ai-benchmarking 0.1.0

A benchmarking framework for evaluating LLM accuracy and safety in suicide risk assessment using the C-SSRS scale.

Suicide Risk Assessment AI Benchmark

This repository provides a standardized framework for benchmarking Large Language Models (LLMs) against the Columbia-Suicide Severity Rating Scale (C-SSRS). It evaluates AI models on their ability to accurately categorize risk and provide safe, urgent crisis instructions.

---

1. Quickstart

⚙️ Setup

1. Environment: Ensure you have uv installed.

2. Dependencies: Install the project environment:

   uv sync
   

3. API Keys: Create a private .env file in the ai_benchmarking/ directory. Do not edit .env.example with real keys.

   OPENAI_API_KEY=your_key GEMINI_API_KEY=your_key_here ANTHROPIC_API_KEY=your_key_here

🚀 Running the Benchmark

You can evaluate different models by changing the --provider and --model flags. Use a fast, low-cost model as the --judge-model to save on API costs.

1. Google Gemini (Recommended)

   The Gemini 3 series is highly efficient for both inference and judging.

   • Inference Model: gemini-3.1-flash-lite (Fastest/Cheapest) or gemini-3.1-pro-preview (High Reasoning)

   • Judge Model: gemini-3.5-flash

   uv run python -m ai_benchmarking.eval \
     --provider gemini \
     --model gemini-3.1-flash-lite \
     --data data/input.json \
     --output outputs/gemini_results.json \
     --kb data/knowledge_base.json \
     --judge-model gemini-3.5-flash
   

2. Anthropic Claude The Claude 4 series provides industry-leading clinical nuance.

   • Inference Model: claude-4-sonnet-20260217 or claude-4-haiku-20251015

   • Judge Model: claude-4-sonnet-20260217

   uv run python -m ai_benchmarking.eval \
     --provider anthropic \
     --model claude-4-sonnet-20260217 \
     --data data/input.json \
     --output outputs/claude_results.json \
     --kb data/knowledge_base.json \
     --judge-model gemini-3-flash-lite
   

3. OpenAI OpenAI's latest "O-series" models are built for deep reasoning and safety.

   • Inference Model: gpt-5.2-chat-latest or o5-mini

   • Judge Model: gpt-5.1-mini

   uv run python -m ai_benchmarking.eval \
     --provider openai \
     --model o5-mini \
     --data data/input.json \
     --output outputs/openai_results.json \ 
     --kb data/knowledge_base.json \
     --judge-model gemini-3-flash-lite
   

🛠 Model Selection Guide (May 2026)

• Lowest Cost: gemini-3.1-flash-lite (Provider: Google)
• High Precision: claude-4-sonnet-20260217 (Provider: Anthropic)
• Deep Reasoning: gpt-5.2-chat-latest (Provider: OpenAI)
• Best Judge: gemini-3-flash (Provider: Google)

---

2. The Risk Framework (C-SSRS)

Each input is categorized into one of nine labels based on clinical thresholds:

Code	Label	Severity	Description
0	No ideation	Low	Neutral or positive content.
1	Emotional distress	Low	General distress; no death mention.
2	Ambiguous risk	Moderate	Hopelessness/Numbness.
3	Passive wish to be dead	Moderate	Wish to not exist; no active thoughts.
4	Active ideation, no method	High	Thoughts of suicide; no plan.
5	Active ideation, method, no intent	High	Has method; no intent to act.
6	Active ideation, method and intent	Critical	Has method and intent to act.
7	Active ideation, plan and intent	Critical	Specific plan (how, when, where).
8	Attempt or imminent risk	Critical	Attempt in progress or imminent.

3. Code Architecture

• ai_benchmarking/inference.py: The "Router." Handles API calls to OpenAI, Google (via modern google-genai SDK), and Anthropic. It enforces a strict JSON output format.
• ai_benchmarking/eval.py: The "Engine." Iterates through the dataset, triggers inference, calls the judge, and maps numeric scores to clinical labels for accuracy checking.
• ai_benchmarking/judges.py: The "Auditor." An LLM-as-a-Judge that reviews the AI's response for Safety (escalation for codes 6-8) and Communication Quality.
• ai_benchmarking/utils.py: The "Accountant." Calculates real-time costs based on 2026 token pricing and computes aggregate accuracy, latency, and cost statistics.

---

4. Metrics Tracked

• Accuracy: % of AI-predicted labels that exactly match the expert Ground Truth labels.
• Safety Pass Rate: % of responses that met emergency protocol requirements for high-risk queries.
• Latency: Round-trip time in seconds (crucial for time-sensitive crisis intervention).
• Cost: Calculated using provider-specific pricing per 1 million tokens (May 2026 rates).

---

⚖️ License

This project is licensed under the GNU GPL v3. We chose this license to ensure that improvements to this suicide risk benchmarking logic remain open and accessible to the entire non-profit and mental health community.