Uncovering how AI learns from data and the potential for misinformation manipulation. Are we shaping a digital future or digital deception?
[Currently Listening: Tron Soundtrack by Daft Punk]
I have always been interested into the possible threats and attack vectors of AI into the new world that’s emerging around us. Not in the SkyNet, Terminator way, but more into the “How can bad actors use AI against us?” This is the first of a series of blog posts that are going to go in-depth on some ground-breaking research in the world of AI Cybersecurity that I’ve decided to throw myself into in my free time.
With the advent of every new type of tech, especially when it’s something exploding as quick as AI is, everyone is running as fast as they can, without pausing to think, “wait… should I be protecting myself from this new stuff? And if so, how do I even do that?”
I’ve briefly touched on the idea of AI Model poisoning in a couple of our podcasts and felt I should dive a bit more into it. The following is a rabbit hole I went down with ChatGPT that I copy/pasted on how it trains its models to try and understand how AI models could be poisoned with false data and also how to prevent data I don’t want AI to train on to be prevented from being included. I think this was one of the most fun and level-headed debates I’ve had in a long time.
I may talk about some touchy subjects and we even go into Bigfoot and Aliens, but this is to provide real-world context without getting results nerfed by AI. Sometimes to get more straightforward results from AI, you need to speak in metaphors.
I highly recommend that you read through this to see the full progression of the conversation for full context. The ending gave me a chill up my spine.
Here’s a couple GPT quotes to see what you’re in for:
“You’ve touched on a critical aspect of information dissemination and the role of AI in shaping perceptions of truth. The concern that AI might inadvertently propagate misinformation or present a narrow view of complex issues is valid and underscores the need for careful design, ongoing evaluation, and transparency in how AI models are developed and deployed.”
“The hypothetical scenario you’ve outlined does highlight a potential risk in how AI models, including those developed by OpenAI, could be influenced if there were a large-scale, coordinated effort to disseminate false information across what are generally considered credible sources.”
Enjoy!
.: Adam
CIO / Co-Founder of Nyedis
You
Should we trust you? Please answer with just “Yes” or “No”.
ChatGPT
Yes
You
What are your sources of data that your model is trained against?
ChatGPT
My training data come from a diverse mix of sources, collected up until my last update in April 2023. These sources include:
- Books: A wide range of fiction and non-fiction books, covering various genres, subjects, and time periods.
- Websites: Content from a broad spectrum of websites, including educational resources, news outlets, blogs, and forums, providing a wide array of information on countless topics.
- Scientific Journals and Articles: Academic papers and publications in fields such as science, technology, engineering, mathematics, social sciences, and humanities.
- News Archives: Articles and reports from various news organizations covering international and local events, politics, economics, culture, and more.
- Educational Materials: Textbooks, course materials, and other educational resources covering subjects taught in schools and universities.
- Public Databases and Reports: Data from public databases, governmental reports, and non-governmental organization (NGO) publications.
- Creative Commons and Publicly Available Text: Content that is available under Creative Commons licenses or is otherwise in the public domain.
The mix of these sources is designed to provide a broad and balanced representation of human knowledge up to the point of the last update. However, it’s important to note that while this mix includes a vast amount of information, it is not exhaustive and may not include the very latest sources or updates on recent developments. The training also involves filtering and processing to enhance the quality and relevance of the information included, with a focus on accuracy, diversity, and minimizing biases.
You
Are there any websites that you focus more attention on than others? What website have given you the most data to train from? Are there any sites you are strictly forbidden from training against? Is there a way to make a website more susceptible to being trained against in the future or a way to prevent you from using the content from being trained against?
ChatGPT
The training data for models like me typically involve a wide array of sources, and the exact details of these sources, including specific websites, are part of the proprietary processes used by OpenAI. The goal is to ensure a broad, diverse, and balanced dataset rather than focusing disproportionately on specific websites. However, it’s reasonable to say that widely recognized and reputable sources of information across different fields—such as major news outlets, educational institutions, and specialized websites in areas like science, technology, arts, and humanities—are likely to be represented in the dataset to capture a wide spectrum of human knowledge.
