AI Risk Categorization Decoded (AIR 2024): From Government Regulations to Corporate Policies

Yi Zeng, Kevin Klyman, Andy Zhou, Yu Yang, Minzhou Pan, Ruoxi Jia, Dawn Song, Percy Liang, Bo Li·June 25, 2024

Summary

The paper "AI Risk Categorization Decoded (AIR 2024)" develops a comprehensive taxonomy of AI risks, derived from government policies (EU, US, and China) and company policies. The four-tier system categorizes risks into System & Operational, Content Safety, Societal, and Legal & Rights categories, with 314 unique risk categories. The taxonomy aims to standardize language for AI safety evaluation, facilitating information sharing and best practices across sectors to mitigate risks in generative AI models. It compares AI regulations, identifies common concerns, and highlights areas of concern such as confidentiality, automated decision-making, and harmful content. The study also maps regulations to risk categories, revealing differences in risk prioritization and enforcement gaps. The taxonomy serves as a foundation for improved regulations, global cooperation, and responsible AI development.

Key findings

10

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the issue of AI risk categorization, specifically focusing on transitioning from government regulations to corporate policies . This problem is not entirely new, as the paper delves into analyzing the ethical and social risks of harm from language models, which has been a growing concern in the field of artificial intelligence . The research contributes to the ongoing efforts to understand and mitigate the potential risks associated with AI technologies, emphasizing the importance of aligning regulations and policies to ensure safe and trustworthy development and use of artificial intelligence .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to analyzing an expert proposal for China's artificial intelligence law .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper proposes several new ideas, methods, and models related to artificial intelligence regulation and governance:

  • A concrete two-tier proposal for foundation models in the EU AI Act is suggested by Rishi Bommasani, Tatsunori Hashimoto, and others .
  • Foundation model transparency reports are introduced by Rishi Bommasani, Kevin Klyman, and their team .
  • An open robustness benchmark called Jailbreakbench for jailbreaking large language models is presented by Patrick Chao, Edoardo Debenedetti, and collaborators .
  • Harmbench, a standardized evaluation framework for automated red teaming and robust refusal, is introduced by Mantas Mazeika, Long Phan, and colleagues .
  • The paper also discusses the societal impact of open foundation models, as analyzed by Longpre, Ashwin Ramaswami, and others .
  • Additionally, the paper delves into the ethical and social risks of harm from language models, as explored by Laura Weidinger, John Mellor, and their team . The characteristics and advantages of the proposed methods in the paper compared to previous methods are as follows:
  1. Two-Tier Proposal for Foundation Models in the EU AI Act:

    • Characteristics: This proposal suggests a structured approach to regulating foundation models in the EU AI Act, providing clear guidelines for their development and deployment.
    • Advantages: By establishing a two-tier system, it allows for more nuanced regulation based on the potential risks and impacts of different types of AI models. This approach enhances transparency and accountability in AI governance.
  2. Foundation Model Transparency Reports:

    • Characteristics: These reports aim to increase transparency around the development and performance of foundation models, detailing aspects like data sources, training processes, and evaluation metrics.
    • Advantages: By providing standardized transparency reports, stakeholders can better understand the inner workings of AI models, fostering trust and enabling informed decision-making regarding their use.
  3. Jailbreakbench for Jailbreaking Large Language Models:

    • Characteristics: This open robustness benchmark focuses on evaluating the security and robustness of large language models through simulated attacks.
    • Advantages: Jailbreakbench offers a standardized framework for assessing the vulnerability of language models to adversarial inputs, helping researchers and developers enhance the security of AI systems.
  4. Harmbench for Automated Red Teaming and Robust Refusal:

    • Characteristics: Harmbench provides a systematic evaluation platform for testing the resilience of AI systems against malicious inputs and assessing their ability to reject harmful requests.
    • Advantages: By using Harmbench, developers can identify and address vulnerabilities in AI models, improving their capacity to withstand attacks and mitigate potential harms.
  5. Societal Impact Analysis of Open Foundation Models:

