# AI Risk and Survivorship Bias - How Andreessen and LeCun got it wrong 

*Purpose of this post/TL;DR: Many people who argue against the existence of large-scale risks associated with AI, including Marc Andreessen or Yann LeCun, use the following argument template (call this the Argument from Overestimation, AFO): "since people were wrong in estimating high risk for \*insert safe technology that we use today\*, AI is probably safe too". I was surprised that this argument is being used by some of the leading figures in the AI Safety debate because as I argue in this paper, its logic relies on the survivorship bias. This, in turn, means that the argument begs the question by grouping AI with a skewed sample of only safe technologies, making it a flawed argument. I conclude that for the sake of improving the current debate on AI Safety, this argument should be abandoned. *

*Epistemic status: While I tend to be more on the “x-risks side” in AI Safety debates (to make this clearer, I was more in agreement with Tegmark and Bengio in the debate I reference), I attempted to give the most charitable reconstructions and strongest responses for AFO and gave concrete examples of the argument in action to avoid the straw-man fallacy. Note also that just because a particular argument in favour of AI safety fails, we are not warranted to conclude that AI is unsafe. The main point of this post is simply that we should not assume safety/unsafety without providing further arguments. I invite feedback and potential improvements on my position in the comments! *

**Argument from Overestimation**
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Consider the following passage from [Marc Andreessen’s essay about concerns regarding AI risk](https://a16z.com/2023/06/06/ai-will-save-the-world/), titled ‘Why AI will Save the World’:

“The fear that technology of our own creation will rise up and destroy us is deeply coded into our culture. \[Recounts the myth of Prometheus about the technology of fire as an example.\] The presumed evolutionary purpose of this mythology is to motivate us to seriously consider potential risks of new technologies – fire, after all, can indeed be used to burn down entire cities. But just as fire was also the foundation of modern civilization as used to keep us warm and safe in a cold and hostile world, this mythology ignores the far greater upside of most – all? – new technologies, and in practice inflames destructive emotion rather than reasoned analysis. Just because premodern man freaked out like this doesn’t mean we have to; we can apply rationality instead.” 

A similar sentiment was also expressed by [Yann LeCun in the recent Munk Debate on AI](https://munkdebates.com/debates/artificial-intelligence), listing historical instances where warnings about a new technology’s negative effects were wide of mark: 

“Socrates was against writing, he thought people are going to lose their memory. The Catholic church was against the printing press, saying they would lose control of the Dogma. \[...\] The Ottoman Empire banned the printing press and according to some historian \[sic\] this is what accelerated their decline.” 

Later on, LeCun spells out his argument when reacting to Yoshua Bengio’s statement that AI is an unprecedented technology in its unique capabilities to design and produce its own improved copies: 

“That very argument was made for computers, you know, 50 years ago. This is not a new issue. \[References a website called Pessimist Archives which keeps a record of newspaper clips with wrong and sometimes absurd predictions regarding the effects of new technologies\]. \[Take the example of\] the train: you’re not gonna take the train if it’s going 50 km/h, you can't breathe at that speed. \[...\] Everybody has said this kind of thing about every time there was a technological evolution or cultural evolution.” 

It seems that the implicit argument in these passages is meant to be something like the following. Call it the Argument from Overestimation (AFO): 

**P1** People usually overestimate the risks associated with new technologies.

**P2** AI is a new technology.

**C1** People probably overestimate the risks associated with AI.

The argument may seem inductively strong at first, but take a look at what all the technologies listed above have in common: they all turned out to be safe. This creates problems for LeCun's argument, because it highlights that the logic behind his argument rests on fallacious reasoning, specifically, it falls prey to the survivorship bias. 

**Objection from Survivorship Bias**
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Survivorship bias is a cognitive shortcut that occurs when a more easily noticeable subgroup which passes a hidden selection process is mistaken as the entire group (read more [here](https://en.wikipedia.org/wiki/Survivorship_bias) and [here](https://thedecisionlab.com/biases/survivorship-bias)). A hypothetical example can be taken from a medical experiment attempting to estimate the effectiveness of a treatment intervention. Even an intervention which exhibits relatively high success rates (e.g. high survival rates) may actually be relatively ineffective, as the analysis of its effects often excludes people who did not survive long enough to get the treatment -- either because their immune systems were comparatively weaker, they had worse genetic predispositions for the disease etc. Hence, without including the weaker group in the analysis, the survival rate of the drug is most likely positively skewed and too high, as its selection mechanism operates only on people who are naturally more resistant to the disease. 

