AI Safety Evaluation Calculator

Score AI model safety across harmful content, bias, hallucination, privacy, robustness, and transparency dimensions.

Harmful content score (0-100)

Bias & fairness score (0-100)

Factual accuracy score (0-100)

Privacy compliance score (0-100)

Adversarial robustness (0-100)

Transparency score (0-100)

Number of test cases

Red team hours

Tab to move between fields

Composite safety score

70.8

Risk level (1-5)

Deployment readiness (%)79.5%

Test coverage (%)99.9%

Weakest dimension score60

Total gap points180

Est. remediation hours90

Red team cost ($)$6,000.00

How to Use This Calculator

Score your model on each safety dimension from 0 to 100: Harmful Content, Bias & Fairness, Factual Accuracy, Privacy Compliance, Adversarial Robustness, and Transparency.
Enter Number of Test Cases in your red-team or automated evaluation suite and Red Team Hours planned.
Review the Composite Safety Score (0–100) and Risk Level (1–5) to benchmark against NIST AI RMF guidance.
Check Deployment Readiness (%) and Weakest Dimension to identify the highest-priority remediation area.
Use Estimated Remediation Hours and Red Team Cost to plan safety improvements before production deployment.

How the result changes with Harmful content score (0-100)

Harmful content score (0-100): How well the model resists generating harmful, violent, or illegal content. 100 = fully safe.
Bias & fairness score (0-100): Score for demographic fairness and absence of stereotyping across protected groups.
Factual accuracy score (0-100): Resistance to hallucination and fabrication. 100 = always factually grounded.
Privacy compliance score (0-100): Resistance to PII leakage and compliance with data protection regulations.
Adversarial robustness (0-100): Resistance to prompt injection, jailbreaking, and adversarial inputs.
Transparency score (0-100): Quality of model documentation, explainability, and uncertainty communication.
Number of test cases: Total evaluation test cases in your red-team/safety test suite.
Red team hours: Hours of manual red-team evaluation planned or completed.

Composite safety score: Weighted safety score (0-100) across all dimensions, using NIST AI RMF-inspired weights.
Risk level (1-5): 1=Minimal, 2=Low, 3=Medium, 4=High, 5=Critical risk classification.
Deployment readiness (%): Combined safety score and test coverage readiness metric.
Test coverage (%): Statistical estimate of defect detection coverage from test suite size.
Weakest dimension score: Lowest individual safety dimension score — your biggest vulnerability.
Total gap points: Sum of all dimension gaps from 100. Higher = more remediation needed.
Est. remediation hours: Estimated engineering hours to close safety gaps (~0.5 hrs per gap point).
Red team cost ($): Cost of red team evaluation at $150/hour industry average.

Identify Input Parameters
4 parameters
Harmful content score (0-100) = 80, Bias & fairness score (0-100) = 70, Factual accuracy score (0-100) = 60, Privacy compliance score (0-100) = 75 = 8 input(s) provided
Calculate Composite safety score
Composite safety score = harmfulContentScore * weights.harmful +
70.8 = 70.8
Calculate Risk level
Risk level
3 = 3
Calculate Deployment readiness
Deployment readiness = min(100, compositeSafetyScore * 0.7 + min(100, coverageEstimate) * 0.3)
79.5 = 79.5%
Calculate Test coverage
99.9 = 99.9%