AI Safety & Alignment
Building AI that is not just capable but trustworthy, fair, and explainable
Session Topics
AI Safety Fundamentals
Specification gaming, reward hacking, goal misgeneralisation — why capable AI systems can fail in unexpected and dangerous ways.
Bias Detection & Mitigation
Sources of bias in training data, evaluation data, and deployment context. Statistical parity, equalised odds, and calibration metrics.
Explainability & Interpretability
LIME, SHAP, attention visualisation, and chain-of-thought as an explainability tool. When black-box models are acceptable and when they're not.
Human-in-the-Loop Patterns
When to require human review, how to design effective human-AI workflows, and avoiding automation bias.
AI Governance & Regulation
The EU AI Act, NIST AI RMF, and practical compliance considerations for AI systems in hiring, healthcare, and finance.
Audit Your Hiring AI for Safety & Bias
Design comprehensive safety and fairness testing for your hiring AI system. Identify potential biases across demographic groups, create tests to catch them, plan monitoring for bias drift, and ensure your system is both trustworthy and legally compliant.
A safety and bias audit report with identified risks, test cases, mitigation strategies, and ongoing monitoring plan.
Key Takeaways
- Bias in → bias out: the data you use reflects and amplifies societal patterns
- Explainability is a legal requirement in regulated domains like hiring and lending
- Human-in-the-loop is not a crutch — it is a deliberate design choice
- Safety is not a feature you add later; it must be designed in from the start