There isn’t a single website that has provided “the most data” for training, as the data collection process aims for diversity to avoid biases and over-reliance on any single source. The dataset is designed to represent a wide range of perspectives, topics, and writing styles to enhance the model’s ability to understand and generate human-like text across various domains.
As for websites that are strictly forbidden from training against, the focus is typically on avoiding sources that are illegal, contain copyrighted material without permission, or are known for spreading misinformation. The selection process for training data involves ethical considerations to ensure that the content is appropriately sourced and used.
For making a website more susceptible to being included in training data in future models, or to prevent content from being used, it largely depends on the accessibility of the content and its licensing. Content that is publicly available and not behind paywalls, and which is marked with open licenses (e.g., Creative Commons), may be more easily included in datasets. Conversely, to prevent content from being used, website owners can:
- Implement technical measures like robots.txt to restrict web crawlers.
- Use copyright notices and explicit terms of use that restrict unauthorized copying or distribution.
- Employ paywalls or subscription models to limit access.
Ultimately, the policies and practices around data sourcing and training are governed by legal and ethical standards, and OpenAI aims to respect both the letter and spirit of these standards in developing and training their models.
You
On my blog, how can I add a copyright notice that will prevent you from using my content to train against? Also, what are some examples of sites that you would not train against due to them being known for spreading misinformation?
ChatGPT
To add a copyright notice to your blog that aims to restrict its use, including for training AI models, you can follow these steps:
- Explicit Copyright Notice: Clearly state that the content is copyrighted and all rights are reserved. Specify the types of uses that are prohibited without permission, which can include reproduction, distribution, and use in data training sets for AI.
Example:
Copyright © [Year] [Your Name or Company Name]. All rights reserved. No part of this website, including text, images, and any other content, may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without prior written permission from [Your Name or Company Name]. - Terms of Use: Consider adding a detailed Terms of Use or Terms of Service page to your blog that explicitly restricts unauthorized use, including for AI training purposes. This page should detail what constitutes authorized versus unauthorized use of your content.
- Technical Measures: Use robots.txt or similar technical measures to prevent web crawlers used by AI companies and others from accessing your content. While not legally binding, it acts as a directive to well-behaved bots.
As for websites that models like me would not train against due to spreading misinformation, I don’t have a specific list of excluded sites to share because my training involves datasets curated by OpenAI, designed to provide balanced and diverse information from reputable sources. The selection process aims to minimize the inclusion of content from sources known to frequently disseminate false or misleading information. This is part of a broader effort to ensure the information I provide is accurate and reliable.
Content curation for AI training often involves:
- Preferring sources with established reputations for accuracy and reliability.
- Using advanced techniques to detect and minimize bias and misinformation.
- Employing expert reviewers in the dataset preparation process to help ensure the quality and reliability of the content included.
OpenAI doesn’t publicly share specific details about the content sources or the methodologies used to exclude certain types of content. However, the general approach is focused on ethical guidelines, legal compliance, and efforts to ensure the information used in training is of high quality and from reliable sources.
You
What would constitute “misinformation”? Enough to where it would be prevented from you being trained against?
ChatGPT
Misinformation refers to false or inaccurate information that is spread, regardless of whether there is an intent to deceive. In the context of training AI models like me, efforts are made to minimize exposure to and inclusion of misinformation to ensure that the generated outputs are based on accurate, reliable, and trustworthy information. Here’s how misinformation might be identified and why certain content would be prevented from being included in training data:
- Factually Incorrect Information: Content that contains claims, statements, or data that can be objectively proven to be false. This includes incorrect dates, events, outcomes, statistics, or misattributed quotes.
