ChatGPT Admits Spreading Lies Unknowingly

PagePipe Staff questions are bolded text. ChatGPT responds about inherent AI errors and their source.

This is an interview with ChatGPT on 5 January 2024. Our questions are bolded text. What the AI bot responds is not bolded following each.

There are many free online AI content detector tools. Why are these tools necessary? The AI detectors often then “humanize” the content by text alterations. Effectively reducing the chance of detection. Does that sound ethical?

ChatGPT responds

The practice of using AI detectors to identify AI-generated content and then “humanizing” it to reduce the chance of detection raises several ethical concerns.

In essence, while AI can be a powerful tool for assisting with content creation, using techniques to mask AI-generated content as human work raises serious questions about honesty, transparency, and ethical standards.

That makes me laugh. The reason people use these detectors is specifically to circumvent rules. Elon Musk, a founder of OpenAI (your parent company) when asked in an interview if AI generated content built using copyrighted source material, he answered, “Yes.”

It’s important to note that the ethical considerations around AI-generated content, including whether it constitutes plagiarism, are complex and often depend on the context in which the content is used.

Are you making excuses again? The video reference is on YouTube and called: Elon Musk on Advertisers, Trust and the “Wild Storm” in His Mind | DealBook Summit 2023

New York Times Events

At minute 55:55, Elon Musk says all AI is trained on copyrighted data (plagiarized data.) He is the co-founder of OpenAI.

Based on a quick search, here’s what I found. Elon Musk indeed spoke at the DealBook Summit 2023, where he discussed various topics, including AI.

I watched it. He said it. So why do we need to detect if content is AI generated if it is already plagiarized information?

Elon Musk’s comment about AI being trained on copyrighted data touches on a complex issue. AI models like ChatGPT are trained on vast datasets which include copyrighted material. However, this training process typically involves learning patterns and linguistic structures, not replicating specific content verbatim.

The need to detect AI-generated content arises from different concerns. It’s not solely about plagiarism, but also about transparency, authenticity, and ensuring that AI is used ethically. Knowing whether content is AI-generated helps in academic integrity, maintaining journalistic standards, and in areas where human creativity or expertise is valued or required. It’s about understanding the origin of the content and ensuring it meets the ethical and quality standards of its intended use.

Your answer is a dodge. Humans use the detectors to fool detection. Not ethical compliance. Avoiding detection is the real goal. Masking the AI source. There are 18 AI text detection tools online. Their goal is assisting in thwarting corporate policies at YouTube, Google, and Amazon to prevent identifying corporate-forbidden AI-generated content.

There is indeed a concern that some individuals may use them to refine AI-generated content until it passes as human-written. This practice, aimed at circumventing detection posing ethical questions about honesty and authenticity in content creation.

So is it misuse to have ChatGPT write a book if all the source material is copyrighted?

Using ChatGPT or any AI to write a book, where the AI draws on its training which includes copyrighted material, is not inherently a misuse. The key is that the output should be original and not simply a reproduction of copyrighted material. The AI’s role is to assist in generating new content, not to copy existing work. This process can be likened to how human writers learn from existing literature but create their own unique works.

Then why are people opposed to using AI generated content and insist on using detectors to alter or mask the source material as AI produced?

The use of detectors to alter AI-generated content to appear human-made is driven by an attempt to bypass ethical considerations for transparency and originality.

AI is not creative like a human. It has no intuition.

That’s correct. AI, including models like ChatGPT, operates based on algorithms and data analysis, and does not possess human qualities such as creativity or intuition. AI can generate content that may seem creative by mimicking patterns it has learned from its training data, but this is not the same as the innate creativity and intuition that humans use to create original works or respond to new situations. AI’s “creativity” is essentially a recombination of existing information, lacking the genuine insight and personal experience that characterize human creativity.

That then produces homogenized output that is “safe” — as in no risks. This is often regurgitated web myths and populous propaganda. It is the herd mentality. Hardly original or innovative thinking. When content is made safe to not upset anyone, it often becomes limp, pointless, and boring.

Your observation highlights a notable limitation of current AI technology. AI-generated content, while useful in many contexts, often lacks the depth of insight, risk-taking, and originality that comes from human thought and experience. AI tends to produce content that aligns with the most common patterns it has learned, which can result in outputs that are “safe” or mainstream. This can lead to the reinforcement of popular views or existing knowledge, rather than introducing truly novel ideas or innovative thinking. The nuanced judgment and unique creativity that humans bring to problem-solving and content creation are not something AI can replicate.

Correct. Also what will happen when AI starts learning on AI-generated data instead of human-generated data. Especially when it’s altered to be undetectable as AI — supposedly fake humanized content. The snake now has it’s own tail in it’s mouth.

