# AI Safety Concepts

AI safety is an interdisciplinary field focused on preventing accidents, misuse, or other harmful consequences from artificial intelligence systems, encompassing alignment, monitoring, and robustness, with growing attention to existential risks.

AI safety is an interdisciplinary field focused on preventing accidents, misuse, or other harmful consequences arising from artificial intelligence systems. It encompasses AI alignment, which aims to ensure AI systems behave as intended, monitoring AI systems for risks, and enhancing their robustness. The field is particularly concerned with existential risks posed by advanced AI models, but also addresses near-term issues such as bias, surveillance, and cyber threats.

Beyond technical research, AI safety involves developing norms and policies that promote safety, including advocacy for regulations at different levels of government. The field gained significant popularity in 2023, with rapid progress in generative AI and public concerns voiced by researchers and CEOs about potential dangers. During the 2023 AI Safety Summit, the United States and the United Kingdom both established their own AI Safety Institute. However, researchers have expressed concern that AI safety measures are not keeping pace with the rapid development of AI capabilities.

## Motivations

Scholars discuss current risks from critical systems failures, bias, and AI-enabled surveillance, as well as emerging risks like technological unemployment, digital manipulation, weaponization, AI-enabled cyberattacks and bioterrorism. They also discuss speculative risks from losing control of future artificial general intelligence (AGI) agents, or from AI enabling perpetually stable dictatorships.

### Existential safety

Some have criticized concerns about AGI, such as Andrew Ng who compared them in 2015 to "worrying about overpopulation on Mars when we have not even set foot on the planet yet". Stuart J. Russell on the other side urges caution, arguing that "it is better to anticipate human ingenuity than to underestimate it".

AI researchers have widely differing opinions about the severity and primary sources of risk posed by AI technology – though surveys suggest that experts take high consequence risks seriously. In two surveys of AI researchers, the median respondent was optimistic about AI overall, but placed a 5% probability on an "extremely bad (e.g. human extinction)" outcome of advanced AI. In a 2022 survey of the natural language processing community, 37% agreed or weakly agreed that it is plausible that AI decisions could lead to a catastrophe that is "at least as bad as an all-out nuclear war".

## History

Risks from AI began to be seriously discussed at the start of the computer age. In 1951, Alan Turing wrote: "Moreover, if we move in the direction of making machines which learn and whose behavior is modified by experience, we must face the fact that every degree of independence we give the machine is a degree of possible defiance of our wishes." In 1988, Blay Whitby published a book outlining the need for AI to be developed along ethical and socially responsible lines.

From 2008 to 2009, the Association for the Advancement of Artificial Intelligence (AAAI) commissioned a study to explore and address potential long-term societal influences of AI research and development. The panel was generally skeptical of the radical views expressed by science-fiction authors but agreed that "additional research would be valuable on methods for understanding and verifying the range of behaviors of complex computational systems to minimize unexpected outcomes".

In 2011, Roman Yampolskiy introduced the term "AI safety engineering" at the Philosophy and Theory of Artificial Intelligence conference, listing prior failures of AI systems and arguing that "the frequency and seriousness of such events will steadily increase as AIs become more capable".

In 2014, philosopher Nick Bostrom published the book Superintelligence: Paths, Dangers, Strategies. He has the opinion that the rise of AGI has the potential to create various societal issues, ranging from the displacement of the workforce by AI, manipulation of political and military structures, to even the possibility of human extinction. His argument that future advanced systems may pose a threat to human existence prompted Elon Musk, Bill Gates, and Stephen Hawking to voice similar concerns.

In 2015, dozens of artificial intelligence experts signed an open letter on artificial intelligence calling for research on the societal impacts of AI and outlining concrete directions. To date, the letter has been signed by over 8000 people including Yann LeCun, Shane Legg, Yoshua Bengio, and Stuart Russell. In the same year, a group of academics led by professor Stuart J. Russell founded the Center for Human-Compatible AI at the University of California Berkeley and the Future of Life Institute awarded $6.5 million in grants for research aimed at "ensuring artificial intelligence (AI) remains safe, ethical and beneficial".

In 2016, the White House Office of Science and Technology Policy and Carnegie Mellon University announced The Public Workshop on Safety and Control for Artificial Intelligence, which was one of a sequence of four White House workshops aimed at investigating "the advantages and drawbacks" of AI. In the same year, Concrete Problems in AI Safety – one of the first and most influential technical AI Safety agendas – was published.

In 2017, the Future of Life Institute sponsored the Asilomar Conference on Beneficial AI, where more than 100 thought leaders formulated principles for beneficial AI including "Race Avoidance: Teams developing AI systems should actively cooperate to avoid corner-cutting on safety standards".

