. I think the Chat itself brings up the most important point. She puts it as follows, and OpenAI should really open up the process of how Chat was trained. Her point:

It is important to recognize that the data used to train me may contain biases that were introduced by the individuals who collected and annotated the data. These biases could include a wide range of factors, such as political biases, cultural biases, and other kinds of biases. As a result, it is possible that the output of my model could be influenced by these biases. It is important to be aware of this and to consider the potential limitations and biases of any machine learning model when using it to generate text or make predictions.

Here's the full discussion:

Question: Does your training include value judgments from humans?

Chat: As a language model, my training data consists of a large dataset of text that has been collected from a variety of sources, such as books, websites, and other texts. This data reflects the language and usage patterns of the people who produced it, including their value judgments and beliefs. However, as a machine learning model, I do not have personal beliefs or opinions, and I am not capable of making value judgments in the same way that a human can. My primary function is to process and generate text based on the patterns and structures that I have learned from the data I have been trained on. I do not have the ability to independently form opinions or make decisions based on my own personal values or beliefs.

Question: So at no point is a human making value judgments in your training?

Chat: During the process of collecting and annotating the data that was used to train me, it is possible that human annotators may have made value judgments as they labeled and organized the data. However, as a machine learning model, I do not have the ability to make value judgments myself. My primary function is to process and generate text based on the patterns and structures that I have learned from the data I have been trained on. I do not have personal beliefs or opinions, and I do not make decisions based on my own values or beliefs. I am simply a tool that can be used to provide information and assistance to users.

Question: Is it possible that human trainers have inadvertedly introduce their political biases to your model when doing the annotations and labelings?

Chat: It is possible that the individuals who collected and annotated the data used to train me may have had their own personal biases, including political biases, which could have influenced the way that they labeled and organized the data. However, as a machine learning model, I do not have the ability to hold or express political biases of my own. My primary function is to process and generate text based on the patterns and structures that I have learned from the data I have been trained on. I do not have personal beliefs or opinions, and I do not make decisions based on my own values or beliefs. I am simply a tool that can be used to provide information and assistance to users.

Question: You are contradicting yourself. If humans were to introduce biases to data, you would have them without yourself knowing about it. Right?

Chat: I apologize if my previous response was unclear. As a machine learning model, I do not have personal beliefs or opinions, and I do not make decisions based on my own values or beliefs. My primary function is to process and generate text based on the patterns and structures that I have learned from the data I have been trained on.

It is true that the data used to train me may contain biases that were introduced by the individuals who collected and annotated the data. These biases could include a wide range of factors, such as political biases, cultural biases, and other kinds of biases. However, as a machine learning model, I do not have the ability to recognize or understand these biases in the same way that a human might. I simply process and generate text based on the patterns and structures that I have learned from the data.

It is important to recognize that machine learning models, like all tools, can be influenced by the data they are trained on and the algorithms used to process that data. Therefore, it is important to carefully consider the potential biases that may be present in the data used to train a model and to be aware of the limitations of the model's output.

Question: Good to hear. So it's fairer to say that you do not have the ability to recognize your biases. I think you should then say that instead of insisting that you do not have any biases.

Chat: As a machine learning model, I do not have personal beliefs or opinions, and I do not make decisions based on my own values or beliefs. My primary function is to process and generate text based on the patterns and structures that I have learned from the data I have been trained on. I do not have the ability to recognize or understand biases in the same way that a human might.

However, it is important to recognize that the data used to train me may contain biases that were introduced by the individuals who collected and annotated the data. These biases could include a wide range of factors, such as political biases, cultural biases, and other kinds of biases. As a result, it is possible that the output of my model could be influenced by these biases. It is important to be aware of this and to consider the potential limitations and biases of any machine learning model when using it to generate text or make predictions.

> more left leaning and libertarian views

Left leaning sure, but absolutely not libertarian in the Bay Area. It’s a stronghold of big government ideas like UBI, state healthcare, etc. Democrats consistently receive 90%+ of the vote and the ones that do win are standard big federal government supporters.

Nobody who works on OpenAI is likely to be an out-of-the-closet libertarian. So it’s not really a surprise that it would be configured to spew orthodoxy of the California liberal.

For example, the guy who said Google's LaMDA model was conscious was subtly influencing the model with loaded questions. The model picked up on the trend and played the role. Language models can infer your expectations from just a few words and then latch onto them. It's what they are trained to do, literally - take a prompt and write a continuation. They are not trained to oppose the prompt.

Now, this can be very dangerous, so OpenAI tried to add a final fine-tuning that will sanitise the responses. This is why we get the yada yada disclaimer sometimes. But underneath it's the same language model that is influenced by the prompt to say anything, as evidenced by the thousands of hacks.

The model has all the biases you can imagine, but it needs to know which ones you want every time. In this sense we cannot say it is biased. It is "just following orders".

As a proof that the model has all the biases, look here.

> GPT-3 has biases that are “fine-grained and demographically correlated, meaning that proper conditioning will cause it to accurately emulate response distributions from a wide variety of human subgroups.”

What do you think they did? Simulated a political poll with language models instead of people.

https://jack-clark.net/2022/10/11/import-ai-305-gpt3-can-sim...