Responsible AI in Practice: Governance Developers Can Follow
Responsible AI & Governance: leave with a model/system card, risk assessment mapped to NIST AI RMF and an incident-reporting process.
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- Bring your own work — code, campaign, report or plan
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What this session would cover
Proposed outline — the mentor finalises the agenda once this topic is scheduled.
- 1Why responsible ai & governance matters — the common problem: AI systems launch without documentation of limits, risks or who is accountable.
- 2Core concepts in plain language: Fairness, privacy, transparency, explainability, accountability, human oversight
- 3Going further: risk assessment, model documentation, data provenance, content provenance, evaluation records, incident reporting
- 4Framework walkthrough: NIST AI Risk Management Framework, OWASP Top 10 for LLM Applications, Model Cards
- 5Practical workflow, built live: A model/system card, risk assessment mapped to NIST AI RMF and an incident-reporting process.
- 6How to measure it: Regression-eval pass rate, Drift alerts, Security findings, Incident count
- 7An illustrative case (a fictional example, not a client result), then live Q&A on your own situation
Who it's for
- • Students and freshers entering tech
- • Working developers and engineers
- • Tech leads and architects
You'd leave with
- A model/system card, risk assessment mapped to NIST AI RMF and an incident-reporting process.
- A working understanding of NIST AI Risk Management Framework and OWASP Top 10 for LLM Applications
- A short list of measures to track: Regression-eval pass rate, Drift alerts, Security findings
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