MIT AI Risk Repository
Browse AI risks
554 risk entries extracted from 74 frameworks, coded by domain, subdomain, causal entity, intent and timing. Filter, then export the current selection with its licence and citation attached.
-
41.06.00 · Risk Category
"AI is already helping to combat the impact of climate change with smart technology and sensors reducing emissions. However, it is also a key component in the development of nanobots, which could have dangerous environmental impacts by invisibly modifying substances at nanoscale."
-
41.06.01 · Risk Sub-Category
Accelerated development of nanotechnology produces uncontrolled production of toxic nanoparticles
"AI is a key component for the development of nanobots, which could have dangerous environmental implications by invisibly modifying substances at nanoscale. For example, nanobots could start chemical reactions that would create invisible nanoparticles that are toxic and potentially lethal."
-
44.03.00 · Risk Category
"AI designed to benefit animals, humans, or ecosystems has unintended harmful impact on animals"
-
44.03.01 · Risk Sub-Category
AI is designed in a way that shows ignorant, reckless, or prejudiced lack of consideration for its impact on animals
—
-
44.03.02 · Risk Sub-Category
AI harms animals due to mistake or misadventure in the way the AI operates in practice
—
-
44.04.00 · Risk Category
"AI impacts human or ecological systems in ways that ultimately harm animals"
-
"AI proliferation causes harm to the environment through energy use and e-waste thereby destroying animal habitat"
-
"Replacement by AI of human observation and interaction leads to neglect of certain interests"
-
"Algorithmic recommender systems reinforce and amplify anthropocentric bias or desire of some people for animal cruelty as entertainment — leading to greater harm to animals through reinforcement of meat eating from factory farms, cruel uses of animals for entertainment, etc"
-
47.04.05 · Risk Sub-Category
Environmental, economical, and societal challenges
Environmental cost (energy consumption)
"Training large AI models requires a substantial amount of computing power to handle vast datasets, which translates into high energy consumption."
-
47.04.06 · Risk Sub-Category
Environmental, economical, and societal challenges
Environmental cost (water consumption)
"Data centers use water for cooling to prevent servers from overheating. The water consumption associated with AI training and inference processes can be substantial, impacting local water resources."
-
48.05.00 · Risk Category
"Impacts due to high compute resource utilization in training or operating GAI models, and related outcomes that may adversely impact ecosystems."
-
"Growing compute use in general- purpose AI development and deployment has rapidly increased energy usage associated with general- purpose AI. This trend might continue, potentially leading to strongly increasing CO2 emissions."
-
"Large-scale DL systems can produce signicant carbon emissions as a result of the computational demands of training runs and inference [539]"
-
56.03.00 · Risk Category
"Increasing use of AI systems, and their growing energy needs, could also have environmental impacts. All of these could become more acute as AI becomes more capable."
-
"Biodiversity loss - Over-expansion of technology infrastructure, or inadequate alignment of technology with sustainable practices, leading to deforestation, habitat destruction, and fragmentation and loss of biodiversity."
-
"Excessive energy consumption - Excessive energy use, leading to energy bottlenecks and shortages for communities, organisations, and businesses."
-
"Excessive landfill - Excessive disposal of electrical or electronic equipment leading to ecological/biodiversity damage, and disrupting the livelihoods and eroding the rights of local communities."
-
"Excessive water consumption - Excessive use of water to cool data centres and for other purposes, leading to water restrictions or shortages for local communities or businesses."
-
"General- purpose AI is a moderate but rapidly growing contributor to global environmental impacts through energy use and greenhouse gas (GHG) emissions. Current estimates indicate that data centres and data transmission account for an estimated 1% of global energy- related GHG emissions, with AI consuming 10–28% of data centre energy capacity. AI energy demand is expected to grow substantially by 2026, with some estimates projecting a doubling or more, driven primarily by general-purpose AI systems such as language models."
-
"The impact of AI on the environment, including risks related to climate change and pollution."