    • Characteristics: This analysis explores the broader societal implications of open foundation models, considering factors like accessibility, bias, and economic effects.
    • Advantages: By examining the societal impact of AI models, policymakers and stakeholders can make more informed decisions about their deployment, ensuring that AI technologies benefit society as a whole.
  6. Ethical and Social Risk Assessment of Language Models:

    • Characteristics: This assessment focuses on identifying and mitigating ethical and social risks associated with language models, such as misinformation propagation and harmful content generation.
    • Advantages: By proactively addressing ethical concerns and social risks, developers can design AI systems that prioritize safety, fairness, and responsible use, fostering a more ethical AI ecosystem.

Overall, the paper's proposed methods offer enhanced transparency, security, resilience, and ethical considerations compared to previous approaches, contributing to the responsible development and deployment of AI technologies.


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Could you please specify the topic or field you are referring to so I can provide you with more accurate information?


How were the experiments in the paper designed?

The experiments in the paper were designed through a systematic, bottom-up approach to construct an AI risk taxonomy grounded in public and private sector policies . The methodology involved collecting a diverse set of policies from eight government policies and 16 company policies, focusing on their relevance, comprehensiveness, and diversity . Each policy and regulation were analyzed using a consistent process to extract and organize risk categories explicitly referenced in each document, involving parsing every line, clustering related sections, identifying specific risks, and maintaining consistency while highlighting unique categories . The process also included a comparative analysis of risk categories across different policies and regulations to identify similarities and differences in how various entities and jurisdictions address similar risks .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation is called "Harmbench," which is a standardized evaluation framework for automated red teaming and robust refusal . The code for Harmbench is open source and can be accessed through the arXiv preprint arXiv:2402.04249 .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide valuable support for the scientific hypotheses that require verification. The study constructs a comprehensive risk taxonomy based on public and private sector policies related to the regulation of risky uses of generative AI models . By analyzing various government regulations and company policies, the research aims to create a more tractable framework for risk mitigation in the field of AI . The findings highlight substantial differences across companies and their policies in terms of prohibited risk categories, illustrating diverse conceptualizations of risks . Additionally, the study reveals that the union of risk categories in company policies is broader than that of existing government policies, indicating potential gaps in enforcement due to the lack of specificity in AI regulation .

Moreover, the research emphasizes the importance of considering initiatives from different jurisdictions to enhance the analysis of AI safety . By incorporating regulations and policies from the US, EU, and China, the study provides insights into the regulatory landscape faced by multinational companies and opportunities for global cooperation on AI safety . This comprehensive approach allows for a more nuanced understanding of the regulatory environment and potential areas for improvement in policies, regulations, and benchmarks related to AI safety .


What are the contributions of this paper?

The paper provides valuable contributions in the field of AI risk categorization and governance:

  • It introduces a standardized evaluation framework called Harmbench for automated red teaming and robust refusal .
  • It assesses the transparency of AI executive order implementation within 90 days .
  • It tracks the AI executive order by the numbers .
  • It discusses the acceptable use policy and terms of service of Meta and Microsoft related to AI .
  • It presents a risk-based tiered approach to governing general-purpose AI .
  • It analyzes an expert proposal for China's artificial intelligence law .
  • It evaluates the sociotechnical safety of generative AI systems .
  • It compares the governance of artificial intelligence in China and the European Union .
  • It provides insights into the societal impact of open foundation models .

What work can be continued in depth?

Work that can be continued in depth typically involves projects or tasks that require further analysis, research, or development. This could include in-depth research studies, complex problem-solving initiatives, detailed data analysis, comprehensive strategic planning, or thorough process improvement efforts. Essentially, any work that requires a deep dive into the subject matter, exploration of various angles, and a detailed examination of the factors involved can be continued in depth.