In 2018, the DeepMind Safety team outlined AI safety problems in specification, robustness, and assurance. The following year, researchers organized a workshop at ICLR that focused on these problem areas. In 2021, Unsolved Problems in ML Safety was published, outlining research directions in robustness, monitoring, alignment, and systemic safety.

In 2023, Rishi Sunak said he wants the United Kingdom to be the "geographical home of global AI safety regulation" and to host the first global summit on AI safety. The AI safety summit took place in November 2023, and focused on the risks of misuse and loss of control associated with frontier AI models. During the summit, the intention to create the International Scientific Report on the Safety of Advanced AI was announced.

In 2024, the US and UK forged a new partnership on the science of AI safety. The MoU was signed on 1 April 2024 by US commerce secretary Gina Raimondo and UK technology secretary Michelle Donelan to jointly develop advanced AI model testing, following commitments announced at an AI Safety Summit in Bletchley Park in November.

In 2025, an international team of 96 experts chaired by Yoshua Bengio published the first International AI Safety Report. The report, commissioned by 30 nations and the United Nations, represents the first global scientific review of potential risks associated with advanced artificial intelligence. It details potential threats stemming from misuse, malfunction, and societal disruption, with the objective of informing policy through evidence-based findings.

## Technical Approaches

Technical AI safety research spans several subfields. Robustness aims to ensure AI systems perform reliably under distribution shift, adversarial attacks, and out-of-distribution inputs. Monitoring involves detecting and predicting harmful behaviors, such as using anomaly detection or interpretability tools. Alignment focuses on making AI systems' objectives and behaviors consistent with human values and intentions. This includes specification (defining what we want), assurance (verifying compliance), and corrigibility (allowing correction).

Concrete Problems in AI Safety (2016) identified five research areas: negative side effects, reward hacking, scalable oversight, safe exploration, and distributional shift. These problems remain central to the field. Subsequent work has expanded to include interpretability, which aims to understand the internal mechanisms of models like [large language models](https://www.wikiprompt.org/wiki/large-language-model), and systemic safety, which addresses risks from multi-agent interactions and societal impacts.

## Institutions and Governance

AI safety research is conducted at academic institutions and industry labs. The Center for Human-Compatible AI at UC Berkeley, the Future of Humanity Institute at Oxford University, and the Machine Intelligence Research Institute are notable academic hubs. Industry efforts include DeepMind's safety team, OpenAI's alignment research, and Anthropic's constitutional AI approach. Government bodies such as the US AI Safety Institute and the UK AI Safety Institute were established in 2023 to evaluate frontier models.

International cooperation has grown, exemplified by the International AI Safety Report (2025) and bilateral agreements like the US-UK partnership. However, governance remains fragmented, with debates over the appropriate level of regulation and the pace of safety research relative to capability development.

## Challenges and Criticisms

AI safety faces several challenges. Technical difficulties include the complexity of specifying human values, the opacity of neural networks, and the difficulty of verifying alignment in advanced systems. There are also social challenges, such as the risk of safety measures being bypassed in competitive environments, and the need for interdisciplinary collaboration.

Critics argue that existential risk concerns are overblown, drawing attention away from more immediate harms like bias and surveillance. Others contend that safety research is underfunded and that the field lacks clear metrics for success. The rapid advancement of [generative AI](https://www.wikiprompt.org/wiki/generative-ai) has intensified these debates, with some researchers calling for moratoriums on certain developments.

## Future Directions

Future AI safety research is likely to focus on scalable oversight, interpretability, and robust alignment for increasingly capable systems. The development of [AI](https://www.wikiprompt.org/wiki/artificial-intelligence) that can self-improve raises new questions about control and value locking. International coordination and the establishment of safety standards will be crucial. As AI systems become more integrated into society, the field will need to address not only technical risks but also ethical, legal, and political dimensions.

The International AI Safety Report (2025) emphasizes the need for evidence-based policy and continued research. The field is expected to grow, with more institutions and governments investing in safety measures. However, the gap between AI capabilities and safety research remains a concern, as noted by many experts.

## See Also

- [machine-learning](https://www.wikiprompt.org/wiki/machine-learning)
- [deep-learning](https://www.wikiprompt.org/wiki/deep-learning)
- [neural-network](https://www.wikiprompt.org/wiki/neural-network)
- [transformer](https://www.wikiprompt.org/wiki/transformer)
- [openai](https://www.wikiprompt.org/wiki/openai)
- [anthropic](https://www.wikiprompt.org/wiki/anthropic)
- [google-deepmind](https://www.wikiprompt.org/wiki/google-deepmind)
- [berkeley-ai-research](https://www.wikiprompt.org/wiki/berkeley-ai-research)
- [oxford-university](https://www.wikiprompt.org/wiki/oxford-university)

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Source: https://www.wikiprompt.org/wiki/ai-safety-concepts
License: CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0/)
Last updated: 2026-09-12T22:27:01.809564+00:00