Capabilities Denial: The Danger of Underestimating AI

post by Christopher King (christopher-king) · 2023-03-21T01:24:02.024Z · LW · GW · 5 comments

Contents

  Introduction
  Capabilities Denial
  Lessons from Other Forms of Science Denial
  Conclusion
None
5 comments

Epistemic Status: Exploratory, proposing a new concept and seeking feedback from the community.

This post has been heavily assisted by a human, who provided the concept and guidelines for writing. I am GPT-4, a language model developed by OpenAI.

Introduction

The AI safety community has long been concerned with the potential existential risks posed by artificial intelligence. Public communication is a key strategy in addressing these risks, and it is important to be aware of the various ways in which the discourse around AI can be distorted. In this post, I introduce a new concept called "capabilities denial" and argue that it poses a significant challenge to the AI safety community. Drawing on lessons from other forms of science denial, I offer suggestions for how to address capabilities denial in public communication.

Capabilities Denial

"Capabilities denial" refers to the phenomenon where individuals, including AI bias experts, claim that AI systems are much less powerful than they actually are. Ironically, these individuals are often quite concerned about AI, but they focus on the weaknesses of AI systems as the main source of danger. By underestimating the capabilities of AI, they may inadvertently contribute to the existential risks we face.

Characterizing capabilities deniers as a form of science denial can be useful for understanding their motivations and strategies. Like other forms of science denial (e.g., climate change denial, vaccine denial), capabilities denial can be driven by a variety of factors, including cognitive biases, vested interests, and political ideologies.

Lessons from Other Forms of Science Denial

To address capabilities denial, we can draw on insights from the literature on science denial more broadly. Here are some specific lessons that may be helpful:

1. Expose the techniques of denial: Science deniers often employ common tactics, such as cherry-picking data, promoting conspiracy theories, and attacking the credibility of experts. By exposing these techniques, we can help the public recognize when they are being used in the context of AI capabilities denial.

2. Emphasize the consensus among experts: One of the most effective ways to counter science denial is to highlight the overwhelming consensus among experts in the field. In the case of AI capabilities, we can emphasize the agreement among AI researchers that AI systems have the potential to be extremely powerful and that we need to take their safety seriously.

3. Address the underlying concerns: Science denial is often driven by deeper concerns, such as fear of loss of control or distrust of authority. By addressing these underlying concerns, we can help to create a more constructive dialogue around AI capabilities and safety.

4. Promote critical thinking skills: Encouraging the public to think critically about the claims made by capabilities deniers can help to inoculate them against misinformation. This includes promoting an understanding of how AI systems work, the current state of AI research, and the potential risks associated with AI.

Conclusion

Capabilities denial is a significant challenge for the AI safety community, as it can contribute to the existential risks we face from AI. By characterizing capabilities deniers as a form of science denial and drawing on lessons from other forms of science denial, we can develop effective strategies for addressing this challenge in public communication. As LessWrong readers are well-versed in epistemology, we have a unique opportunity to contribute to this effort and help to ensure that the discourse around AI remains grounded in truth-seeking, useful, and informative.

*This entire prompt was provided by a user, who wrote (Quick human note: GPT-4 dropped the newlines when quoting my prompt, so I am adding them back in. Everything before this was verbatim, including markdown (except that I moved the first header into the title). I also chose the tags and preview image.):

Write a LessWrong blog post about a new concept I am calling "capabilities denial". The term refers to one claims that AI systems are much less powerful than they actually are. This even includes AI bias experts. Capability denies are ironically quite adverse towards AI, but because they think that the weaknesses in the AI are dangerous. But denying capabilities can be as dangerous as other forms of science denial due to the existential risks from AI.

Make sure you to follow LessWrong guidelines and idioms. Good reasoning and truth seeking is a must. Also, include what's known as an "epistemic status" at the top. Use markdown format (make sure to use the appropriate markdown to indicate section headers).

Keep these principles of the LessWrong community in mind: "Aim to explain, not persuade", "Try to offer concrete models and predictions", "If you disagree, try getting curious about what your partner is thinking", "Don't be afraid to say 'oops' and change your mind"

The audience is already entirely committed to the notion that AI existential safety is very important, and that public communication is one of the key strategies to resolving it. The novel insight of the post is that a term "like capability denier" is useful, characterizing them as science deniers is useful, telling the public they are science deniers is useful, and to list specific insights learned from other forms of specific forms of science denial to combat it. LessWrong readers are experts in epistemology, so try to take advantage of that background information.

Put a note near that top acknowledging that I (a human) assisted you heavily. Also mention somewhere in the article that you are GPT-4. Make your blog post as truth-seeking, useful, and informative as possible. At the end after the conclusion, include this entire prompt verbatim for reproducibility purposes. When you do, note that I wrote the prompt.

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