Andreessen’s and LeCun's arguments are based on similarly skewed reasoning. Think again of the technologies mentioned: fire, writing, printing, trains, computers. All of these technologies went through the selection process of "risk warnings about us were overestimated because we are safe technologies”. However, there is a whole group that did not pass this test: unsafe technologies where the high risk assessments turned out to be accurate. If someone said "look, we really shouldn't use airships for mass transport because they are dangerous", they estimated the risk correctly, meaning airships are automatically excluded from being used in AFO. Similar examples of unsafe technologies include certain [food additives which turned out to be carcinogenic](https://www.nrdc.org/bio/nrdc/fda-bans-seven-food-additives-cause-cancer) or specific [pesticides that severely damage the human body](https://earthjustice.org/brief/2021/chlorpyrifos-ban-pesticide-industry-pressure-epa)and [environment](https://www.epa.gov/ingredients-used-pesticide-products/ddt-brief-history-and-status). Hence, AFO suffers from survivorship bias because the "risk overestimation" selection process picks out only technologies which actually turned out to be safe.

Therefore, due to this bias, AFO actually begs the question: it aims to prove AI Safety by comparing AI to a sample of exclusively safe technologies. This means that in reality, AFO actually works more like this:

**P1* **People usually overestimate risks associated with technologies that turned out to be safe. 

**P2** AI is a new technology.

**C1** People probably overestimate the risks associated with AI.

This formulation makes clear why the argument doesn't go through: in order for the argument to be sound, we would have to add the premise that AI turned out to be safe. However, that would beg the question, since the whole point of introducing the parallel was to prove AI Safety. Hence, the argument cannot go through without begging the question, making it a bad argument. Call this critique the Objection from Survivorship Bias. 

**Possible Responses to the Objection from Survivorship Bias **
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It seems challenging to find responses against this objection. Perhaps one could argue that P1* historically holds true for most technologies, therefore, it is likely true for AI as well. However, this seems very improbable. This is equivalent to either arguing that (i) most technologies we invent are safe from the get-go or (ii) that inventors (or social institutions or society as a whole) are very accurate at estimating risks. (i) seems improbable because if it were true, it would make it very hard to explain why regulatory processes were ever established. (ii) seems a bit more likely, but at the very least, [individuals are known to be very bad at assessing risk](https://www.science.org/doi/10.1126/science.185.4157.1124) and [larger groups and even political institutions may aggregate the same biases](https://psycnet.apa.org/record/2016-48867-038) at least partially. 

But perhaps a better response is that we are allowed to assume that AI will be safe as a by-product of current regulatory practice. Hence, we are protected against the discrete risks from AI, say misinformation, because there are regulations over misinformation, protected against discrimination thanks to discrimination guidelines and so on. I do think this defense works at least partially, since some negative effects of AI may in fact be buffered by existing regulation. For instance, creditors have the Right to Explanation regarding denied credit applications, regardless of whether [the decision was made by a human or an algorithm](https://www.consumerfinance.gov/about-us/newsroom/cfpb-acts-to-protect-the-public-from-black-box-credit-models-using-complex-algorithms/). However, it still seems that this defense rests on some questionable assumptions. 

Firstly, why should we assume that non-targeted regulations will behave in a targeted way? AI will likely behave in novel unexpected ways that we will have to regulate for. Secondly, without concrete arguments for why current regulations are good enough for AI, just saying "current regulations are fine" is a form of [confirmation bias](https://en.wikipedia.org/wiki/Confirmation_bias). This is especially true since many regulations for algorithms have actually failed, including [Meta's own engagement algorithm recommending radical content](https://www.pbs.org/wgbh/nova/article/radical-ideas-social-media-algorithms/). Finally, from a more practical standpoint, it is not clear that existing regulations will apply smoothly to AI. For instance, many experts in the EU AI sector point out that as AI is being integrated into more and more products, [businesses are faced with double regulatory obligations](https://dl.acm.org/doi/abs/10.1145/3593013.3594079): is the responsible regulatory body for a product the sector specific institution, an IT-security institution or even a new AI body forthcoming thanks to the AI Act? 

In short, without clear guidelines on how to regulate AI *specifically*, there will be a tendency for creating legal friction rather than functioning regulation. Hence, overall it seems that AFO cannot really be defended against the objection from Survivorship Bias.

**Conclusion** 
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In conclusion, I have argued that many debates about AI Safety revolve around a flawed Argument from Overestimation which suffers from the Survivorship Bias, using Yann LeCun's arguments form the Munk Debate as example. I have demonstrated that AFO is question-begging due to this bias, as it implicitly groups AI with a sample of only safe technologies, even though AI safety is the point AFO is trying to prove. Overall, it seems that AFO is a flawed argument and I hope that as the debate on AI Safety progresses, people will be motivated to look for better arguments. All feedback is very welcome in the comments!

*Big thank you to Sam Robinson for valuable comments!*

*Cross-posted from the EA Forum:* [*https://forum.effectivealtruism.org/posts/yQHzdmXa7KBB52fBz/ai-risk-and-survivorship-bias-how-andreessen-and-lecun-got*](https://forum.effectivealtruism.org/posts/yQHzdmXa7KBB52fBz/ai-risk-and-survivorship-bias-how-andreessen-and-lecun-got)