The AI Risk Repository

How to use the AI Risk Repository

Our Database is free to copy and use
The Causal and Domain Taxonomies can be used separately to filter this database to identify specific risks, for instance, risks occurring pre-deployment or post-deployment or related to Misinformation
The Causal and Domain Taxonomies can be used together to understand how each causal factor (i.e., entity, intention and timing) relate to each risk domain. For example, to identify the intentional and unintentional variations of Discrimination & toxicity
‍Offer feedback or suggest missing resources, or risks, here, or email airisk[at]mit.edu

We provide examples of use cases for some key audiences below.

How policymakers might use this tool

To understand the research and policy landscape.
For risk assessments to inform policy decisions.
As shared framework for discussing AI risks with other groups.
As a way to monitor emergent risks and ensure complete oversight.
To identify new, previously undocumented risks.
To prioritize and plan funding.

How risk evaluators might use this tool

To identify new, previously undocumented, risks.
To understand the risk landscape and curate or create related evaluations.
As a framework for discussing risks and potential evaluations with clients.
As a basis for developing specific risk determination criteria.
As a way to determine and communicate the scope of an audit.

How academics might use this tool

As a foundation for developing other classifications (e.g., the actions taken to address specific types of risks, or the actors involved in those risks).
To find underexplored areas of AI risk research.
To develop material for education and training.
To help validate where they have identified new, previously undocumented, risks.
To understand the landscape of existing research.

How industry might use this tool

To conduct internal risk assessments.
To identify new, previously undocumented, risks.
Evaluating risk exposure and developing risk mitigation strategies.
To develop research and training.

Frequently Asked Questions

How can I access the database without a Google account?

Please access via OneDrive. We will create a better formatted version or find a better solution in the future.

How did you create the AI Risk Repository?

We used a systematic search strategy, forwards and backwards searching, and expert consultation, to identify 56 AI risk classifications, frameworks, and taxonomies. We extracted 1000+ risks from these documents into a living AI risk database. We used the best fit framework synthesis approach to create our taxonomies.

This involved selecting an existing framework, coding extracted risks against it, then iteratively refining the framework through analysis of risks until we developed a comprehensive structure that could effectively categorize all relevant risks.

Which existing frameworks & documents did you include?

You can view the frameworks that were identified and extracted into the Repository:

As a slide deck, showing key figures or tables presenting the framework, and citation information for the document.
In the database, with metadata and extracted information for each document.

What can I do if I think there is a missing risk or resource?

Use this form to offer feedback, suggest resources or risks, or make contact.

You can also email pslat[at]mit.edu.

What are some limitations of the current AI Risk Repository?

The Repository has several limitations:

Limited to risks from 56 documents (although we screened >17,000 records after a systematic search of peer-reviewed and gray literature)
May be missing emerging, domain-specific risks, and unpublished risks.
Has potential for errors and subjective bias; we used a single expert reviewer for extraction and coding.
May include poorly communicated or unclear risks: we extracted risks as presented.
Our taxonomies prioritize clarity and simplicity over nuance.
Our taxonomies do not categorize risks by potentially important factors such as risk impact, likelihood, or discuss the interaction between risks.

See ourfor a full list and suggestions for future research.

Why do you have two taxonomies?

During this synthesis process, we realized that our database broadly contained two types of classification systems:

High-level categorizations of causes of AI risks (e.g., when or why risks from AI occur)
Mid-level hazards or harms from AI (e.g, AI is trained on limited data or used to make weapons)

Because these classification systems were so different, it was hard to unify them; high-level risk categories such as “Diffusion of responsibility” or “Humans create dangerous AI by mistake” do not map to narrower categories like “Misuse” or “Noisy Training Data,” or vice versa. We therefore decided to create two different classification systems that together would form our unified classification system.

Is this unique?

To the best of our knowledge, this is the first comprehensive review of AI risk frameworks and taxonomies which extracts their risks and releases that data for further adaptation and use.

Please let us know of anything that we may have missed.

What are some other databases of AI risks?

https://attack.mitre.org/
https://airisk.io/
https://avidml.org/#efforts
https://www.aitracker.org/#catalog-tabs

Please let us know of anything that we may have missed.

How do I cite the AI Risk Repository?

To reference our repository, you can cite our pre-print paper:

Slattery, P., Saeri, A. K., Grundy, E. A. C., Graham, J., Noetel, M., Uuk, R., Dao, J., Pour, S., Casper, S., & Thompson, N. (2024). A systematic evidence review and common frame of reference for the risks from artificial intelligence. https://doi.org/10.48550/arXiv.2408.12622

What are the risks from Artificial Intelligence?

A comprehensive living database of over 1000 AI risks categorized by their cause and risk domain

What is the AI Risk Repository?

How can I use the Repository?

AI Risk Database

Causal Taxonomy of AI Risks

Domain Taxonomy of AI Risks

How to use the AI Risk Repository

Frequently Asked Questions

Our Team

Acknowledgments