Summary Risk Assessment for AGI Companies Techniques and Recommendations arxiv.org
24,728 words - PDF document - View PDF document
One Line
The text suggests that AGI companies like OpenAI and Google DeepMind should improve their risk management practices by adopting safety-critical industry techniques and considering the role of humans in their control structure models.
Slides
Slide Presentation (12 slides)
Key Points
- AGI companies, such as OpenAI and Google DeepMind, need to improve their risk management practices due to concerns about catastrophic risks associated with artificial general intelligence.
- Risk assessments are essential for identifying, analyzing, and evaluating risks associated with AI systems, including future catastrophic risks and human extinction.
- Qualitative techniques are prioritized for quantifying the likelihood of catastrophic risks from AI, but some quantitative techniques should be attempted for better comparisons and communication.
- Scenario analysis, fishbone method, and risk typologies are recommended risk assessment techniques for AGI companies.
- Cross-impact analysis, Delphi technique, bow tie analysis, and risk matrices are additional techniques that can be used to assess and prioritize risks.
- Involving multiple stakeholders and considering technical, human, and organizational factors is crucial for effective risk assessment in AGI companies.
- The document provides a list of references and sources related to risk assessment for AGI companies, covering topics such as system bias, large language models, debiasing word embeddings, and real-world applications of AI.
Summaries
40 word summary
AGI companies, such as OpenAI and Google DeepMind, should enhance risk management practices in light of concerns about catastrophic risks. The paper suggests utilizing popular risk assessment techniques from safety-critical industries. The control structure model in AGI companies involves human
83 word summary
AGI companies, like OpenAI and Google DeepMind, need to improve their risk management practices in response to concerns about catastrophic risks associated with artificial general intelligence. The paper reviews popular risk assessment techniques from safety-critical industries and suggests ways AGI companies
The control structure model in AGI companies consists of human and technical controllers, with control actions including shutdown mechanisms and changes to the model's architecture. Checklists have been developed to evaluate catastrophic risks from AI systems, saving time and decentralizing risk assessment.
1251 word summary
AGI companies, such as OpenAI and Google DeepMind, need to improve their risk management practices due to concerns about catastrophic risks associated with artificial general intelligence. This paper reviews popular risk assessment techniques from safety-critical industries and suggests ways AGI companies
The prospect of AGI is increasingly taken seriously, with AGI reaching mainstream academic discourse, news coverage, and government agendas. AI systems already cause significant harm, and there are increasing concerns about future catastrophic risks, including human extinction. Risk assessments are essential
AGI companies are those that aim to build AI systems that perform as well as or better than humans on cognitive tasks. Risk is defined as the possibility of an undesired event occurring. Risk assessment involves identifying, analyzing, and evaluating risks. Catast
Numerous surveys and studies have been conducted on the risks associated with AI, including catastrophic risks. Various typologies and taxonomies have been developed to categorize these risks, both for existential risks and societal risks. Different risk analysis techniques have been applied,
There is a lack of comprehensive information on risk assessment techniques for AGI companies. The methodology used to identify and select techniques involved starting with a leading risk assessment standard and adding techniques from other industries. Criteria were then used to exclude and prioritize techniques. Techniques
Quantifying the likelihood of catastrophic risks from AI is challenging, so qualitative techniques are prioritized. However, some quantitative techniques should be attempted for better comparisons and communication. Risk identification is important, and scenario analysis, fishbone method, and risk typologies
AGI companies should use scenario analysis to investigate different futures and plan for a variety of possibilities. Monitoring events that align with a scenario can serve as a warning sign. However, scenarios developed through this technique should not be relied upon as accurate predictions of the
AGI companies can use the fishbone method to identify risk sources, as it is simple, time-efficient, and less likely to miss risk sources compared to brainstorming. However, the fishbone method does not account for interactions between risk sources that do
OpenAI and Anthropic likely have similar risk typologies and taxonomies for catastrophic risks from AI, although they have not made them public. Researchers have proposed typologies that categorize catastrophic risks into accident, misuse, and structural risks. DeepMind has
AGI companies should consider risks associated with various actors and types of harm caused by AI systems. Risk typologies and taxonomies are beneficial for identifying and understanding risks, promoting a common understanding among stakeholders, and supporting other risk assessment techniques. However, creating