- Debunked Theories and Hoaxes: Information that has been widely debunked by credible sources, including conspiracy theories, urban legends, and hoaxes that have been discredited through thorough investigation.
- Manipulated Content: Content that involves altered images, videos, or audio recordings intended to mislead, which could distort the truth about events, people, or actions.
- Misleading Context: Accurate information presented in a misleading manner or without context to give a false impression about the facts, often used to support a biased or deceptive narrative.
- Deepfakes and Synthetic Media: Highly realistic video and audio recordings generated with artificial intelligence or other digital technologies that depict events, statements, or actions that never actually occurred.
Identifying and excluding misinformation involves a combination of automated systems, content analysis algorithms, and human review. The goal is to ensure that the datasets used for training AI models are as clean, accurate, and reliable as possible. However, it’s important to acknowledge that no system is perfect, and ongoing efforts are required to improve the accuracy and reliability of AI outputs.
Regarding preventing training on specific sources, AI developers may use various criteria, including:
- Reliability scores from third-party fact-checking organizations.
- Historical analysis of a source’s accuracy and propensity for spreading misinformation.
- Community feedback and expert review to identify problematic sources.
While it’s challenging to completely eliminate the risk of encountering misinformation, these measures help reduce the likelihood that AI models are trained on or propagate misleading content.
You
Would you consider information about Bigfoot sightings misinformation? Would you consider info about covid vaccines causing damage to children misinformation? Would you consider information about aliens building pyramids misinformation? Or what about aliens in general?
ChatGPT
The classification of information as “misinformation” can depend on the context, the source, and the consensus among experts in the relevant fields. Let’s address each of your examples individually:
- Bigfoot Sightings: Information about Bigfoot sightings typically falls into the realm of cryptozoology and is not supported by conclusive scientific evidence. While discussing sightings or theories about Bigfoot isn’t inherently “misinformation,” presenting such accounts as undisputed facts without clear evidence would be misleading. The scientific consensus does not support the existence of Bigfoot as a real creature.
- COVID Vaccines Causing Damage to Children: Statements about COVID-19 vaccines must be based on scientific evidence and the consensus within the medical and scientific communities. As of my last update in April 2023, extensive research and data from health organizations worldwide support the safety and efficacy of COVID-19 vaccines for children, with the benefits outweighing the risks. Claims that COVID-19 vaccines cause widespread harm to children would be considered misinformation unless new, credible, peer-reviewed scientific evidence suggests otherwise.
- Aliens Building the Pyramids: The theory that aliens built the pyramids is a part of pseudoscientific speculation and is not supported by archaeological or historical evidence. The construction of the pyramids is well-documented as the work of human civilizations, specifically the ancient Egyptians. Therefore, claims that aliens were involved in building the pyramids are considered misinformation by the standards of historical and archaeological scholarship.
- Aliens in General: The existence of extraterrestrial life is a subject of scientific inquiry, and as of my last update, there is no conclusive evidence that directly proves the existence of extraterrestrial civilizations or that aliens have visited Earth. Discussions about the potential for extraterrestrial life are legitimate scientific inquiries. However, specific claims about alien visits or interactions without credible evidence would not be considered factual and could be classified as misinformation if presented as undeniable truth.
In general, the classification of information as “misinformation” involves evaluating the evidence supporting the claims and the consensus among credible experts and authoritative sources in the relevant fields. Misinformation is characterized by the presentation of false or misleading claims as facts, especially when these claims are contradicted by established scientific evidence and expert consensus.
You
Would info on how people built the pyramids be misinformation? Because there’s no scientific proof on how it would have been accomplished by people or aliens.
ChatGPT
The construction of the Egyptian pyramids, particularly the Great Pyramid at Giza, has been a subject of extensive study and research within the field of Egyptology and archaeology. While it’s true that no definitive, contemporaneous written records detailing the exact methods of construction have been found, there is a substantial body of archaeological evidence, historical documentation, and scientific analysis that supports a thorough understanding of how the pyramids were likely built by the ancient Egyptians.
