security compliancealgorithmic fairnesseu ai actmodel auditingresponsible ai
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 command of fairness metrics they have actually computed: demographic parity, equalised odds, disparate impact ratios, plus tooling such as Fairlearn, AIF360, SHAP, or model cards.
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 used on real models, explains why one was chosen over another, and knows where each metric breaks down.
02
Evaluation factor
Real incidents and findings
30% weight
Probe concrete audits they ran: which model, what bias surfaced, how it was measured across subgroups, and what the documented finding or impact assessment said.
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 a named audit end to end, including proxy variables found, subgroup sample sizes, and the remediation actually recommended.
03
Evaluation factor
Risk judgement
20% weight
Assess how they weigh accuracy loss against harm reduction, handle EU AI Act high-risk classification, NIST AI RMF, GDPR Article 22, and sector rules like ECOA or EEOC.
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
Ranks harms by severity and affected population, cites the governing regulation, and defends a trade-off with reasoning rather than blanket prohibition.
04
Evaluation factor
Getting things fixed
15% weight
Test how they moved engineering and product teams to act: reweighting, dataset rebalancing, threshold changes, blocked launches, or governance gates added to the release process.
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 a launch they delayed or changed, names who pushed back, and shows the retraining or gate that persisted afterwards.
Put this rubric to work
Score every candidate against the same standard
Add these weighted factors to Hirevire and let AI evaluate recorded answers against your rubric.