New technology like heat pumps and inexperienced stakeholders contribute to the complexity of power and heating. Legislation deadlines for emissions may be missed, and tenants may not understand how to properly use heaters. Causal mapping can be used by AGI companies to identify and explore
AGI companies can use the Delphi technique to inform important decisions by obtaining estimates on the likelihood of specific risks. This can include the likelihood of competitors releasing similar models, receiving more investments, or new AGI companies being founded. The Delphi technique
Cross-impact analysis is a technique that helps organizations understand the interactions and correlations between different events that contribute to a risk. It involves gathering expert forecasts on the likelihood of events and considering the effects of other events. By running a computer analysis on these estimates
Cross-impact analysis is a technique that involves creating a matrix of events and asking experts about their likelihood and interdependencies. Software and statistical methods are used to confirm the consistency of expert estimates and generate future scenarios. AGI companies can use this analysis to
Bow tie analysis is a popular and simple technique that helps organizations assess the effectiveness of their controls in managing risks. It involves mapping causes, consequences, and controls of an undesired event in a diagram that resembles a bow tie. Preventive controls aim to
AGI companies should use bow tie analysis to systematically approach their controls. They should update the maps as changes are made and new knowledge is gained. System-theoretic process analysis (STPA) is a more sophisticated technique that helps assess the effectiveness of
The control structure model consists of human controllers and technical controllers. Control actions include shutdown mechanisms, changes to the model's architecture, and evals conducted during training. UCAs involving controllers, actuators, or sensors failing to respond may be caused by the
Only a few checklists for evaluating catastrophic risks from AI have been developed. These checklists aim to assess the impact of research projects on existential risks and provide a starting point for evaluating risks from AI systems. Checklists can save time, decentralize risk
Different types of risks require separate definitions and scales for evaluation. A consequence/likelihood matrix can be developed to rank the priority of risks. AGI companies can use risk matrices to prioritize catastrophic risks from AI, but evaluating these risks is challenging due to ethical
AGI companies should use risk matrices to visualize and prioritize risks. However, developing a risk matrix can be challenging due to normative judgments and the level of detail required. Valid comparisons of risks may be compromised if the level of detail is inconsistent. Priority
To effectively assess risks in AGI companies, it is important to involve multiple stakeholders and consider not only technical factors but also human and organizational factors. Implementing risk assessment techniques is valuable only if their results inform decision-making. The paper discusses ten popular risk
We are grateful for input and support from various individuals, including Cullen O'Keefe, James Ginns, Malcolm Murray, Emma Bluemke, Jan Brauner, and others. We also acknowledge the use of ChatGPT for editing assistance
This document provides a list of references and sources related to risk assessment for AGI companies. It includes papers and articles on topics such as system bias in natural language processing, the dangers of large language models, debiasing word embeddings, real-world application
This summary provides a concise version of the excerpted text, highlighting key points and preserving important details. The summary is organized into separate paragraphs to distinguish distinct ideas for readability, while retaining the original order in which ideas were presented.
The excerpted text includes
The summary includes a list of various sources related to artificial general intelligence (AGI) and risk assessment techniques. These sources cover topics such as trustworthy AI, cross-impact analysis, multimodal foundation models, red teaming language models, datasheets for
This summary provides a list of references and sources related to risk assessment for AGI companies. The sources include articles, blog posts, papers, and handbooks that discuss various aspects of AI risk, threat models, hazard analysis frameworks, scenario analysis, AI
This summary provides a list of references that are mentioned in the document "Risk Assessment for AGI Companies Techniques and Recommendations." The references include various sources such as reports, preprints, books, and articles. Some notable references include Microsoft's guide for assessing
This summary provides a list of references cited in a document about risk assessment for AGI companies. The references cover various topics related to AI, including risk management, transparency of machine learning, auditing biased AI products, AI functionality, algorithmic auditing, Del
The document includes various sources related to risk assessment for artificial general intelligence (AGI) companies. These sources cover topics such as AI program search, sociotechnical harms of algorithmic systems, model evaluation for extreme risks, guidelines for reliable and safe AI
This excerpt provides a list of various risk assessment techniques for AGI companies. It includes techniques for eliciting views from experts and stakeholders, identifying risks, analyzing causes, consequences, and likelihood of risks, analyzing controls, and evaluating risks. The reasons for