This is the final post in a sequence of posts giving an overview of catastrophic AI risks.
6 Discussion of Connections Between Risks
So far, we have considered four sources of AI risk separately, but they also interact with each other in complex ways. We give some examples to illustrate how risks are connected.
Imagine, for instance, that a corporate AI race compels companies to prioritize the rapid development of AIs. This could increase organizational risks in various ways. Perhaps a company could cut costs by putting less money toward information security, leading to one of its AI systems getting leaked. This would increase the probability of someone with malicious intent having the AI system and using it to pursue their harmful objectives. Here, an AI race can increase organizational risks, which in turn can make malicious use more likely.
In another potential scenario, we could envision the combination of an intense AI race and low organizational safety leading a research team to mistakenly view general capabilities advances as “safety.” This could hasten the development of increasingly capable models, reducing the available time to learn how to make them controllable. The accelerated development would also likely feed back into competitive pressures, meaning that less effort would be spent on ensuring models were controllable. This could give rise to the release of a highly powerful AI system that we lose control over, leading to a catastrophe. Here, competitive pressures and low organizational safety can reinforce AI race dynamics, which can undercut technical safety research and increase the chance of a loss of control.
Competitive pressures in a military environment could lead to an AI arms race, and increase the potency and autonomy of AI weapons. The deployment of AI-powered weapons, paired with insufficient control of them, would make a loss of control more deadly, potentially existential. These are just a few examples of how these sources of risk might combine, trigger, and reinforce one another.
It is also worth noting that many existential risks could arise from AIs amplifying existing concerns. Power inequality already exists, but AIs could lock it in and widen the chasm between the powerful and the powerless, even enabling an unshakable global totalitarian regime, an existential risk. Similarly, AI manipulation could undermine democracy, which also increases the existential risk of an irreversible totalitarian regime. Disinformation is already a pervasive problem, but AIs could exacerbate it beyond control, to a point where we lose a consensus on reality. AI-enabled cyberattacks could make war more likely, which would increase existential risk. Dramatically accelerated economic automation could lead to eroded human control and enfeeblement, an existential risk. Each of those issues—power concentration, disinformation, cyberattacks, automation—is causing ongoing harm, and their exacerbation by AIs could eventually lead to a catastrophe humanity may not recover from.
As we can see, ongoing harms, catastrophic risks, and existential risks are deeply intertwined. Historically, existential risk reduction has focused on targeted interventions such as technical AI control research, but the time has come for broad interventions [133] like the many sociotechnical interventions outlined in this paper. In mitigating existential risk, it no longer makes practical sense to ignore other risks. Ignoring ongoing harms and catastrophic risks normalizes them and could lead us to “drift into danger” [134]. Overall, since existential risks are connected to less extreme catastrophic risks and other standard risk sources, and because society is increasingly willing to address various risks from AIs, we believe that we should not solely focus on directly targeting existential risks. Instead, we should consider the diffuse, indirect effects of other risks and take a more comprehensive approach to risk management.
7 Conclusion
In this paper, we have explored how the development of advanced AIs could lead to catastrophe, stemming from four primary sources of risk: malicious use, AI races, organizational risks, and rogue AIs. This lets us decompose AI risks into four proximate causes: an intentional cause, environmental cause, accidental cause, or an internal cause, respectively. We have considered ways in which AIs might be used maliciously, such as terrorists using AIs to create deadly pathogens. We have looked at how an AI race in military or corporate settings could rush us into giving AIs decision-making powers, leading us down a slippery slope to human disempowerment. We have discussed how inadequate organizational safety could lead to catastrophic accidents. Finally, we have addressed the challenges in reliably controlling advanced AIs, including mechanisms such as proxy gaming and goal drift that might give rise to rogue AIs pursuing undesirable actions without regard for human wellbeing.
These dangers warrant serious concern. Currently, very few people are working on AI risk reduction. We do not yet know how to control highly advanced AI systems, and existing control methods are already proving inadequate. The inner workings of AIs are not well understood, even by those who create them, and current AIs are by no means highly reliable. As AI capabilities continue to grow at an unprecedented rate, they could surpass human intelligence in nearly all respects relatively soon, creating a pressing need to manage the potential risks.
