Scientific software audits: key technology in research 🚀
Scientific code audits verify formulas, data flow, and models (including AI) to ensure reproducible and safe results. 🔬🧪✅
- 🔢 Verification of formulas, constants, and units
- 📊 Data validation and reproducibility of ML training
- 🛠️ Static analysis, testing, and performance optimization
- 🔒 Security and traceability for compliance and trust
Some ways to validate that an AI does not induce bias
- Data audit: review representativeness and distributions across sensitive groups.
- Labeling review: check consistency and diversity of labelers.
- Subgroup evaluation: measure performance separately by sex, age, race, etc.
- Fairness metrics: calculate parity, equalized odds, predictive parity, etc.
- Counterfactual tests: change sensitive attributes and observe effects on outputs.
- Explainability: use SHAP/LIME to identify feature contributions.
- Adversarial / stress testing: edge inputs and rare cases.
- Preprocessing: resampling, reweighting, or class balancing.
- Postprocessing adjustments: calibration or group-based correction.
- Fairness-aware models: use algorithms that incorporate fairness constraints.
- Expert review and external audit: human judgment and domain-driven validation.
- Monitoring in production: detect drift and emerging biases with alerts.
In short#
It’s like reviewing a lab recipe step by step: confirming measurements, ingredients (data), and procedures (algorithms) are correct so the experiment is reliable; if AI is involved, you also check that the data don’t induce bias. 👩🔬🔍
Also published on LinkedIn.