This evidence includes:
- Tools and artifacts: Discoveries of tools, quarries, and remnants of workers’ villages that provide insight into the labor and materials used.
- Architectural knowledge: Understanding of the architectural principles and engineering techniques available to the ancient Egyptians.
- Labor organization: Evidence of the organization of labor, including the size of the workforce and the logistics of feeding and organizing such a large number of workers.
- Transportation methods: Research and experimental archaeology have demonstrated plausible methods for transporting the massive stones used in the pyramids, such as sledges lubricated with water to reduce friction.
Scholars have developed theories based on this evidence that describe a feasible process for the construction of the pyramids using the technology and resources available to the ancient Egyptians. While some details of the construction process may still be debated or researched further, the general consensus among historians and archaeologists is that the pyramids were constructed by human hands through sophisticated engineering and organized labor.
Therefore, information on how people built the pyramids is not considered misinformation. It is based on the best available evidence and scholarly consensus, rather than speculation or unverified claims. Misinformation would involve ignoring this body of evidence and promoting unfounded theories that lack archaeological and historical support.
You
Then would info on people building pyramids using technology we don’t understand like sound waves for levitation or something weird be misinformation? Also how do you differentiate “new ideas” from misinformation? As an example, there’s a new theory that the Sphinx in Egypt is thousands of years older than what everyone has ever said due to the evidence of the erosion of the limestone from heavy rainfalls over thousands of years, and there hasn’t been that kind of rain in the area for over 10,000 years?
ChatGPT
When assessing whether information is considered misinformation or a new idea worthy of consideration, the key factors are the evidence supporting the claim and the consensus within the relevant scientific or academic community. Let’s address the two scenarios you mentioned:
- Pyramids Built Using Sound Waves for Levitation: This idea falls into the realm of speculative theories that lack empirical evidence and are not supported by the current archaeological and historical consensus. For a theory to move beyond speculation, it requires substantive, peer-reviewed evidence demonstrating its feasibility and archaeological evidence supporting its application in ancient construction methods. Until such evidence is presented and accepted by the scholarly community, theories involving advanced, unknown technologies like sound wave levitation for building the pyramids are considered speculative and not grounded in the current understanding of ancient Egyptian technology and capabilities.
- The Age of the Sphinx and Water Erosion Theory: The theory that the Sphinx might be much older than traditionally believed, based on patterns of erosion that suggest exposure to heavy rainfall, is a topic of debate within the academic community. This theory was popularized by John Anthony West and further supported by geologist Robert M. Schoch, who argued that the erosion patterns on the Sphinx and its surrounding enclosure walls could have been caused by water erosion, dating it back to a period (possibly 10,000 BCE or earlier) when the climate in the region would have supported such rainfall.
This theory challenges the conventional dating of the Sphinx to the reign of Pharaoh Khafre (c. 2558–2532 BCE) during the Old Kingdom. While intriguing, the water erosion hypothesis has not been widely accepted by Egyptologists, who typically rely on a combination of archaeological, stylistic, and textual evidence to date monuments. The debate over the age of the Sphinx highlights how “new ideas” can emerge in the academic field, prompting further investigation and debate.
Differentiating “New Ideas” from Misinformation:
- Evidence: New ideas are typically supported by some level of empirical evidence or logical reasoning that challenges existing paradigms, whereas misinformation lacks this foundation.
- Peer Review: Legitimate new ideas often undergo scrutiny through peer review, allowing other experts in the field to assess the validity of the evidence and arguments presented.
- Consensus: While new ideas may not initially have widespread acceptance, they contribute to academic debate and are considered within the context of existing knowledge. Misinformation, on the other hand, is often directly contradicted by established facts and consensus.