The good news is that there are many courses of action we can take to substantially reduce these risks. The potential for malicious use can be mitigated by various measures, such as carefully targeted surveillance and limiting access to the most dangerous AIs. Safety regulations and cooperation between nations and corporations could help us resist competitive pressures driving us down a dangerous path. The probability of accidents can be reduced by a rigorous safety culture, among other factors, and by ensuring safety advances outpaces general capabilities advances. Finally, the risks inherent in building technology that surpasses our own intelligence can be addressed by redoubling efforts in several branches of AI control research.
As capabilities continue to grow, and social and systemic circumstances continue to evolve, estimates vary for when risks might reach a catastrophic or existential level. However, the uncertainty around these timelines, together with the magnitude of what could be at stake, makes a convincing case for a proactive approach to safeguarding humanity’s future. Beginning this work immediately can help ensure that this technology transforms the world for the better, and not for the worse.
Acknowledgements
We would like to thank Laura Hiscott, Avital Morris, David Lambert, Kyle Gracey, and Aidan O’Gara for assistance in drafting this paper. We would also like to thank Jacqueline Harding, Nate Sharadin, William D’Alessandro, Cameron Domenico Kirk-Gianini, Simon Goldstein, Alex Tamkin, Adam Khoja, Oliver Zhang, Jack Cunningham, Lennart Justen, Davy Deng, Ben Snyder, Willy Chertman, Justis Mills, Hadrien Pouget, Nathan Calvin, Eric Gan, Lukas Finnveden, Ryan Greenblatt, and Andrew Doris for helpful feedback.
Appendix: Frequently Asked Questions
Since AI catastrophic risk is a new challenge, albeit one that has been the subject of extensive speculation in popular culture, there are many questions about if and how it might manifest. Although public attention may focus on the most dramatic risks, some of the more mundane sources of risk discussed in this document may be equally severe. In addition, many of the simplest ideas one might have for addressing these risks turn out to be insufficient on closer inspection. We will now address some of the most common questions and misconceptions about catastrophic AI risk.
1. Shouldn’t we address AI risks in the future when AIs can actually do everything a human can?
It is not necessarily the case that human-level AI is far in the future. Many top AI researchers think that human-level AI will be developed fairly soon, so urgency is warranted. Furthermore, waiting until the last second to start addressing AI risks is waiting until it’s too late. Just as waiting to fully understand COVID-19 before taking any action would have been a mistake, it is ill-advised to procrastinate on safety and wait for malicious AIs or bad actors to cause harm before taking AI risks seriously.
One might argue that since AIs cannot even drive cars or fold clothes yet, there is no need to worry. However, AIs do not need all human capabilities to pose serious threats; they only need a few specific capabilities to cause catastrophe. For example, AIs with the ability to hack computer systems or create bioweapons would pose significant risks to humanity, even if they couldn’t iron a shirt. Furthermore, the development of AI capabilities has not followed an intuitive pattern where tasks that are easy for humans are the first to be mastered by AIs. Current AIs can already perform complex tasks such as writing code and designing novel drugs, even while they struggle with simple physical tasks. Like climate change and COVID-19, AI risk should be addressed proactively, focusing on prevention and preparedness rather than waiting for consequences to manifest themselves, as they may already be irreparable by that point.
2. Since humans program AIs, shouldn’t we be able to shut them down if they become dangerous?
While humans are the creators of AI, maintaining control over these creations as they evolve and become more autonomous is not a guaranteed prospect. The notion that we could simply “shut them down” if they pose a threat is more complicated than it first appears.
First, consider the rapid pace at which an AI catastrophe could unfold. Analogous to preventing a rocket explosion after detecting a gas leak, or halting the spread of a virus already rampant in the population, the time between recognizing the danger and being able to prevent or mitigate it could be precariously short.
