AI STRATEGY
Operationalize AI Governance from Day One
Prove Your AI Is Fair and Safe
Bias and safety audits aren’t optional—they’re essential. Regular reviews across key demographic, behavioral, and contextual factors help catch and correct systemic risks.
Why it's Important
Detects and reduces discriminatory model behavior
Protects vulnerable user groups
Builds a foundation of fairness and inclusion
Supports legal and ethical compliance
Maintains reputation and public trust
How to Implement
Select audit dimensions (e.g., race, gender, geography, device)
Design representative test cases or use synthetic proxies
Analyze model behavior across slices (e.g., score gaps, outcome fairness)
Log and prioritize findings by risk level
Create an internal response or remediation plan
Share summary results with stakeholders
Repeat audits regularly or at key milestones
Available Workshops
Fairness Risk Brainstorm
Model Behavior Gap Analysis
Audit Coverage Mapping
Bias Impact Estimation Exercise
Synthetic User Persona Review
Ethical Case Study Walkthrough
Deliverables
Bias/safety audit protocol
Audit dataset (test cases and expected behaviors)
Audit report with key findings
Remediation backlog and status board
Stakeholder communication brief
How to Measure
% of known bias dimensions tested per cycle
Gaps in outcome or quality scores across groups
Number of risks remediated vs. outstanding
Audit frequency and velocity
Time-to-remediation for severe findings
Change in fairness metrics between model versions
Pro Tips
Share audit findings during sprint planning
Build an audit calendar into product OKRs
Visualize gaps and deltas with heatmaps
Invite third-party reviewers for major audits
Turn audit themes into ethics training modules
Consider 3rd party audit to provide more transparency
Get It Right
Involve diverse teams in audit design
Validate test cases with real or representative users
Document assumptions and known limitations
Align audits with external standards (e.g., IEEE, ISO)
Use audits to inform roadmap—not just cleanup
Don't Make These Mistakes
Treating audits as one-time compliance tasks
Ignoring intersectionality (e.g., race + gender)
Using biased benchmarks or labels
Failing to disclose audit results to leadership
Waiting until after launch to test fairness