Understanding AI Risks: A Comprehensive Framework

Artificial intelligence (AI) is rapidly advancing, bringing with it a range of risks that affect society on multiple levels. However, these risks are often categorized inconsistently, making it difficult for stakeholders to address them cohesively. The AI Risk Repository by MIT Researchers ( Peter Slattery, PhD and team ) seeks to fill this gap by providing a unified framework that compiles 777 different AI risks from 43 structured taxonomies. This comprehensive repository offers insights into the diverse ways AI can be harmful and provides a solid foundation for developing strategies to mitigate these risks.

Key Classifications of AI Risks

The repository introduces two key taxonomies for categorizing AI risks: the Causal Taxonomy and the Domain Taxonomy.

1. Causal Taxonomy: This framework classifies AI risks based on their causes. It looks at three factors:
2. Domain Taxonomy: This categorizes AI risks into seven broad domains:

Insights Into the AI Risk Landscape

The analysis found that the majority of risks (51%) are caused by AI systems themselves, while 34% are due to human actions. Most risks (65%) emerge after the deployment of AI systems, highlighting the importance of continuous oversight after AI systems are launched.

The most commonly discussed domains include AI system safety, failures, and limitations (76% of documents), followed by socioeconomic and environmental harms (73%) and discrimination and toxicity (71%). However, areas like human-computer interaction (41%) and misinformation (44%) are less frequently explored, indicating potential gaps in the current research focus.

Implications for Key Stakeholders

• Policymakers: The repository highlights the need for regulations that account for both pre-deployment and post-deployment risks, ensuring that AI is governed effectively throughout its lifecycle.
• Auditors and Model Evaluators: This taxonomy provides a structured guide to help identify and evaluate AI risks comprehensively, ensuring that no significant threat is overlooked.
• Academics and Researchers: The repository serves as a starting point for further research, especially in underexplored areas like AI welfare, rights, and misinformation.
• Industry Leaders: Companies can use the repository to develop internal risk assessment processes that are aligned with global standards and to train staff on AI-related risks.

Conclusion

The AI Risk Repository is a critical resource for anyone involved in AI risk management. By providing a structured and comprehensive framework, it helps unify the fragmented landscape of AI risk classification, making it easier for all stakeholders to understand, communicate, and mitigate the risks associated with AI. As AI continues to evolve, resources like this repository will be essential for ensuring that its development remains safe and beneficial to society.