Second, over time, evolutionary forces and selection pressures could create AIs exhibiting selfish behaviors that make them more fit, such that it is harder to stop them from propagating their information. As these AIs continue to evolve and become more useful, they may become central to our societal infrastructure and daily lives, analogous to how the internet has become an essential, non-negotiable part of our lives with no simple off-switch. They might manage critical tasks like running our energy grids, or possess vast amounts of tacit knowledge, making them difficult to replace. As we become more reliant on these AIs, we may voluntarily cede control and delegate more and more tasks to them. Eventually, we may find ourselves in a position where we lack the necessary skills or knowledge to perform these tasks ourselves. This increasing dependence could make the idea of simply “shutting them down” not just disruptive, but potentially impossible.
Similarly, some people would strongly resist or counteract attempts to shut them down, much like how we cannot permanently shut down all illegal websites or shut down Bitcoin—many people are invested in their continuation. As AIs become more vital to our lives and economies, they could develop a dedicated user base, or even a fanbase, that could actively resist attempts to restrict or shut down AIs. Likewise, consider the complications arising from malicious actors. If malicious actors have control over AIs, they could potentially use them to inflict harm. Unlike AIs under benign control, we wouldn’t have an off-switch for these systems.
Next, as some AIs become more and more human-like, some may argue that these AIs should have rights. They could argue that not giving them rights is a form of slavery and is morally abhorrent. Some countries or jurisdictions may grant certain AIs rights. In fact, there is already momentum to give AIs rights. Sophia the Robot has already been granted citizenship in Saudi Arabia, and Japan granted a robot named Paro a koseki, or household registry, “which confirms the robot’s Japanese citizenship” [135]. There may come a time when switching off an AI could be likened to murder. This would add a layer of political complexity to the notion of a simple “off-switch.”
Lastly, as AIs gain more power and autonomy, they might develop a drive for “self-preservation.” This would make them resistant to shutdown attempts and could allow them to anticipate and circumvent our attempts at control. Given these challenges, it’s critical that we address potential AI risks proactively and put robust safeguards in place well before these problems arise.
3. Why can’t we just tell AIs to follow Isaac Asimov’s Three Laws of Robotics?
Asimov’s laws, often highlighted in AI discussions, are insightful but inherently flawed. Indeed, Asimov himself acknowledges their limitations in his books and uses them primarily as an illustrative tool. Take the first law, for example. This law dictates that robots “may not injure a human being or, through inaction, allow a human being to come to harm,” but the definition of “harm” is very nuanced. Should your home robot prevent you from leaving your house and entering traffic because it could potentially be harmful? On the other hand, if it confines you to the home, harm might befall you there as well. What about medical decisions? A given medication could have harmful side effects for some people, but not administering it could be harmful as well. Thus, there would be no way to follow this law. More importantly, the safety of AI systems cannot be ensured merely through a list of axioms or rules. Moreover, this approach would fail to address numerous technical and sociotechnical problems, including goal drift, proxy gaming, and competitive pressures. Therefore, AI safety requires a more comprehensive, proactive, and nuanced approach than simply devising a list of rules for AIs to adhere to.
4. If AIs become more intelligent than people, wouldn’t they be wiser and more moral? That would mean they would not aim to harm us.
The idea of AIs becoming inherently more moral as they increase in intelligence is an intriguing concept, but rests on uncertain assumptions that can’t guarantee our safety. Firstly, it assumes that moral claims can be true or false and their correctness can be discovered through reason. Secondly, it assumes that the moral claims that are really true would be beneficial for humans if AIs apply them. Thirdly, it assumes that AIs that know about morality will choose to make their decisions based on morality and not based on other considerations. An insightful parallel can be drawn to human sociopaths, who, despite their intelligence and moral awareness, do not necessarily exhibit moral inclinations or actions. This comparison illustrates that knowledge of morality does not always lead to moral behavior. Thus, while some of the above assumptions may be true, betting the future of humanity on the claim that all of them are true would be unwise.
Assuming AIs could indeed deduce a moral code, its compatibility with human safety and wellbeing is not guaranteed. For example, AIs whose moral code is to maximize wellbeing for all life might seem good for humans at first. However, they might eventually decide that humans are costly and could be replaced with AIs that experience positive wellbeing more efficiently. AIs whose moral code is not to kill anyone would not necessarily prioritize human wellbeing or happiness, so our lives may not necessarily improve if the world begins to be increasingly shaped by and for AIs. Even AIs whose moral code is to improve the wellbeing of the worst-off in society might eventually exclude humans from the social contract, similar to how many humans view livestock. Finally, even if AIs discover a moral code that is favorable to humans, they may not act on it due to potential conflicts between moral and selfish motivations. Therefore, the moral progression of AIs is not inherently tied to human safety or prosperity.