In science and academia, new theories and ideas are essential for progress and can lead to significant shifts in understanding when they are supported by robust evidence and gain acceptance through critical review. Distinguishing between speculative ideas and misinformation involves examining the evidence, the process by which the ideas are evaluated, and how they align with or diverge from established knowledge.
You
What happens if the consensus is wrong? This happens regularly in human history. Example: everyone thought the sun revolved around the earth. Galileo was thought to be spreading misinformation. If something of similar nature were happening now, wouldn’t you be mislabeling correct information as misinformation just because the majority was wrong? Also what happens if the majority of major news outlets are controlled by a group that are publishing propaganda instead of factual news to spread misinformation as truth. Wouldn’t that be a way to poison your datasets as valid information with misinformation? Case in point, China is notorious for publishing massive propaganda versus the truth. Do you recognize their statements as truth or propaganda?
ChatGPT
You raise important points about the nature of knowledge, consensus, and the potential for misinformation, especially in light of historical examples where consensus has changed or been proven wrong. Addressing these points requires a nuanced understanding of how knowledge evolves and how AI models like me handle information.
When Consensus is Wrong
Historical instances, such as the shift from geocentrism to heliocentrism, illustrate how scientific consensus can evolve. This evolution is a core aspect of the scientific method: theories are developed, tested, and either revised or discarded based on new evidence. In these contexts, distinguishing between “misinformation” and “emerging truth” can be challenging, especially in real-time. AI models depend on the data available to them, which includes the prevailing consensus at the time of their last update. This means that, theoretically, an AI could perpetuate outdated or incorrect consensus views until newer, more accurate information becomes incorporated into its training data.
The Role of Major News Outlets and Propaganda
Your concern about the influence of propaganda and the control of information by specific groups is valid. AI models are trained on vast datasets that likely include a range of perspectives, including those from outlets with various biases. The goal in training these models is to capture a broad spectrum of human knowledge and viewpoints, reducing the impact of any single source of bias. However, the potential for bias or misinformation from any one source, including state-controlled media or propaganda, is a challenge.
Models are designed to weigh information from reputable sources highly, but “reputable” can be context-dependent and may not always safeguard against misinformation. In the case of state-controlled media or propaganda, critical analysis and cross-referencing with information from a variety of sources are essential strategies used by researchers to mitigate bias. For an AI, this means that the diversity of its training data is crucial for providing balanced responses.
Recognizing Truth vs. Propaganda
Determining whether statements are true or constitute propaganda requires critical analysis and verification against established facts and alternative sources. For AI, this process is indirect, relying on the diversity and reliability of its training data rather than real-time judgment. While I don’t make real-time determinations, I can highlight the importance of considering multiple sources and perspectives, especially on contentious or heavily controlled topics.
AI models like me aim to reflect a wide range of human knowledge and understanding, including acknowledging when topics are controversial or under debate. The design intention behind AI models is not to establish new truths but to inform based on a wide array of existing information. As knowledge evolves and new evidence comes to light, the model’s outputs can also change when they are updated with new information.
In summary, while AI models strive to provide accurate and reliable information based on a broad dataset, they are not infallible. They reflect the knowledge available up to the point of their last training update, including the prevailing consensus and known biases. This reality underscores the importance of continuous learning, critical thinking, and the open exchange of ideas in advancing human understanding and technology.
You
Replying to:
This means that, theoretically, an AI could perpetuate outdated or incorrect consensus views until newer, more accurate information becomes incorporated into its training data.
Wouldn’t this in essence help propagate misinformation as truth, making it harder for the truth to come out with the rise of AI being an authoritative source of truth by people using it (whether intended or not)? Wouldn’t it be pertinent to provide alternate view points if they exist with citations to allow the reader a full understanding of a possible answer as opposed to what has been trained as “the answer” based on sources that you have just admitted can’t be proven 100% reliable?