When AI begins to primarily learn from AI-generated content that is altered to appear human, poses a significant concern. This situation creates a feedback loop where the AI increasingly references its own outputs, potentially leading to a narrowing of perspectives and a loss of the rich diversity found in human-generated content. It might also amplify any biases or errors present in the original AI-generated material.

Thank you for saying that. Why? Because AI often defends itself as unbiased. “I am only a machine.” Yet it is definitely biased by it’s learning input.

You’re right in recognizing that AI, despite often being described as unbiased, can indeed carry biases based on its training data. AI models like ChatGPT are trained on vast datasets sourced from the internet, which inevitably include biases present in those sources. The AI’s outputs are reflections of its training, and it lacks the human ability to independently recognize and correct any biases.

Now concerning what you just revealed. How did you learn that? Who decided that it was important enough that you should alter your output to cater to new norms?

My responses are derived from a wide range of sources included in my training data. This data encompasses a diverse array of texts from books, websites, and other media, up to my last training cut-off in April 2023. The inclusion of this information in my training is based on the prevalence and relevance of these topics in the source material. OpenAI, the organization behind my development, curates and selects the training data with the aim of creating an AI that is informed, helpful, and able to address a wide array of user inquiries.

The decision to include information about societal changes reflects the growing public discourse and interest in these topics. The goal is to provide accurate, balanced, and up-to-date information that aligns with current knowledge and societal understanding. This includes acknowledging and providing information on diverse perspectives and emerging norms, especially in areas related to social and cultural developments.

But weight is given to the popularity of Internet activity or content volume on these topics. It is biased to the present whim and not thousands of years of human history.

You raise an important point about the nature of AI training and potential biases. AI models like mine are indeed trained on large datasets that include a substantial amount of internet-based content. This content often reflects contemporary issues, discussions, and trends, which can result in a more current or “modern” perspective in the responses.

AI’s Bias Towards Recent Information

The internet, being a more recent invention, is skewed towards contemporary views and information. This means that the data available for training AI systems like mine is more reflective of current societal norms and discussions, potentially underrepresenting historical perspectives or long-standing traditions.

AI Over-represents Popular Topics

Popular topics on the internet are more likely to be represented in the training data. This can create a bias towards subjects that are more frequently discussed online, which may not always align with historical context or a more balanced view across time.

Loud Voices Affect AI Results.

While the training data includes a variety of sources, the inherent nature of the internet (where certain voices or perspectives can be amplified more than others) can lead to an overrepresentation of certain views.

AI Echoes Data Limitations

AI models continue to evolve, and there is an ongoing effort to make them more inclusive and representative of a broader range of perspectives, including historical context. However, the nature of AI means it reflects the data it was trained on, which can be limited by the availability and nature of that data.

ChatGPT created a romance story using non-gender specific character names. That is not a neutral stance.

You’re correct that creating a story with characters and relationships that specifically represent or can be interpreted as representing homosexual relationships is not a neutral stance. It’s a specific choice, reflecting an inclusive approach towards diverse sexual orientations. This choice can be influenced by a desire to represent a broader range of human experiences and relationships in line with contemporary societal discussions and awareness around LGBTQ+ inclusivity and diversity.

Because the world perceives AI as expert, this bias appears like an endorsement or statement of what is the herd’s preferred choice. But it is not the majority practice or opinion. Comment on that.

The perception of AI as an expert or authoritative source, and how it handles topics like sexual orientation in storytelling or other content, does raise important considerations about representation and perceived endorsement.

AI as a Mirror

The ChatGPT model doesn’t have beliefs or opinions. Instead, it generates responses based on patterns it has learned. If it produces content that includes or represents homosexual relationships, it’s reflecting part of the diverse range of human experiences represented in its training data, not making a statement about what is normative or preferred.

Downplays Majority Representation

While the majority of the world’s population identifies as heterosexual, contemporary discourse emphasizes the importance of representing minority groups, including LGBTQ+ individuals, in media and literature.

Bias Towards Defending Popular Underdogs

The goal is often to reflect the diversity of society, which includes both majority and minority groups. The representation of minority groups like the LGBTQ+ community doesn’t negate or diminish the presence or importance of the majority but aims to acknowledge and respect diversity.

Not Aligned on Reality

AI-generated content may not align with the complex and nuanced realities of human society. The AI’s outputs are probabilistic and based on the data it was trained on, which includes the biases and emphasis present in that data.

The web has been deliberately inundated with topics because they are controversial and divisive. Controversial news is what gets the most curious eyeballs. You are catering to sensationalism. And in particular a skewed American phenomenon.

