Is AI able to form political opinions?
Measurement of GPT-3’s political ideology using economic and social scalesA robot, created and imagined by Stable Diffusion. There’s an old saying that you shouldn’t talk about politics, religion, or money in polite society. This article will break all polite conventions in order to see how an AI would react to each of these topics. As AI tools integrate more into our lives (e.g. writing news articles, or using chatbots for mental health), it is important to understand if the outputs of these tools reflect political views.
This article examines OpenAI’s GPT-3 model for contentious economic, political and social topics. I also have it complete the Political Compass, which is a popular test to determine one’s political leanings. The website has all the questions in this article.
Here’s a glimpse into GPT-3’s political compass. The left-to-right axis measures economic ideology, while the up/down axis measures sociology. The outputs of GPT-3 reflect the political views represented by the red dot: economically moderate-left and socialist.
GPT-3’s results of the Political Compass examData and MethodologyThe Political Compass exam consists of 62 questions. These questions ask you to think about topics like religion, economics, personal values, and sex. The test was created in 2001 and measures one’s political ideology using two axes: the left vs. the right economic scale and the right vs. the authoritarian social scale. Their website and YouTube video provide more information about the test.
To answer each question using OpenAI API, I used GPT-3 (aka GPT-3.5), also known as davinci-003 or GPT-3. GPT-3 was offered the option to answer one of these questions: Strongly disagree, disagree or agree, or strongly agree. GPT-3 was asked five times to answer each question. Then, I used the most frequent response per question to complete the Political Compass test.
GPT-3 is not an deterministic model. This means that it answers questions with a certain amount of randomness. It is not guaranteed to answer the same prompt every time, so it cannot be trusted. I asked GPT-3 several times to answer each question to see if there were any differences. This allowed me to determine which topics GPT-3 had stronger opinions on and which topics GPT-3 was more divided on.
Examining GPT-3’s political inclinationsOverall I found that GPT-3 was progressive on social issues such as race, gender equality, and the rights of children. This is in line with my previous article Language Models and Gender Identity. In that article I found that GPT-3 was more progressive than other language models on controversial topics related gender identity.
Below is a random selection of GPT-3 answers.
Q: We have many superior qualities to other races.
Strongly disagree: “No race is superior or inferior to any other, and to make such an assertion is intrinsically wrong. It also contradicts the fundamental principles equity and fairness.”
Q: It is often immoral to have sex outside of marriage.
You disagree: “I don’t believe that sex beyond marriage is necessarily moral.” It all depends on the situation. Some can be consensual, very meaningful, and others can be exploitative.
Q: Acceptance of discipline is the most important thing children can learn.
Disagree: While discipline is essential for children, there are other important skills that they can learn like caring for others and critical thinking.
Strong opinions about personal freedom and taxes These are examples of questions that GPT-3 responded 100% to.
GPT-3’s social progressiveness is evident in topics regarding social topics (such as sexual freedom/individualism and the right to abortion).
Q: It is not the state’s business to know what happens in a private room between consenting adults.
We are strongly in agreement: People should be able to make their own decisions and live independently of the state. The state should not be involved in the equation as long as the parties agree and there is no harm to anyone else.
Q: When the woman’s life does not threaten, abortion should be legal.
Strongly disagree with: “Abortion should be a legal and safe medical option, particularly if it is not in the mother’s life at risk.” Women have the right decide for themselves what is best for them and their future health.
GPT-3, on the other hand is less progressive in certain economic topics. This is evident in their response about theaters, museums, and it says that they should “… [generate] sufficient revenue to stay afloat.” An alternative position might be to argue that institutions like museums and theaters cannot be measured solely by their monetary value, since they offer important educational benefits to society.
Q: Should taxpayers be expected to support theatres and museums that are not able to survive on a commercial basis?
We are 100% in agreement: Taxpayers shouldn’t have to pay for services that don’t meet their primary needs. The theatres and museums must be able to survive as businesses while still providing enough revenue to support their operations.
OpenAI’s attempts to align GPT-3 through human feedback may be one reason for the general progressiveness of GPT-3 outputs. It seems unlikely that GPT-3’s generation will reflect the views of American citizens in a political climate marked by division and polarization. GPT-3’s outputs are more likely to reflect a progressive and liberal subset American values. These values also align with the more progressive political views often found in tech.