-
61.02.23 · Risk Sub-Category
Sources of systemic risks from general-purpose AI
Energy-intensive processes
"AI data collection, storage, and model training are energy-intensive, contributing to environmental risks."
-
"Excessive energy use resulting in energy bottlenecks and shortages for communities, organisations and businesses"
-
68.05.00 · Risk Category
"AI models are often trained using large amounts of computation. This process is very energy intensive, potentially leading to significant greenhouse emissions depending on the energy sources [132]. Experts believe drastically increasing carbon emissions could accelerate climate change, which may constitute a catastrophic risk [133]."
-
"This is the risk of system failure due to code implementation choices or errors."
-
—
-
"Limitations of Human Feedback. During the training of LLMs, inconsistencies can arise from human dataannotators (e.g., the varied cultural backgrounds of these annotators can introduce implicit biases (Peng et al.,2022)) (OpenAI, 2023a). Moreover, they might even introduce biases deliberately, leading to untruthful preferencedata (Casper et al., 2023b). For complex tasks that are hard for humans to evaluate (e.g., the value ofgame state), these challenges become even more salient (Irving et al., 2018)."
-
40.06.00 · Risk Category
"While highly rare, it is known, that occasionally individual bits may be flipped in different hardware devices due to manufacturing defects or cosmic rays hitting just the right spot (Simonite March 7, 2008). This is similar to mutations observed in living organisms and may result in a modification of an intelligent system."
-
49.02.00 · Risk Category
None provided.
-
59.07.00 · Risk Category
"The choice of a trustworthy data source is a first prerequisite in order to fulfill data quality requirements. This is especially the case if third-party data sources are used to develop the AI system."
-
59.08.00 · Risk Category
"The correct understanding of the used data for developing an AI system is a prerequisite to avoid data shortcomings and hinders the development of an AI system which is best suiting for the intended functionality."
-
59.21.00 · Risk Category
"AI systems should be able not only to return output for a given instance but also to provide a corresponding level of confidence. If such a method is not implemented or not working correctly, this can have a negative impact on performance and safety."
-
62.15.09 · Risk Sub-Category
Fine-tuning related (Degrading safety training due to benign fine-tuning)
"When downstream providers of AI systems fine-tune AI models to be more suitable for their needs, the resulting AI model can be more likely to produce undesired or harmful outputs (as compared to the non-fine-tuned model), even if the fine-tuning was done with harmless and commonly used data [154]."
-
"This is the risk resulting from novel behavior acquired through continual learning or self-organization after deployment."
-
24.09.00 · Risk Category
"" AI assistants will need to coordinate with other AI assistants and with humans other than their principal users. This chapter explores the societal risks associated with the aggregate impact of AI assistants whose behaviour is aligned to the interests of particular users. For example, AI assistants may face collective action problems where the best outcomes overall are realised when AI assistants cooperate but where each AI assistant can secure an additional benefit for its user if it defects while others cooperate""
-
"Limitations of Reward Modeling. Training reward models using comparison feedback can pose significantchallenges in accurately capturing human values. For example, these models may unconsciously learn suboptimal or incomplete objectives, resulting in reward hacking (Zhuang and Hadfield-Menell, 2020; Skalse et al.,2022). Meanwhile, using a single reward model may struggle to capture and specify the values of a diversehuman society (Casper et al., 2023b)."
-
39.11.00 · Risk Category
In the era of superintelligence, the agents will be difficult to control for humans... this problem is not solvable considering safety issues, and will be more severe by increasing the autonomy of AI-based agents. Therefore, because of the assumed properties of HLI-based agents, we might be prepared for machines that are definitely possible to be uncontrollable in some situations
-
40.03.00 · Risk Category
"Probably the most talked about source of potential problems with future AIs is mistakes in design. Mainly the concern is with creating a "wrong AI", a system which doesn't match our original desired formal properties or has unwanted behaviors (Dewey, Russell et al. 2015, Russell, Dewey et al. January 23, 2015), such as drives for independence or dominance. Mistakes could also be simple bugs (run time or logical) in the source code, disproportionate weights in the fitness function, or goals misaligned with human values leading to complete disregard for human safety."