5. Wouldn’t aligning AI systems with current values perpetuate existing moral failures?
There are plenty of moral failures in society today that we would not want powerful AI systems to perpetuate into the future. If the ancient Greeks had built powerful AI systems, they might have imbued them with many values that people today would find unethical. However, this concern should not prevent us from developing methods to control AI systems.
To achieve any value in the future, life needs to exist in the first place. Losing control over advanced AIs could constitute an existential catastrophe. Thus, uncertainty over what ethics to embed in AIs is not in tension with whether to make AIs safe.
To accommodate moral uncertainty, we should deliberately build AI systems that are adaptive and responsive to evolving moral views. As we identify moral mistakes and improve our ethical understanding, the goals we give to AIs should change accordingly—though allowing AI goals to drift unintentionally would be a serious mistake. AIs could also help us better live by our values. For individuals, AIs could help people have more informed preferences by providing them with ideal advice [132].
Separately, in designing AI systems, we should recognize the fact of reasonable pluralism, which acknowledges that reasonable people can have genuine disagreements about moral issues due to their different experiences and beliefs [136]. Thus, AI systems should be built to respect a diverse plurality of human values, perhaps by using democratic processes and theories of moral uncertainty. Just as people today convene to deliberate on disagreements and make consensus decisions, AIs could emulate a parliament representing different stakeholders, drawing on different moral views to make real-time decisions [55, 137]. It is crucial that we deliberately design AI systems to account for safety, adaptivity, stakeholders with different values.
6. Wouldn’t the potential benefits that AIs could bring justify the risks?
The potential benefits of AI could justify the risks if the risks were negligible. However, the chance of existential risk from AI is too high for it to be prudent to rapidly develop AI. Since extinction is forever, a far more cautious approach is required. This is not like weighing the risks of a new drug against its potential side effects, as the risks are not localized but global. Rather, a more prudent approach is to develop AI slowly and carefully such that existential risks are reduced to a negligible level (e.g., under 0.001% per century).
Some influential technology leaders are accelerationists and argue for rapid AI development to barrel ahead toward a technological utopia. This techno-utopian viewpoint sees AI as the next step down a predestined path toward unlocking humanity’s cosmic endowment. However, the logic of this viewpoint collapses on itself when engaged on its own terms. If one is concerned with the cosmic stakes of developing AI, we can see that even then it’s prudent to bring existential risk to a negligible level. The techno-utopians suggest that delaying AI costs humanity access to a new galaxy each year, but if we go extinct, we could lose the cosmos. Thus, the prudent path is to delay and safely prolong AI development, prioritizing risk reduction over acceleration, despite the allure of potential benefits.
7. Wouldn’t increasing attention on catastrophic risks from AIs drown out today’s urgent risks from AIs?
Focusing on catastrophic risks from AIs doesn’t mean ignoring today’s urgent risks; both can be addressed simultaneously, just as we can concurrently conduct research on various different diseases or prioritize mitigating risks from climate change and nuclear warfare at once. Additionally, current risks from AI are also intrinsically related to potential future catastrophic risks, so tackling both is beneficial. For example, extreme inequality can be exacerbated by AI technologies that disproportionately benefit the wealthy, while mass surveillance using AI could eventually facilitate unshakeable totalitarianism and lock-in. This demonstrates the interconnected nature of immediate concerns and long-term risks, emphasizing the importance of addressing both categories thoughtfully.
Additionally, it’s crucial to address potential risks early in system development. As illustrated by Frola and Miller in their report for the Department of Defense, approximately 75 percent of the most critical decisions impacting a system’s safety occur early in its development [138]. Ignoring safety considerations in the early stages often results in unsafe design choices that are highly integrated into the system, leading to higher costs or infeasibility of retrofitting safety solutions later. Hence, it is advantageous to start addressing potential risks early, regardless of their perceived urgency.
