AI Risk Collection (AIRC)

Objective:
AIRC is dedicated to collecting, curating, and categorizing real-world examples of AI risks that emerge during research and deployment. The project aims to serve as a knowledge base to inform AI safety research, policy, and best practices, while facilitating the development of AI risk mitigation strategies.

Key Goals:

1. Data Collection & Curation: Identify and collect detailed AI risk examples from real-world applications across diverse sectors (e.g., healthcare, finance, autonomous systems).
2. Risk Categorization: Systematically categorize risks based on the nature of failure, harm, or unintended outcomes.
3. Risk Prediction & Prevention: Create a repository of case studies that can help developers and regulators foresee potential risks during AI system development and deployment.
4. Community Collaboration: Encourage contributions from researchers, engineers, policymakers, and stakeholders for a robust, multi-disciplinary understanding of AI risks.
5. Practical Solutions & Best Practices: Develop guidelines for the safe deployment of AI systems, addressing specific risks based on collected examples.

Architecture:

1. Data Collection and Ingestion:

   • Sources of Data: Open-source research, industry case studies, user-generated reports, academic papers, and regulatory bodies.
   • Automated Ingestion Pipelines: Utilize NLP models to scrape relevant information from papers, reports, and media mentions related to AI risks.
   • Crowdsourced Reporting Mechanism: A platform that allows industry experts and users to submit risk cases, ensuring anonymity and structured data submission for consistency.

2. Risk Categorization Engine:

   • Taxonomy of Risks: Define and categorize risks into types, including:
     • Algorithmic bias
     • Model failure or degradation
     • Unsafe autonomy
     • Vulnerability to adversarial attacks
     • Unintended social, economic, or ethical consequences
   • Severity Levels: Assign severity scores based on potential or actual harm (e.g., minor, moderate, critical).
   • Sector-Specific Risks: Tailor taxonomies to specific domains such as healthcare, transportation, finance, defense, etc.

3. Case Study Repository:

   • Searchable Database: Make the collection of risk examples accessible through a structured database, allowing users to search by risk type, severity, domain, and mitigation strategies.
   • Versioned Risk Examples: Maintain a history of how a particular risk evolved or was mitigated over time (especially useful for AI systems that update frequently).
   • User Feedback Loop: Include feedback mechanisms where users can rate the quality of risk examples or suggest additional details and references.

4. Risk Analytics Dashboard:

   • Visual Insights: Provide dashboards to visualize AI risk trends across sectors, highlighting common patterns, emerging risks, and historical data.
   • Heatmaps & Risk Scores: Show a heatmap of common risk hotspots by sector or AI model type.
   • Mitigation Tracking: Visualize how mitigation strategies have affected risk profiles over time.

5. Community Collaboration & Incentives:

   • Open Contributions: Allow researchers and developers to contribute AI risk examples and suggested mitigations.
   • Research Partnerships: Collaborate with research institutions, AI companies, and regulators to validate and expand the dataset.
   • Incentives for Contributions: Offer recognition or rewards (e.g., citations, co-authorship) for valuable contributions.

6. Publication & Knowledge Sharing:

   • Annual Report: Publish yearly reports summarizing key findings, trends, and recommendations for policymakers and industry players.
   • Workshops & Conferences: Host events to discuss new AI risks, present case studies, and share research findings.
   • API for Risk Data: Offer an API to integrate the risk collection into AI development platforms, enabling developers to query risk examples as they design systems.

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Concrete Examples of AI Risks for AIRC:

1. Example 1: Bias in Predictive Policing Algorithms

   • Sector: Law Enforcement
   • Risk Type: Algorithmic Bias
   • Description: Predictive policing tools were found to disproportionately target minority communities due to biased training data, leading to over-policing in certain neighborhoods.
   • Impact: Legal ramifications, social unrest, and loss of trust in law enforcement.
   • Mitigation Strategy: Implement fairness auditing and retraining on more diverse datasets; ongoing community oversight.

2. Example 2: Adversarial Attack on Autonomous Vehicles

   • Sector: Transportation
   • Risk Type: Vulnerability to Adversarial Attacks
   • Description: An autonomous vehicle was fooled by small perturbations to street signs, causing it to misread a stop sign as a yield sign.
   • Impact: Increased risk of accidents and safety failures.
   • Mitigation Strategy: Strengthen adversarial defense mechanisms by regularly testing models against adversarial examples.

3. Example 3: Financial Algorithm Flash Crash

   • Sector: Finance
   • Risk Type: Model Degradation
   • Description: A high-frequency trading algorithm malfunctioned due to unexpected market conditions, leading to a flash crash and wiping out billions of dollars in value within minutes.
   • Impact: Economic instability and regulatory concerns.
   • Mitigation Strategy: Real-time monitoring and back-testing of AI models under simulated extreme market conditions.

4. Example 4: Misdiagnosis in AI-based Healthcare Diagnostics

   • Sector: Healthcare
   • Risk Type: Model Failure
   • Description: An AI diagnostic tool incorrectly identified benign tumors as malignant due to a flawed training dataset.
   • Impact: Patient harm and loss of trust in AI-based medical tools.
   • Mitigation Strategy: Use a multi-layered verification system, combining AI suggestions with human expertise, and maintain diverse and comprehensive training data.

5. Example 5: AI Misinformation Propagation

   • Sector: Media & Social Platforms
   • Risk Type: Unintended Social Consequences
   • Description: An AI model for content recommendation on a social media platform amplified misinformation, contributing to societal polarization.
   • Impact: Harmful social division, disinformation, and ethical concerns.
   • Mitigation Strategy: Introduce human moderation and implement content verification tools to reduce the spread of false information.

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