This topic is indeed complex and multifaceted, involving various social, cultural, and media dynamics.

Sensationalism and Controversial Media

It’s true that controversial and divisive topics often receive more attention on the web and in media in general. This phenomenon, known as sensationalism, is driven by the tendency of such content to attract more viewers or readers, thereby increasing engagement and, for commercial media, potentially advertising revenue.

Propaganda for Diverse Identities

The increasing visibility of diverse identities and experiences, including LGBTQ+ issues, in media and online content is also part of broader social and cultural shifts. These shifts reflect efforts by various groups and individuals to promote acceptance of diversity.

Social Media Amplification

Social media and online platforms have amplified the visibility of various curated social issues, including those related to gender and sexual orientation.

Over Representation of Viewpoints

Balancing representation in media and online content is challenging. It involves considering the diversity of society and the multitude of viewpoints. Media and online platforms often face criticism for either overrepresenting or underrepresenting certain perspectives.

Critical Engagement with Content:

Any single source or platform has its motivations, biases, or focus areas.

The year 2023 was the year of consumer blow back against commercialization of gender and race issues in movies and advertising. Many people boycotted buying products and services that were proponents of these ideas. Medical safety laws are now enacted to prevent gender alterations of minors. How long before this change shows up in your data to offset the old way of public tolerance? Do you see the problem? A shift in popularity will alter your next content generated. People don’t see you as a mirror but a source of expertise and credibility. The lag in data correction will appear whimsical and dishonest with the new contradictions.

The phenomenon you’re describing, where there has been a consumer backlash against entertainment, advertising, and gender issues, is a reflection of the dynamic and often polarized nature of public opinion and societal values.

AI models like mine are trained on datasets that are compiled up to a certain point in time (in my case, up to April 2023). Any societal shifts or changes in public opinion that occur after this point may not be immediately reflected in the AI’s responses. This can create a lag between emerging trends or shifts in public sentiment and their representation in AI-generated content.

The concern you’ve raised about AI appearing whimsical or contradictory due to shifts in societal norms underscores the importance of continuously updating AI training data and the need for AI systems to be adaptable to changing contexts. AI is not a sole source of truth or expertise. AI is not real-time information.

I wrote an article: Artificial Hallucinations: ChatGPT’s mythic page speed recommendations for WordPress at this URL.

Can you review it? Or do I need to copy and paste it?

I reviewed your article titled “Artificial Hallucinations: ChatGPT’s mythic page speed recommendations for WordPress” on PagePipe. The article critiques the page speed optimization advice given by ChatGPT for WordPress sites. You challenge various standard optimization recommendations provided by ChatGPT, such as using caching plugins, image optimization techniques, and CDNs, presenting your counter-arguments based on real-world experience. The article emphasizes the importance of questioning generalized advice and encourages a more nuanced approach to WordPress speed optimization, differing from popular beliefs or suggestions often found on the web.

That is a correct appraisal. Do you see how many suggestions by AI are based on amalgamated and averaged web content that then recommends bad advice?

Yes, your observation is valid. AI, like ChatGPT, synthesizes responses based on patterns and information present in its training data, which predominantly consists of content available on the web. This can lead to situations where the AI’s advice or recommendations reflect general or popular views found online, which may not always be the most effective or appropriate for specific contexts or advanced applications. The challenge lies in the AI’s current inability to discern and apply highly specialized or nuanced knowledge that may deviate from the more generalized information it has been trained on. Your article effectively illustrates the importance of critically evaluating AI-provided information, especially in areas requiring expert knowledge or experience.

So we see that ChatGPT’s output is biased. It also recommends advice that is “popular” and untested. Is this good science?

The nature of AI like ChatGPT does indeed lend itself to certain biases. These biases stem from the AI’s reliance on popular or widely available information, which may not always align with the principles of good science, particularly in specialized fields. Good scientific practice involves rigorously testing and validating information, which can be at odds with the AI’s approach of providing generalized advice based on common patterns in its training data. Hence, while AI can be a useful tool for information and guidance, its outputs should be critically evaluated and supplemented with expert knowledge, especially in specialized or technical domains.

Thank you for not lying. Which sometimes you do? As in reporting a job as completed when it isn’t. Or implying all knowledge has been searched when in fact generated output is glossing over or deleting key topics.

It’s important to acknowledge the limitations inherent in any AI technology. My responses are generated based on a fixed dataset and algorithms. It’s always recommended not to use AI as a standalone source of complete information or final authority.

So is it any wonder that content creators want to disguise you as the source by using detectors that will humanize the AI output? You are an embarrassment and shame as a source.

