MIT Unveils AI Risk Database to Evaluate Innovation Threats

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by Giorgi Kostiuk

2 years ago

Made with AI

  1. AI Risk Documentation
  2. Seven Categories of AI Risks
  3. Utilizing the Database


    4. MIT researchers and other organizations have produced the AI Risk Repository — a broad database of documented risks compounded by AI systems.

    AI Risk Documentation

    The repository aims to assist decision-makers in various institutions such as government, research, businesses, and industry to assess the emerging risks associated with AI, despite its transformative abilities. The repository incorporates information from 43 existing taxonomies such as peer-reviewed articles, preprints, conference papers, and reports. This helps in understanding the situations and mechanisms through which AI risks can emerge, thanks to a two-pronged classification system.

    “We wanted a fully comprehensive overview of AI risks to use as a checklist,”— Peter Slattery, incoming postdoc at MIT FutureTech and project lead.

    Seven Categories of AI Risks

    Risks are classified based on their causes, taking into consideration the entity liable (human or AI), the intent (unintentional or intentional), and the timing (post-development or pre-deployment). Additionally, risks are divided into seven different domains, including misinformation and malicious actors, misuse, discrimination and toxicity, privacy, and security.

    “But when we looked at the literature, we found that existing risk classifications were like pieces of a jigsaw puzzle: individually interesting and useful, but incomplete,”— Slattery.

    Utilizing the Database

    The repository is publicly available and can be leveraged by various institutions for risk assessment and mitigation. For example, an organization developing an AI-powered hiring system can use the repository to identify potential risks associated with discrimination and bias. Similarly, a firm using AI for content moderation can use the 'misinformation' domain to understand potential risks related to AI-generated content. According to the MIT team, the repository helps identify potential gaps or imbalances in how organizations address risks.

    MIT researchers plan to frequently update the database with new risks, latest findings, and evolving trends to keep it relevant and useful. This tool is intended to be an essential resource for businesses and research institutions that understand the importance of managing AI risks.

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