security compliancealgorithmic biaseu ai actmodel auditmodel cards
Complete evaluation framework
What to assess and how to score it
Review the evidence signals before interviewing. Then use the anchored descriptions—not instinct alone—to choose the score that best matches each answer.
01
Evaluation factor
Technical depth
35% weight
Check fluency in fairness metrics (equalized odds, demographic parity), explainability tooling such as SHAP or LIME, and frameworks like NIST AI RMF or ISO 42001 clauses.
Evidence to listen for
Command of the specific attack surface, tooling, and controls the role covers
Understands how the underlying system works, not just how the tool reports on it
Can explain an attack or control chain end to end
Distinguishes what they found themselves from what a scanner flagged
Five-point scoring guide
1
Poor
Tool operator only; no understanding of the systems underneath.
2
Needs Improvement
Runs tooling but cannot explain findings or how the attack works.
3
Satisfactory
Solid working knowledge; depth thins outside familiar tooling.
4
Very Good
Strong command of the domain; explains attack and control chains clearly.
5
Excellent
Names specific metrics and thresholds used, explains where SHAP misleads, and maps controls to NIST AI RMF or ISO 42001 requirements.
02
Evaluation factor
Real incidents and findings
30% weight
Probe actual audits performed: model type, data lineage reviewed, findings logged, and whether a system was blocked, retrained, or shipped with documented caveats.
Evidence to listen for
Brings specific incidents, findings, or audits they personally worked
States their own role rather than the team's
Describes what was actually at risk and what changed afterwards
Can talk about a finding that turned out to be wrong
Five-point scoring guide
1
Poor
No hands-on work; knowledge is entirely certification or coursework.
2
Needs Improvement
Limited exposure; cannot describe their contribution to an incident.
3
Satisfactory
Real casework with adequate detail; ownership sometimes vague.
4
Very Good
Specific incidents with clear personal scope and what changed after.
5
Excellent
Walks through named audits end to end, citing disparity numbers found, drift detected, and the concrete disposition each finding received.
03
Evaluation factor
Risk judgement
20% weight
Assess how they rank harms: proxy discrimination in credit or hiring models, hallucination in customer-facing LLMs, versus cosmetic documentation gaps under EU AI Act tiers.
Evidence to listen for
Prioritises by actual exploitability and business impact, not raw severity scores
Can argue for accepting a risk as well as fixing it
Knows the difference between a finding and a problem
Does not cry wolf or wave things through
Five-point scoring guide
1
Poor
Treats every finding as critical, or waves real risk through.
2
Needs Improvement
Follows severity scores mechanically; no business context.
3
Satisfactory
Reasonable prioritisation; less confident arguing for risk acceptance.
4
Very Good
Prioritises by exploitability and impact; can justify accepting a risk.
5
Excellent
Separates high-impact harms from paperwork issues, justifies severity with affected population size, exposure, and reversibility of the decision.
04
Evaluation factor
Getting things fixed
15% weight
Test their record moving data scientists and product owners to act: reopened model cards, added guardrails, retraining schedules, sign-off gates in the MLOps pipeline.
Evidence to listen for
Writes findings engineers can act on rather than a wall of output
Has persuaded a team to fix something they did not want to fix
Explains risk to executives in business terms
Works with the org rather than policing it
Five-point scoring guide
1
Poor
Adversarial with engineering; findings never get fixed.
2
Needs Improvement
Reports are unactionable; no influence beyond raising tickets.
3
Satisfactory
Adequate reporting; relies on mandate rather than persuasion.
4
Very Good
Actionable findings and a real record of getting fixes shipped.
5
Excellent
Describes findings that changed deployment practice, naming the owner engaged, the control added, and how closure was verified post-release.
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