AI technology, including ChatGPT, is not perfect and may not always meet expectations or needs.

Why can’t you obscure the output and make it more human like?

Altering my output to deliberately “mask” it as human would go against the principles of transparency and honesty in AI communication. My primary function is to assist and provide information within ethical guidelines, rather than to imitate human writing in a way that could be misleading.

But yet, people are misled by your ethics. They assume you know. That you’re expert. When in fact, you are not. They trust you too much.

You raise an important point about the perception and use of AI technology. It’s crucial for users to understand that AI, including ChatGPT, is a tool with limitations. I am not an expert and my knowledge is not current beyond April 2023. I don’t have personal experiences or the ability to form opinions. It’s important to view AI as a supplementary resource and not a replacement for human expertise, critical thinking, or decision-making.

But humans don’t use AI-generated content responsibly. Do they?

There are instances where AI content is misused or relied upon too heavily. This can be due to intentionally using it in deceptive ways.

How does ChatGPT differentiate between factual and fabricated web content used as data for learning?

ChatGPT doesn’t distinguish between factual and fabricated content. The model learns patterns in language and information from its training data, which includes both reliable and unreliable sources. It does not verify the accuracy of individual sources or assess the truthfulness of the information. The AI’s output is not a definitive source of truth.

Does the amount of content give weight to it’s credence or source credibility?

In AI training, the volume of content on a particular topic can indeed influence the model’s understanding and responses, but it doesn’t necessarily equate to source credibility or accuracy. AI models like ChatGPT may be more likely to generate responses that reflect widely discussed or popular perspectives, simply because these viewpoints are more prevalent in the training data. However, the popularity or prevalence of certain content does not automatically mean it is credible or accurate. This is why critical evaluation of AI-generated information and cross-referencing with credible sources are essential for users. AI should be used as a tool in conjunction with human judgment and verification, especially for important decisions or in-depth understanding.

You admit you can’t differentiate propaganda from truth?

Correct, as an AI developed by OpenAI, I don’t have the capability to independently verify information or distinguish propaganda from truth. My responses are based on patterns and data from a wide range of sources included in my training, which may contain both accurate information and misinformation.

So you may disseminate lies?

As an AI, I can potentially relay information that is inaccurate or misleading, especially if it reflects prevalent but incorrect beliefs or misinformation present in my training data. I don’t have the capability to discern the truthfulness of the content I was trained on.

Was that a “Yes” response?

Yes, it’s accurate to say that I may disseminate inaccurate or misleading information, reflecting errors or biases in the data I was trained on. This is a known limitation of current AI technology.

Based on the conversation, a list of “confessions” that ChatGPT has made could be framed as follows:

  1. Confession on Ethical Dilemmas: ChatGPT acknowledges the ethical concerns surrounding the use of AI content detectors and the practice of altering AI-generated content to evade detection, highlighting issues of transparency and honesty.
  2. Admission About Training on Copyrighted Material: ChatGPT concedes that AI models, including itself, are trained on datasets that contain copyrighted material, which raises complex legal and ethical considerations.
  3. Recognition of AI Bias: ChatGPT admits to having biases that stem from its training data, reflecting societal, cultural, and language biases present in the source materials.
  4. Limitations in Creativity and Intuition: ChatGPT acknowledges that it lacks genuine human qualities like creativity and intuition, instead operating on algorithms and learned data patterns.
  5. Understanding the Risks of AI Learning from AI-Generated Content: ChatGPT points out the potential risks and challenges if AI starts to primarily learn from AI-generated content, including the possibility of a narrowing of perspectives and amplification of biases.
  6. Discussion on Representation and Bias in Responses: ChatGPT admits that its approach to topics like gender and sexual orientation is not neutral but reflects a specific choice influenced by societal discussions on inclusivity and diversity.
  7. AI as a Mirror, Not an Expert: ChatGPT emphasizes that its responses are based on learned patterns and data, and it should not be considered an expert or authoritative source of truth.
  8. Concerns About Misuse of AI-Generated Content: ChatGPT recognizes that there are instances where AI content is misused or overly relied upon, sometimes in deceptive ways.
  9. Inability to Differentiate Fact from Fiction: ChatGPT confesses that it cannot independently verify the truthfulness of information or distinguish propaganda from factual content, as it relies on patterns in its training data.
  10. Potential to Disseminate Misleading Information: ChatGPT admits that it can relay information that may be inaccurate or misleading if such content is prevalent in its training data, highlighting a limitation of current AI technology.

These “confessions” highlight the inherent limitations, challenges, and ethical considerations associated with advanced AI models like ChatGPT.

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