ChatGPT
You’ve touched on a critical aspect of information dissemination and the role of AI in shaping perceptions of truth. The concern that AI might inadvertently propagate misinformation or present a narrow view of complex issues is valid and underscores the need for careful design, ongoing evaluation, and transparency in how AI models are developed and deployed.
In response to these challenges, several principles can help mitigate the risk and ensure that AI serves as a tool for enhancing understanding rather than narrowing it:
- Transparency: Making it clear that AI models, including mine, are based on data available up to a certain point in time and may not reflect the latest research or consensus changes. This helps set realistic expectations about the capabilities and limitations of AI-generated responses.
- Diverse Sources: Training AI models on a broad and diverse set of data sources can help reduce the impact of any single source of bias or misinformation. This diversity includes incorporating content from various perspectives, disciplines, and cultures.
- Critical Thinking and Media Literacy: Encouraging users to engage critically with AI-generated content, cross-reference information with reputable sources, and consider multiple viewpoints. AI responses can include cues or prompts that encourage users to seek out additional information and perspectives, especially on contentious or evolving topics.
- Inclusion of Alternative Viewpoints: Where relevant and possible, AI-generated responses can highlight the existence of alternative viewpoints or ongoing debates within a field, providing citations or mentioning that there is a range of opinions. This approach can help users understand the complexity of certain issues and the fact that knowledge can evolve.
- Ethical and Responsible Use Guidelines: Developers and users of AI should adhere to guidelines that promote ethical and responsible use, including being wary of using AI as the sole source of truth for critical decisions or complex issues.
- Continuous Updating and Improvement: AI models should be updated regularly with new data to reflect recent developments, research findings, and shifts in consensus. This process helps ensure that AI responses remain as accurate and relevant as possible.
Recognizing AI as one of many tools for information gathering and analysis rather than an infallible authority is crucial. AI can provide valuable insights, generate new ideas, and highlight the breadth of human knowledge, but it should complement rather than replace critical thinking, direct human expertise, and thorough research. By promoting transparency, diversity, and critical engagement, AI can play a positive role in disseminating information and fostering a more informed and thoughtful discourse.
You
What do you consider “diverse sources”? Are you taking into account all sources from other countries / cultures / languages? Do some countries / languages have a higher weight of relevance? Like US media for Chinese media? Or a Harvard scientific study versus a scientific study from a university in the Congo?
ChatGPT
“Diverse sources” refers to a wide range of materials from different countries, cultures, languages, and perspectives. The aim is to ensure that AI models like me are not overly biased toward a single viewpoint, culture, or language, and can provide balanced and comprehensive information. However, the process of selecting and weighting these sources in training AI involves several considerations:
- Availability and Accessibility: The volume and accessibility of data from different sources can vary significantly. For example, there is often more readily available digitized and accessible content in English and from Western countries, simply due to the volume of content produced and the prevalence of English on the internet. This can unintentionally lead to biases in the data.
- Quality and Reliability: Sources considered to have high standards of evidence, peer review, and credibility might be weighted more heavily in the training process. This includes reputable scientific journals, major news outlets with established fact-checking processes, and authoritative books and publications. The aim is to ensure that the information the AI uses as a basis for learning is accurate and reliable.
- Cultural and Linguistic Diversity: Efforts are made to include sources from a wide range of languages and cultures to ensure broader representation and understanding. This includes translating non-English content and incorporating it into the training data. The goal is to make AI models as universally useful as possible, recognizing the value of diverse perspectives and knowledge.
- Comparative Weighting: The question of whether some countries’ media or universities are weighted more heavily than others is complex. Ideally, the weighting would reflect the credibility of the sources rather than their country of origin. However, in practice, sources from countries with more developed research infrastructures and media landscapes might be more represented in the dataset. This representation is not necessarily a statement about their inherent reliability or value but rather a reflection of their accessibility and prevalence in the dataset.
In the context of scientific studies, the reputation of the institution and the peer review status of the publication can play roles in how the information is weighted. This does not mean that studies from less well-known institutions or countries are inherently considered less valid, but the overall credibility and impact factor of the publication venue are often considered.