Introduction
Background
Overview of AI development and growing concerns
Importance of standardized risk classification
Objective
To develop a comprehensive taxonomy for AI risks
Standardize language and facilitate risk evaluation
Address global cooperation and responsible AI practices
Method
Data Collection
Government policies (EU, US, China) analysis
Company policies and guidelines review
Data Preprocessing
Extraction and synthesis of risk themes
Cross-referencing and categorization
Taxonomy Development
System & Operational Risks
Automated failures and system vulnerabilities
Cybersecurity threats
Supply chain risks
Content Safety
Harmful content generation
Misinformation and disinformation
Bias and algorithmic discrimination
Societal Risks
Job displacement and economic impact
Ethical implications (e.g., fairness, transparency)
Social cohesion and inequality
Legal & Rights Risks
Privacy and confidentiality breaches
Accountability and liability
Human rights violations
Comparative Analysis
AI regulations comparison across jurisdictions
Common concerns and areas of concern
Enforcement gaps and recommendations
Mapping Regulations to Risks
Risk prioritization by region
Regulatory gaps and harmonization needs
Applications and Implications
Standardizing risk assessments in AI development
Enhancing global collaboration on AI safety
Fostering responsible AI practices and guidelines
Conclusion
Summary of key findings and taxonomy's significance
Future directions and potential for continuous improvement
Call to action for stakeholders in AI governance.
Basic info
papers
computers and society
artificial intelligence
Advanced features
Insights
How many risk categories does the four-tier system in the paper classify AI risks into?
What is the primary focus of the "AI Risk Categorization Decoded (AIR 2024)" paper?
Which governments' policies does the taxonomy draw upon for its development?
What are the main goals of using this standardized language for AI safety evaluation, as mentioned in the paper?

AI Risk Categorization Decoded (AIR 2024): From Government Regulations to Corporate Policies

Yi Zeng, Kevin Klyman, Andy Zhou, Yu Yang, Minzhou Pan, Ruoxi Jia, Dawn Song, Percy Liang, Bo Li·June 25, 2024

Summary

The paper "AI Risk Categorization Decoded (AIR 2024)" develops a comprehensive taxonomy of AI risks, derived from government policies (EU, US, and China) and company policies. The four-tier system categorizes risks into System & Operational, Content Safety, Societal, and Legal & Rights categories, with 314 unique risk categories. The taxonomy aims to standardize language for AI safety evaluation, facilitating information sharing and best practices across sectors to mitigate risks in generative AI models. It compares AI regulations, identifies common concerns, and highlights areas of concern such as confidentiality, automated decision-making, and harmful content. The study also maps regulations to risk categories, revealing differences in risk prioritization and enforcement gaps. The taxonomy serves as a foundation for improved regulations, global cooperation, and responsible AI development.
Mind map
Human rights violations
Accountability and liability
Privacy and confidentiality breaches
Social cohesion and inequality
Ethical implications (e.g., fairness, transparency)
Job displacement and economic impact
Bias and algorithmic discrimination
Misinformation and disinformation
Harmful content generation
Supply chain risks
Cybersecurity threats
Automated failures and system vulnerabilities
Cross-referencing and categorization
Extraction and synthesis of risk themes
Company policies and guidelines review
Government policies (EU, US, China) analysis
Address global cooperation and responsible AI practices
Standardize language and facilitate risk evaluation
To develop a comprehensive taxonomy for AI risks
Importance of standardized risk classification
Overview of AI development and growing concerns
Call to action for stakeholders in AI governance.
Future directions and potential for continuous improvement
Summary of key findings and taxonomy's significance
Fostering responsible AI practices and guidelines
Enhancing global collaboration on AI safety
Standardizing risk assessments in AI development
Regulatory gaps and harmonization needs
Risk prioritization by region
Enforcement gaps and recommendations
Common concerns and areas of concern
AI regulations comparison across jurisdictions
Legal & Rights Risks
Societal Risks
Content Safety
System & Operational Risks
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Applications and Implications
Mapping Regulations to Risks
Comparative Analysis
Taxonomy Development
Method
Introduction
Outline
Introduction
Background
Overview of AI development and growing concerns
Importance of standardized risk classification
Objective
To develop a comprehensive taxonomy for AI risks
Standardize language and facilitate risk evaluation
Address global cooperation and responsible AI practices
Method
Data Collection
Government policies (EU, US, China) analysis
Company policies and guidelines review
Data Preprocessing
Extraction and synthesis of risk themes
Cross-referencing and categorization
Taxonomy Development
System & Operational Risks
Automated failures and system vulnerabilities
Cybersecurity threats
Supply chain risks
Content Safety
Harmful content generation
Misinformation and disinformation
Bias and algorithmic discrimination
Societal Risks
Job displacement and economic impact
Ethical implications (e.g., fairness, transparency)
Social cohesion and inequality
Legal & Rights Risks
Privacy and confidentiality breaches
Accountability and liability
Human rights violations
Comparative Analysis
AI regulations comparison across jurisdictions
Common concerns and areas of concern
Enforcement gaps and recommendations
Mapping Regulations to Risks
Risk prioritization by region
Regulatory gaps and harmonization needs
Applications and Implications
Standardizing risk assessments in AI development
Enhancing global collaboration on AI safety
Fostering responsible AI practices and guidelines
Conclusion
Summary of key findings and taxonomy's significance
Future directions and potential for continuous improvement
Call to action for stakeholders in AI governance.
Key findings
10