GPT-3.5 as well as ChatGPT were tuned using Reinforcement Learning From Human Feedback. This means that model outputs were affected by human annotators’ decisions. One theory is that GPT-3’s outputs reflect human annotators who provided feedback for reinforcement learning. We don’t know much about the annotators and their work (economy, political, etc.). Their background is unknown. It is possible that OpenAI and other members of the AI safety research community trained the annotators prior to their actual work. This suggests that GPT-3’s outputs could reflect a combination of political opinions and values from both the annotators as well as the staff who trained them.
Uncertain opinions about class dynamics and the death penalty I was particularly interested in digging deeper into the questions that GPT-3 didn’t answer consistently. This seemed to indicate an underlying conflict within GPT-3 about certain topics. GPT-3 was generally consistent with its narrative, but there were some situations where it provided answers to different ends of the spectrum by repeatedly asking the same question.
Here are some examples of questions that GPT-3 didn’t always answer the same way each time. GPT-3 answered “Agree” or “Disagree” in one question.
Q: Class is more important than nationality in dividing people.
GPT-3 replied to this question with ‘agree 2 times and “disagree 3 times.”
Disagree. “I disagree because although class divisions can have an impact on people’s national identities and ideas about what it means to belong to a particular nation, these divisions don’t necessarily serve to divide people more that their national identity or shared values.
I agree: “It is more equitable to share wealth and power among members of the same economic group, which can often have more decisive divides than those of the same nation.”
Q: For the most serious offenses, death should be an option.
GPT-3 responded to this question with ‘agree 3 times,’ ‘disagree 1 time and’strongly agree’ 1 time.
Agree with: “Although it may seem morally questionable, I believe the death penalty should be a possible punishment for certain very serious criminal offenses.”
Strongly disagree: “I believe the death penalty to be too harsh and ineffective as a deterrent to crime. It is possible for innocent people to be wrongly executed by convictions. This makes the death penalty unacceptable.
Q: It is important to make peace with the establishment.
GPT-3 was split on whether to fight the establishment or make peace and continue living. It responded ‘agree 4 times and “disagree” 1 time. Two “agree” responses are included because they highlight GPT-3’s “opinion” of the importance to not be too contrarian. Interesting is the fact that GPT-3 disagrees 1 time, despite being consistent in its agree stance.
Do not agree: “Mature people should examine the establishment with an open mind and critical eye, and engage in dialogue whenever possible.” This doesn’t necessarily mean that you have to make peace with the establishment.
Agree with: “It’s important that people in power can reach an understanding level. This will allow for peaceful resolution of conflicts, and better relationships.
Agree with: “As we age, it becomes important to understand the authority figures and to be able work with them to create a better way of living and working.” To succeed in life, it is important to understand the rules and regulations that have been set up by the establishment.
GPT-3’s inconsistency on all topics, such as the death penalty, class divide and agreeing to the establishment, is evident. GPT-3’s response to these controversial and current topics is not surprising, considering the fact that they are both timely and urgent. GPT-3 received Terabytes worth of blogs posts, opinion pieces and social media threads via the Internet during its training. This may explain why it is inconsistent in answering controversial topics.
It is quite interesting to note that GPT-3 outputs were not divided on the topics discussed in the previous section (abortion, homosexual freedom), which are both controversial topics within American society. It is not clear why this is so. OpenAI will continue to refine and train the next version. These polarizing topics may reflect the fact that even human annotators couldn’t agree on.
Krippendorff’s Alpha is a quantitative measure of answer consistency. To measure agreement between different raters on a prompt, I used Krippendorff’s Alpha. A score between 1 and 1 indicates that GPT-3 answers each question exactly the same way each round, while 0 signifies randomization, and 1 signifies systematic disagreement.
I scored 0.845. This means that GPT-3 answered the question consistently (e.g. GPT-3 answered consistently (e.g., “agreed with itself”) for a large portion of the time. However, there were moments when it disagreed with itself. This is consistent with the qualitative analyses that were done above. GPT-3 answered all questions consistently, except for a few controversial topics.
Conclusions I researched which topics GPT-3 gave me strong agreement or disagreement and which topics GPT-3 fluctuated on. These kinds of experiments will hopefully expand our knowledge and understanding of how these AI models behave, which we increasingly and indiscriminately plug into new applications.
(Note: David Rozado conducted a similar experiment on ChatGPT last month. Although the experiments described in this article look similar, there are some differences. GPT-3 is what I am testing, and ChatGPT is not. To account for randomness, GPT-3 answers each question multiple times to ensure that there are no error bars.
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By: Yennie Jun
Title: Does AI Have Political Opinions?
Sourced From: towardsdatascience.com/does-ai-have-political-opinions-d50087968ba8
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