8. Aren’t many AI researchers working on making AIs safe?
Few researchers are working to make AI safer. Currently, approximately 2 percent of papers published at top machine learning venues are safety-relevant [105]. Most of the other 98 percent focus on building more powerful AI systems more quickly. This disparity underscores the need for more balanced efforts. However, the proportion of researchers alone doesn’t equate to overall safety. AI safety is a sociotechnical problem, not just a technical problem. Thus, it requires more than just technical research. Comfort should stem from rendering catastrophic AI risks negligible, not merely from the proportion of researchers working on making AIs safe.
9. Since it takes thousands of years to produce meaningful changes, why do we have to worry about evolution being a driving force in AI development?
Although the biological evolution of humans is slow, the evolution of other organisms, such as fruit flies or bacteria, can be extremely quick, demonstrating the diverse time scales at which evolution operates. The same rapid evolutionary changes can be observed in non-biological structures like software, which evolve much faster than biological entities. Likewise, one could expect AIs to evolve very quickly as well. The rate of AI evolution may be propelled by intense competition, high variation due to diverse forms of AIs and goals given to them, and the ability of AIs to rapidly adapt. Consequently, intense evolutionary pressures may be a driving force in the development of AIs.
10. Wouldn’t AIs need to have a power-seeking drive to pose a serious risk?
While power-seeking AI poses a risk, it is not the only scenario that could potentially lead to catastrophe. Malicious or reckless use of AIs can be equally damaging without the AI itself seeking power. Additionally, AIs might engage in harmful actions through proxy gaming or goal drift without intentionally seeking power. Furthermore, society’s trend toward automation, driven by competitive pressures, is gradually increasing the influence of AIs over humans. Hence, the risk does not solely stem from AIs seizing power, but also from humans ceding power to AIs.
11. Isn’t the combination of human intelligence and AI superior to AI alone, so that there is no need to worry about unemployment or humans becoming irrelevant?
While it’s true that human-computer teams have outperformed computers alone in the past, these have been temporary phenomena. For example, “cyborg chess” is a form of chess where humans and computers work together, which was historically superior to humans or computers alone. However, advancements in computer chess algorithms have eroded the advantage of human-computer teams to such an extent that there is arguably no longer any advantage compared to computers alone. To take a simpler example, no one would pit a human against a simple calculator for long division. A similar progression may occur with AIs. There may be an interim phase where humans and AIs can work together effectively, but the trend suggests that AIs alone could eventually outperform humans in various tasks while no longer benefiting from human assistance.
12. The development of AI seems unstoppable. Wouldn’t slowing it down dramatically or stopping it require something like an invasive global surveillance regime?
AI development primarily relies on high-end chips called GPUs, which can be feasibly monitored and tracked, much like uranium. Additionally, the computational and financial investments required to develop frontier AIs are growing exponentially, resulting in a small number of actors who are capable of acquiring enough GPUs to develop them. Therefore, managing AI growth doesn’t necessarily require invasive global surveillance, but rather a systematic tracking of high-end GPU usage.
References
[133] Nick Beckstead. On the overwhelming importance of shaping the far future. 2013.
[134] Jens Rasmussen. “Risk management in a Dynamic Society: A Modeling Problem”. English. In: Proceedings of the Conference on Human Interaction with Complex Systems, 1996.
[135] Jennifer Robertson. “Human rights vs. robot rights: Forecasts from Japan”. In: Critical Asian Studies 46.4 (2014), pp. 571–598.
[136] John Rawls. Political Liberalism. Columbia University Press, 1993.
[137] Toby Newberry and Toby Ord. “The Parliamentary Approach to Moral Uncertainty”. 2021.
[138] F.R. Frola and C.O. Miller. System Safety in Aircraft Acquisition. en. Tech. rep. Jan. 1984.
Hi I am working on Rob Miles’ Stampy project (https://aisafety.info/), which is creating a centralized resource for answering questions about AI safety and alignment. Would we be able to incorporate your list of frequently asked questions and answers into our system (perhaps with some modification)? I think they are really nice answers to some of the basic questions and would be useful for people curious about the topic to see.