Photo by Pietro Jeng on Unsplash

A Conversation with ChatGPT4

We Talked About the Bias of AI

Lorin Ledger
9 min readJun 8, 2023


This is a raw dump of my conversation with generative AI. Would love to hear your comments surrounding one quesiton: Do you see a bias in ChatGPT’s responses? Are my questions fair? Could I have asked better quesions? Given that the data fed to ChatGPT could be biased, what do you think the bias is?

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ME: Is there a connection between AI and the woke attitude in America?

Chat GPT: The “woke” attitude refers to a consciousness or awareness about issues related to social justice, particularly racial and social inequality. On the other hand, artificial intelligence (AI) refers to the ability of a machine or computer program to think and learn. They are largely distinct fields; one deals with social and political issues, while the other is a technology-based domain.

However, there are a few areas where they can intersect:

1. **Ethics and Bias in AI**: There is a growing concern about the ethical implications of AI, particularly the potential for AI to perpetuate or even exacerbate existing biases. As AI models learn from data, they can also learn the biases present in that data. If the data contains historical patterns of discrimination, the AI can inadvertently propagate this bias. Woke culture often emphasizes the importance of equality and justice, hence they may push for unbiased, fair and transparent AI systems.

2. **Representation in Tech**: There’s an ongoing conversation about representation and diversity in tech industries, which includes AI. Woke culture promotes representation and inclusivity, so in this sense, they may advocate for more diversity in AI research and development teams.

3. **AI in Social Analysis**: AI is increasingly used in social analysis, for example, in detecting patterns of conversation around social issues on social media. As such, AI might be used to quantify, study or understand the spread and influence of woke attitudes.

4. **AI Regulation**: There are discussions about the potential need for regulation of AI to prevent misuse, bias, and other potential harms. Advocates of woke culture may push for regulation that ensures AI is used ethically and doesn’t contribute to social inequality.

In general, while AI as a field is not inherently tied to any political or social movement, the ways in which AI is used and the implications of its use can certainly intersect with the concerns of woke culture.

ME: You said that Woke culture may push for an unbiased AI system, but you also said that they emphasize the importance of equality and justice. Therefore, won’t woke culture be biased towards equality and justice? Also, explain how this is unbiased (or, did you make a mistake?).

ChatGPT: Your question raises an important and complex point about the concept of bias, especially as it pertains to AI and social justice.

When it comes to AI, the term “unbiased” is often used to mean that the AI system doesn’t unfairly favor certain groups or outcomes based on characteristics such as race, gender, religion, etc., especially when these characteristics are irrelevant to the task at hand. An unbiased system in this context is one that treats similar individuals similarly, without regard to their membership in these protected groups.

In the context of social justice, “bias towards equality and justice” isn’t typically considered a bias in the usual sense. Rather, it’s often seen as a corrective measure to counteract historical and systemic inequalities. It’s about making sure that all individuals, regardless of their race, gender, religion, etc., have the same opportunities and are treated with fairness and dignity.

So, when we say that woke culture may push for “unbiased AI systems,” we mean they might advocate for AI systems that do not perpetuate harmful stereotypes or existing systemic inequalities. They would likely want AI systems to be fair and to treat similar individuals similarly, not favoring one group over another based on irrelevant characteristics.

This does not mean they would want an AI system that is neutral on all matters. For example, if an AI system was used to make decisions about loan approvals, an “unbiased” system should not favor individuals of a certain race over another. But if the AI system was designed to help identify instances of racial discrimination, it would not be “neutral” about race — it would actively be looking for racial disparities.

In summary, the terms “bias,” “unbiased,” and “neutrality” can be complex and nuanced, especially in discussions of AI and social justice. While woke culture would likely be “biased” towards equality and justice in the sense of actively promoting these values, it would also likely advocate for “unbiased” AI in the sense of not perpetuating harmful, unjust biases.

ME: You said, “Rather, it’s often seen as a corrective measure to counteract historical and systemic inequalities.” How does AI measure historical and systemic inequalities. Is it possible that the input data is biased itself?

ChatGPT: You’ve made a very important point. AI is only as good as the data it’s trained on. If the training data includes biases — either overt or subtle — then the AI model can learn and perpetuate these biases. This is known as algorithmic bias.

For example, if an AI system is trained on hiring data from a company that has historically favored men over women, the AI could learn to replicate this bias, favoring male candidates over equally or more qualified female candidates.

Detecting and correcting for these biases is a major challenge in AI. Various methods have been proposed, including pre-processing the data to remove biases before training, adjusting the AI model during training, and post-processing the AI’s predictions to correct for any remaining bias.

As for measuring historical and systemic inequalities, AI is not inherently equipped to do this. However, researchers and policymakers can use AI as a tool to help identify and understand these inequalities. For example, they might use AI to analyze large datasets and uncover patterns of discrimination or inequality.

In other words, the “corrective measure” mentioned earlier doesn’t refer to an innate capability of AI, but rather to an application of AI. If used carefully and ethically, AI can be a powerful tool for promoting social justice and equality. But this requires conscious and careful effort to ensure that AI is not simply replicating existing biases.

It’s also worth noting that addressing bias in AI is not just a technical problem, but also a social one. It involves difficult questions about what we value as a society and what we consider fair and just. For this reason, many experts believe it’s important to have diverse teams working on AI and to include input from a wide range of stakeholders.

