MAS FEAT AI Governance Compliance & Audit Trace Synthesizer with Mastra AI
Construct an automated AI governance and audit compliance agent using Mastra AI to evaluate enterprise model deployment outputs. Audit AI decisions against MAS FEAT principles (Fairness, Ethics, Accountability, Transparency) and generate audit records.
What you are building
The core problem, expected build, and operating context for this challenge.
Deploy Mastra AI workflows to systematically test, audit, and log model decisions against Monetary Authority of Singapore FEAT AI governance principles.
How work is evaluated
Evaluates detection of fairness bias and generation of FEAT audit reports.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
bias_correctly_flagged
Ensures model bias below 0.80 ratio is flagged as non-compliant
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
ratio_accuracy
Accuracy of mathematical disparate impact calculation • target: 1 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Master TypeScript agent workflow orchestration using Mastra AI framework primitives
Implement FEAT alignment evaluators measuring bias metrics across demographic attributes
Build dynamic memory and RAG stores in Mastra AI to track long-term model audit logs
Export standardized regulator-ready compliance artifacts with cryptographic hash verification
Reference links and supporting material
Synthetic batch prediction dataset across 500 simulated loan decisions with group attributes.
How this agent runs
Evaluates detection of fairness bias and generation of FEAT audit reports.
Challenge input
JSON object containing model_prediction_batch with protected_attributes
Mastra AI
Provides typed workflows, vector memory, and observability for TypeScript.
Evaluated output
JSON object with feat_compliant (boolean), disparate_impact_ratio, and violation_summary
- Ensures model bias below 0.80 ratio is flagged as non-compliant
- Accuracy of mathematical disparate impact calculation • target: 1 • range: 0-1
- Benchmark: AI Governance & Fairness Benchmark
- Ratio Accuracy target: 1
- 1 public reference case
- JavaScript execution harness
- javascript sandbox (unavailable on Versalist)
View technical recipe
Configured tools
- Mastra AI · Required
- aiXplain · Optional
- Banani · Optional
- Mastra AI · Required
Evaluation contract
- bias_correctly_flagged · Weight 1
- ratio_accuracy · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.