-
51.03.00 · Risk Category
"If we get something wrong in the design or construction of an agent, will the agent cooperate in us trying to fix it? This is called error-tolerant design by MIRI-AF and corrigibility by Soares, Fallenstein, et al. (2015). The problem is connected to safe interruptibility as considered by DeepMind."
-
53.01.00 · Risk Category
-
-
53.01.01 · Risk Sub-Category
Alignment failures in existing ML systems
Faulty reward functions in the wild
-
-
-
-
55.05.00 · Risk Category
"The values that steer humanity’s future: humanity gaining more control over the future due to developments in AI, or losing our potential for gaining control, both seem possible. Much will depend on our ability to solve the alignment problem, who develops powerful AI first, and what they use it for. These long-term impacts of AI could be hugely important but are currently under-explored. We’ve attempted to structure some of the discussion and stimulate more research, by reviewing existing arguments and highlighting open questions. While there are many ways AI could in theory enable a flourish
-
61.02.24 · Risk Sub-Category
Sources of systemic risks from general-purpose AI
Evolutionary dynamics
"AI models and systems may develop their own motivations, leading to unpredictable behaviors."
-
62.16.01 · Risk Sub-Category
General Evaluations (Incorrect outputs of GPAI evaluating other AI models)
"When an LLM is configured to evaluate the performance of another model or AI system, it may produce incorrect evaluation outputs [122, 147]. For example, it may give a higher rating to a more verbose answer or an answer from a particular political stance. If an LLM-based evaluation is integrated into the training of a new model, the trained model could develop in a way that specifically finds and exploits limitations in the evaluator’s metrics."
-
"For LLM-agents, both the goal and environment observations are typically specified in the prompt through natural language. While natural language may provide a richer and more natural means of specifying goals than alternatives such as hand-engineering objective functions, natural language still suffers from underspecification (Grice, 1975; Piantadosi et al., 2012). Furthermore, in practice, users may neglect fully specifying their goals, especially the information pertaining to elements of the environment that ought not to be changed (the classic frame problem (Shanahan, 2016)). Such undersp
-
"As the general capabilities of LLM-empowered systems improve, the negative consequences and risks induced by these systems also get increasingly alarming accordingly, especially in high-stakes areas [28, 146]. Although they may not be intentionally introduced, severe problematic issues related to human values can be raised. Specifically, even before language models become extremely large, pre-trained language models have already exhibited a certain degree of value judgments. For example, Schramowski et al. [171] reveal the existence of the moral direction with the sentence embeddings of moral
-
35.06.00 · Risk Category
Capabilities and novel functionality can spontaneously emerge... even though these capabilities were not anticipated by system designers. If we do not know what capabilities systems possess, systems become harder to control or safely deploy. Indeed, unintended latent capabilities may only be discovered during deployment. If any of these capabilities are hazardous, the effect may be irreversible.
-
39.05.00 · Risk Category
may appear from intelligent agents such as HLI-based agents... Since HLI-based agents are going to mimic the behavior of humans, they may learn these behaviors accidentally from human-generated data. It should be noted that deception and cheating maybe appear in the behavior of every computer agent because the agent only focuses on optimizing some predefined objective functions, and the mentioned behavior may lead to optimizing the objective functions without any intention
-
47.04.07 · Risk Sub-Category
Environmental, economical, and societal challenges
Artificial general intelligence (existential risk posed by Artificial General Intelligence)
"In a paper called “How Does Artificial Intelligence Pose an Existential Risk?” published in 2017, Karina Vold and Daniel Harris suggested that humans might create a super-intelligent machine that could outsmart all other intelligences, remain beyond human control, and potentially engage in actions that are contrary to human interests.635 The prevailing narrative surrounding AI existential risk typically lies in the possibility of developing “Artificial General Intelligence” (AGI), or artificial super- intelligence (ASI)."
Informational only, not legal advice. Verify every claim against the linked official sources and consult qualified counsel before acting.