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the issue of AI risk categorization, specifically focusing on transitioning from government regulations to corporate policies . This problem is not entirely new, as the paper delves into analyzing the ethical and social risks of harm from language models, which has been a growing concern in the field of artificial intelligence . The research contributes to the ongoing efforts to understand and mitigate the potential risks associated with AI technologies, emphasizing the importance of aligning regulations and policies to ensure safe and trustworthy development and use of artificial intelligence .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to analyzing an expert proposal for China's artificial intelligence law .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper proposes several new ideas, methods, and models related to artificial intelligence regulation and governance:

  • A concrete two-tier proposal for foundation models in the EU AI Act is suggested by Rishi Bommasani, Tatsunori Hashimoto, and others .
  • Foundation model transparency reports are introduced by Rishi Bommasani, Kevin Klyman, and their team .
  • An open robustness benchmark called Jailbreakbench for jailbreaking large language models is presented by Patrick Chao, Edoardo Debenedetti, and collaborators .
  • Harmbench, a standardized evaluation framework for automated red teaming and robust refusal, is introduced by Mantas Mazeika, Long Phan, and colleagues .
  • The paper also discusses the societal impact of open foundation models, as analyzed by Longpre, Ashwin Ramaswami, and others .
  • Additionally, the paper delves into the ethical and social risks of harm from language models, as explored by Laura Weidinger, John Mellor, and their team . The characteristics and advantages of the proposed methods in the paper compared to previous methods are as follows:
  1. Two-Tier Proposal for Foundation Models in the EU AI Act:

    • Characteristics: This proposal suggests a structured approach to regulating foundation models in the EU AI Act, providing clear guidelines for their development and deployment.
    • Advantages: By establishing a two-tier system, it allows for more nuanced regulation based on the potential risks and impacts of different types of AI models. This approach enhances transparency and accountability in AI governance.
  2. Foundation Model Transparency Reports:

    • Characteristics: These reports aim to increase transparency around the development and performance of foundation models, detailing aspects like data sources, training processes, and evaluation metrics.
    • Advantages: By providing standardized transparency reports, stakeholders can better understand the inner workings of AI models, fostering trust and enabling informed decision-making regarding their use.
  3. Jailbreakbench for Jailbreaking Large Language Models:

    • Characteristics: This open robustness benchmark focuses on evaluating the security and robustness of large language models through simulated attacks.
    • Advantages: Jailbreakbench offers a standardized framework for assessing the vulnerability of language models to adversarial inputs, helping researchers and developers enhance the security of AI systems.
  4. Harmbench for Automated Red Teaming and Robust Refusal:

