AI RISK REPORT
RiskRAG: A Data-Driven Solution for Improved AI Model Risk Reporting
RiskRAG addresses the critical need for improved AI model risk reporting. Existing model cards often lack actionable insights and context-specific risks. RiskRAG, a Retrieval Augmented Generation (RAG) based solution, leverages 450K model cards and 600 real-world incidents to pre-populate contextualized risk reports. Studies show developers prefer RiskRAG over standard model cards, as it better meets design requirements for identifying diverse, prioritized, contextualized, and actionable risks. It encourages more careful decision-making in AI model selection, improving transparency and responsible AI adoption.
Executive Impact: Key Metrics from RiskRAG Implementation
Our data-driven approach yields significant improvements in AI model risk reporting, fostering responsible AI development and deployment.
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Model Cards Analyzed
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Unique Risk Sections
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Real-World Incidents
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Developer Preference
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
AI Risk Documentation
Tools for Populating AI Risk Documentation
RiskRAG Methodology
AI Risk Documentation
Explores various formats and standards proposed for documenting AI model risks, including model cards and risk cards, and highlights their shortcomings in contextualization and actionability.
86%
of model cards omit risk discussions, or provide generic, unactionable content.
| Feature | Model Cards (Traditional) | RiskRAG (Our Solution) |
|---|---|---|
| Model-specific risks | Partially |
|
| Structured risks | No |
|
| Contextualized to uses | Partially |
|
| Mitigation strategies | Generic |
|
| Prioritization of risks | No |
|
| Real-world harm focus | Limited |
|
Tools for Populating AI Risk Documentation
Examines automated tools designed to assist practitioners in identifying potential uses, risks, and harms associated with AI systems, evaluating their ability to generate model-specific and actionable insights.
96%
of model cards with risk content copy from a small set, indicating lack of unique, specific insights.
RiskRAG's Data-Driven Approach
RiskRAG leverages a vast dataset of 450,000 model cards and 600 real-world AI incidents. This data-driven strategy ensures that identified risks are relevant and grounded in actual deployment scenarios, avoiding the hallucinations and generic outputs often seen with pure LLM-based tools. By combining retrieval-based and generation-based methods, RiskRAG ensures comprehensive, contextualized, and actionable risk reporting, directly addressing the limitations of existing solutions that fail to provide model-specific risks or mitigation strategies.
RiskRAG Methodology
Details the architecture and data-driven approach of RiskRAG, including its use of Retrieval Augmented Generation (RAG) to generate model-specific, contextualized, and prioritized risk reports with actionable mitigation strategies.
Enterprise Process Flow
Model Description Input
→
Retrieve Similar Risks & Incidents
→
Generate Structured Risks
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Map Risks to Real-World Uses
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Retrieve & Map Mitigations
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Prioritize Risks & Mitigations
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Output Actionable Risk Report
74%
of developers preferred RiskRAG over standard model cards for high-risk hiring application tasks.
Calculate Your Potential ROI with RiskRAG
Estimate the efficiency gains and cost savings your enterprise could achieve by streamlining AI risk reporting.
Annual Cost Savings
Annual Hours Reclaimed
Your RiskRAG Implementation Roadmap
A phased approach to integrate RiskRAG and transform your AI risk reporting capabilities.
Phase 1: Discovery & Integration
Initial assessment of existing AI models, data sources, and integration points. Setup of RiskRAG within your infrastructure.
Phase 2: Customization & Training
Tailoring RiskRAG to your specific organizational taxonomy and risk frameworks. Fine-tuning models with proprietary incident data.
Phase 3: Pilot & Feedback Loop
Deployment of RiskRAG in a pilot program with a subset of models. Gathering developer feedback for iterative refinement.
Phase 4: Full-Scale Deployment
Broad implementation across all relevant AI models. Establishing continuous monitoring and update processes.
Ready to Transform Your AI Risk Reporting?
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