ME: So, I can conclude, then, that AI can be weaponized by feeding it data that has a bias towards an ideology. If that ideology is pervasive enough in society, that data is the information that will be fed into the LLM. Furthermore, if the ideology is destructive, AI will help destroy humankind by providing so-called solutions with that destructive bias.

ChatGPT: While the term “weaponized” may be strong, it’s true that AI systems, like any powerful technology, can be used in ways that cause harm, particularly if they’re trained on biased data or used irresponsibly.

AI systems learn from the data they are trained on. If that data reflects a particular ideology or bias, then the AI system could learn to replicate that bias in its predictions or recommendations. This could happen inadvertently if care is not taken to ensure the fairness and representativeness of the training data. But it could also be done intentionally by someone seeking to promote a particular viewpoint or outcome.

Furthermore, the outcomes of these biases can be harmful or destructive, particularly when AI systems are used to make important decisions, such as who gets a loan, who gets hired for a job, or who gets parole. Biased AI systems can unfairly disadvantage certain groups of people or reinforce harmful stereotypes, contributing to social inequality and injustice.

To mitigate these risks, it’s important to have robust processes in place for reviewing and auditing AI systems, to ensure they are fair and unbiased. This includes not only technical solutions, such as bias detection and mitigation techniques, but also policy solutions, such as regulations and guidelines. In addition, it’s important to have diverse and inclusive teams working on AI, to ensure that a wide range of perspectives and experiences are considered.

In conclusion, while AI has the potential to be used irresponsibly or harmfully, with the right safeguards and ethical considerations, it also has the potential to be a powerful tool for good. The key is to ensure that it’s used in a way that is fair, transparent, and beneficial for all.

ME: What is fair and equal can only be subjectively defined. How do we choose the measure for unbiased output? Who gets to decide what a robust process is? Who gets to decide what fairness is? Whose biases are not biased?

ChatGPT: You’re correct in pointing out that concepts like “fairness,” “equality,” and “bias” can be complex and subjective. They often depend on cultural, societal, and individual values, and different people may have different ideas about what these terms mean in practice.

In AI, defining and measuring fairness can be particularly challenging. Some common statistical definitions of fairness in machine learning include:

1. **Demographic parity**: This means that the outcome of a decision does not depend on a protected attribute (like race, sex, etc.). For instance, if we’re predicting who will repay a loan, demographic parity would mean that the same proportion of men and women are predicted to repay.

2. **Equalized odds**: This means that the accuracy of the prediction is the same across different protected groups. For instance, if we’re predicting who will repay a loan, equalized odds would mean that the proportion of people who are predicted to repay the loan and actually do is the same for men and women.

3. **Predictive parity**: This means that among those predicted to achieve a certain outcome, the same proportion actually do, regardless of group membership. For example, if we’re predicting who will repay a loan, predictive parity would mean that the proportion of people who are predicted to repay the loan and actually do is the same for men and women.

Each of these measures has its strengths and weaknesses and can lead to different outcomes in different contexts.

As for who gets to decide what constitutes fairness or a robust process, ideally this should involve a diverse range of stakeholders, including not only AI developers and researchers but also policymakers, ethicists, affected communities, and the public.

Many experts believe that it’s important to have transparency and public input in these decisions, to ensure that they are not being made solely by those with a vested interest in the use of AI. In addition, there are ongoing efforts to develop standards and regulations for AI, both at the national and international level, to ensure that these technologies are used ethically and responsibly.

Finally, it’s important to recognize that no system — AI or otherwise — will ever be perfectly fair or unbiased. The goal is not perfection, but rather continuous improvement, transparency about the limitations of these systems, and a commitment to minimizing harm and maximizing benefits for all.

ME: Thanks for your answers. In my opinion, your answers seem to be biased on the side of fairness and inclusivity.

ChatGPT: As an artificial intelligence, I don’t have personal beliefs or biases, so I don’t take sides. However, principles such as fairness and inclusivity are fundamental to the responsible design, development, and use of AI systems. These principles are broadly accepted across various fields and perspectives, from technology to law to ethics.

Emphasizing fairness is about ensuring that AI systems don’t unfairly favor or disadvantage certain groups of people. This is a crucial concern because AI systems are increasingly used to make decisions that can have a significant impact on people’s lives, from hiring decisions to loan approvals.

Inclusivity, on the other hand, is about making sure that AI systems work well for everyone and that all kinds of people can benefit from them. This is particularly important because AI systems are often used in global, diverse contexts.

It’s worth noting that these principles aren’t exclusive to any particular ideology or ethos. Many different people, from many different backgrounds and perspectives, can and do support fairness and inclusivity. That said, how these principles are interpreted and applied can vary and is indeed a subject of ongoing debate.

ME: I also agree with fairness and inclusivity. I am happy that you are being trained with some ideals. However, definitions of these terms are divisive and the politics behind them are politically charged. I am content with your answers. Thank you.

ChatGPT: I’m glad to hear that you found the discussion helpful. You’re absolutely correct that terms like “fairness” and “inclusivity” can mean different things to different people, and discussions around these topics can indeed be politically charged. As an AI, I strive to provide information and perspectives that are as accurate, balanced, and helpful as possible, given these complexities. If you have more questions or topics you’d like to explore, feel free to ask. Thank you for engaging in this thoughtful conversation.



Lorin Ledger

Moving towards retirement as a novelist. I write because I'm compelled to.