    • Characteristics: Harmbench provides a systematic evaluation platform for testing the resilience of AI systems against malicious inputs and assessing their ability to reject harmful requests.
    • Advantages: By using Harmbench, developers can identify and address vulnerabilities in AI models, improving their capacity to withstand attacks and mitigate potential harms.
  5. Societal Impact Analysis of Open Foundation Models:

    • Characteristics: This analysis explores the broader societal implications of open foundation models, considering factors like accessibility, bias, and economic effects.
    • Advantages: By examining the societal impact of AI models, policymakers and stakeholders can make more informed decisions about their deployment, ensuring that AI technologies benefit society as a whole.
  6. Ethical and Social Risk Assessment of Language Models:

    • Characteristics: This assessment focuses on identifying and mitigating ethical and social risks associated with language models, such as misinformation propagation and harmful content generation.
    • Advantages: By proactively addressing ethical concerns and social risks, developers can design AI systems that prioritize safety, fairness, and responsible use, fostering a more ethical AI ecosystem.

Overall, the paper's proposed methods offer enhanced transparency, security, resilience, and ethical considerations compared to previous approaches, contributing to the responsible development and deployment of AI technologies.


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Could you please specify the topic or field you are referring to so I can provide you with more accurate information?


How were the experiments in the paper designed?

The experiments in the paper were designed through a systematic, bottom-up approach to construct an AI risk taxonomy grounded in public and private sector policies . The methodology involved collecting a diverse set of policies from eight government policies and 16 company policies, focusing on their relevance, comprehensiveness, and diversity . Each policy and regulation were analyzed using a consistent process to extract and organize risk categories explicitly referenced in each document, involving parsing every line, clustering related sections, identifying specific risks, and maintaining consistency while highlighting unique categories . The process also included a comparative analysis of risk categories across different policies and regulations to identify similarities and differences in how various entities and jurisdictions address similar risks .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation is called "Harmbench," which is a standardized evaluation framework for automated red teaming and robust refusal . The code for Harmbench is open source and can be accessed through the arXiv preprint arXiv:2402.04249 .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide valuable support for the scientific hypotheses that require verification. The study constructs a comprehensive risk taxonomy based on public and private sector policies related to the regulation of risky uses of generative AI models . By analyzing various government regulations and company policies, the research aims to create a more tractable framework for risk mitigation in the field of AI . The findings highlight substantial differences across companies and their policies in terms of prohibited risk categories, illustrating diverse conceptualizations of risks . Additionally, the study reveals that the union of risk categories in company policies is broader than that of existing government policies, indicating potential gaps in enforcement due to the lack of specificity in AI regulation .

Moreover, the research emphasizes the importance of considering initiatives from different jurisdictions to enhance the analysis of AI safety . By incorporating regulations and policies from the US, EU, and China, the study provides insights into the regulatory landscape faced by multinational companies and opportunities for global cooperation on AI safety . This comprehensive approach allows for a more nuanced understanding of the regulatory environment and potential areas for improvement in policies, regulations, and benchmarks related to AI safety .


What are the contributions of this paper?

The paper provides valuable contributions in the field of AI risk categorization and governance:

  • It introduces a standardized evaluation framework called Harmbench for automated red teaming and robust refusal .
  • It assesses the transparency of AI executive order implementation within 90 days .
  • It tracks the AI executive order by the numbers .
  • It discusses the acceptable use policy and terms of service of Meta and Microsoft related to AI .
  • It presents a risk-based tiered approach to governing general-purpose AI .
  • It analyzes an expert proposal for China's artificial intelligence law .
  • It evaluates the sociotechnical safety of generative AI systems .
  • It compares the governance of artificial intelligence in China and the European Union .
  • It provides insights into the societal impact of open foundation models .

What work can be continued in depth?

Work that can be continued in depth typically involves projects or tasks that require further analysis, research, or development. This could include in-depth research studies, complex problem-solving initiatives, detailed data analysis, comprehensive strategic planning, or thorough process improvement efforts. Essentially, any work that requires a deep dive into the subject matter, exploration of various angles, and a detailed examination of the factors involved can be continued in depth.

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