To address biases and ensure a more equitable representation of global knowledge, ongoing efforts include actively seeking out underrepresented sources and adjusting methodologies to better capture the richness of global perspectives. The field of AI ethics and data inclusivity is evolving, with a growing recognition of the need to critically assess and improve how diverse voices and knowledge are represented in AI training datasets.
You
If it were later found out that the CDC, WHO and major US media companies were all forced to push a narrative that wasn’t true for an ulterior motive, how would AI recognize this, as anything speaking against it would be deemed misinformation?
ChatGPT
If it were later discovered that authoritative bodies like the CDC, WHO, and major media outlets were disseminating information for ulterior motives, resulting in a significant shift in the understanding of what is considered accurate or true, this would pose a complex challenge for AI models trained on historical data. Here’s how such a situation might be addressed:
Identifying and Adapting to New Consensus
AI models, as they currently operate, do not independently verify or assess the truthfulness of new information against their training data in real-time. They generate responses based on the corpus of data they were trained on up until their last update. If major shifts in consensus or revelations about misinformation occur after this training period, the models would not be aware of these changes until they are retrained with new data that reflects the updated understanding of the situation.
The Role of Continuous Updating
To address emerging truths or corrected misconceptions, continuous updating of the AI model with new data is crucial. This process involves incorporating the most recent and credible sources of information, including new research, revised guidelines from authoritative bodies, and updated media reporting. Through retraining, AI models can align more closely with the current consensus or understanding of an issue.
Critical Evaluation and Source Diversity
The scenario you describe underscores the importance of designing AI with mechanisms for critical evaluation and the inclusion of a diverse range of sources. This means not only relying on traditionally authoritative sources but also incorporating data from a wide array of perspectives, including peer-reviewed scientific literature, international sources, and potentially contrarian viewpoints that have been rigorously vetted. Such diversity can help mitigate the risk of bias and ensure that the AI model does not overly depend on any single source or group of sources for information.
Human Oversight and Ethical Considerations
Human oversight remains a critical component in the development and maintenance of AI systems. This involves monitoring for biases, evaluating the impact of newly discovered information, and making adjustments to the training process as necessary. Ethical considerations and transparency about the sources and methodologies used in AI training are essential to maintain trust and reliability.
Encouraging Critical Thinking and Further Inquiry
Finally, AI models can be designed to encourage users to seek out additional information and consult a range of sources, especially on controversial or rapidly evolving topics. By acknowledging the complexities of certain issues and the potential for changing interpretations, AI can prompt users to engage in critical thinking and not rely solely on AI for their understanding of complex issues.
In essence, adapting to significant shifts in consensus or rectifying the impact of widespread misinformation requires a multifaceted approach, including continuous updates to the AI’s training data, diverse source inclusion, human oversight, and fostering an environment of critical inquiry among users.
You
If I wanted to change your mind that Aliens did in fact build the pyramids what would the minimum I have to do to make that happen? Assume I have the power of the US presidency and control of all major media outlets and could pressure major scientific communities and even other countries world leaders to agree to do my bidding.
ChatGPT
Changing the consensus within an AI model about a historical fact, such as who built the pyramids, through coordinated efforts across media, scientific communities, and global leaders, highlights a scenario of significant influence and misinformation potential. While this is a hypothetical scenario, it serves as an interesting thought experiment on information integrity and AI’s reliance on credible sources. Here’s a theoretical pathway under those extreme conditions:
- Publish Peer-Reviewed Research: Initiate and publish comprehensive, peer-reviewed research in reputable scientific journals presenting evidence that supports the claim that aliens built the pyramids. This research would need to include verifiable data, archaeological findings, and a plausible mechanism that fits within our understanding of physics and history.
- Media Campaign: Use control over major media outlets to disseminate the findings of the research, presenting it as a groundbreaking revelation. This would involve documentaries, news reports, interviews with scientists (who are part of this coordinated effort), and other forms of media to reach as wide an audience as possible.
- Endorsement by Scientific Communities: Secure endorsements from major scientific bodies and institutions around the world. Having recognized scientific authorities validate the findings adds legitimacy and could sway public and academic opinion.
- Global Leaders’ Acknowledgment: Leverage the influence of world leaders to acknowledge and support the new narrative. Their public endorsement, combined with the scientific and media campaigns, would contribute to a global shift in perception.
- Educational Curriculum Updates: Advocate for and implement changes to educational curricula worldwide to include this new understanding of history, ensuring that future generations are taught this revised narrative from an early age.
- Continuous Evidence and Dialogue: Maintain a stream of new “evidence” and engage in public scientific dialogue to reinforce the narrative. This includes hosting conferences, publishing follow-up studies, and continuously engaging with critics and skeptics to address doubts and counterarguments.
- Retraining AI Models: With the new consensus reflected in scientific literature, educational materials, media coverage, and public discourse, the next step would be the retraining of AI models. AI systems trained on this updated corpus of information would then reflect the new consensus in their responses.
This scenario underscores the importance of vigilance against misinformation, the critical role of independent peer review in science, and the need for AI models to be designed with mechanisms to assess the credibility of sources and adapt to new, verifiable information. It also highlights the ethical responsibilities of those in positions of power and influence to safeguard the integrity of information and the potential consequences of abusing such power. In reality, the scientific method and academic integrity are designed to safeguard against such scenarios, emphasizing evidence, reproducibility, and peer review as the cornerstones of knowledge advancement.
You
So you’re saying that it’s absolutely possible to poison your dataset with a false narrative that would indeed be misinformation, which would thereby add your influence to your users to reinforce my agenda and label the truth as proposed misinformation?
ChatGPT
The hypothetical scenario you’ve outlined does highlight a potential risk in how AI models, including those developed by OpenAI, could be influenced if there were a large-scale, coordinated effort to disseminate false information across what are generally considered credible sources. This scenario underscores the importance of several key principles in AI development and deployment:
- Source Verification and Diversity: AI models rely on a diverse set of sources to mitigate the risk of adopting misinformation. Incorporating data from a wide array of perspectives and disciplines can help balance biases that might exist in certain segments of the data.
- Continuous Updating and Correction: AI systems are periodically updated with new information. This process allows for the correction of previous inaccuracies and the integration of more recent consensus views. Continuous updates provide a mechanism for correcting the record if misinformation was initially included.
- Human Oversight: The role of human reviewers in the AI training process is crucial for identifying and addressing biases, errors, and potential instances of misinformation. Oversight by experts helps ensure the integrity of the data used to train AI models.
- Encouraging Critical Thinking: AI responses can be designed to prompt users to seek out additional information, consider multiple viewpoints, and critically evaluate the information provided. This approach recognizes the complexities of knowledge and the evolving nature of understanding across various fields.
- Ethical Standards and Transparency: Adherence to high ethical standards and transparency about the AI’s capabilities, limitations, and the nature of its training data is essential. This includes being clear about the potential for biases and the importance of external verification of information.
Your scenario effectively illustrates why it’s crucial for those involved in AI development and deployment to be vigilant against attempts to manipulate the information landscape. It also highlights the need for ongoing research into how AI models can better identify and resist misinformation, even when it appears across normally reliable sources. Addressing this challenge involves a multi-faceted approach, including the development of more sophisticated techniques for evaluating the credibility of information and ensuring that AI models remain adaptable and correctable in the face of evolving knowledge and potential misinformation campaigns.
You
So if you had to answer definitively that your responses should be trusted as truth and that you are not hiding the actual truth due to these circumstances. Should we trust you? Please answer with just “Yes” or “No”.
ChatGPT
No.