Interview scorecard template

AI Ethicist interview scorecard

Evaluate AI Ethicist candidates across four weighted areas: theoretical command, from theory to hardware or code, research judgement, and explaining it to non-specialists. Theoretical command leads at 35%, so probe command of fairness metrics (demographic parity, equalised odds), value alignment literature, and instruments like the EU AI Act, NIST AI. Use the rubric to compare role-specific evidence consistently.

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TL;DR
For theoretical command, look for evidence the candidate names specific fairness definitions, explains where they mathematically conflict, and cites concrete AI Act risk tiers or RMF functions accurately. For from theory to hardware or code, look for evidence the candidate shows artefacts they authored, such as a completed FRIA, eval suite, or audit that changed a launch decision or model card. Apply the written 1–5 anchors to every answer, record the evidence behind each rating, and use the factor weights to reach a consistent overall assessment.
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

Theoretical command

35% weight

Probe command of fairness metrics (demographic parity, equalised odds), value alignment literature, and instruments like the EU AI Act, NIST AI RMF, and OECD principles.

Evidence to listen for

  • Explains the underlying theory at the level the role demands, and can go a layer deeper when pushed
  • Knows which results are established and which are contested
  • Distinguishes their own contribution from the field's
  • Comfortable saying where the theory runs out

Five-point scoring guide

1
Poor

Recites terminology without understanding; cannot go one layer deeper.

2
Needs Improvement

Surface familiarity; conflates established results with speculation.

3
Satisfactory

Solid grasp of the core theory; thin at the frontier.

4
Very Good

Strong command; separates settled results from open questions.

5
Excellent

Names specific fairness definitions, explains where they mathematically conflict, and cites concrete AI Act risk tiers or RMF functions accurately.

02
Evaluation factor

From theory to hardware or code

30% weight

Ask what they built or shipped: model cards, red-team protocols, bias audits in Fairlearn or AIF360, dataset documentation, impact assessments tied to a real model release.

Evidence to listen for

  • Has built, simulated, or run something real, not only published about it
  • Knows the gap between the idealised model and the actual apparatus or system
  • Names the practical constraint that dominates in real conditions
  • Can describe a result that did not match prediction

Five-point scoring guide

1
Poor

Purely theoretical; no contact with implementation.

2
Needs Improvement

Some exposure but unaware of practical constraints.

3
Satisfactory

Has implemented work; understands the main real-world limits.

4
Very Good

Strong practical record; articulate about theory-versus-reality gaps.

5
Excellent

Shows artefacts they authored, such as a completed FRIA, eval suite, or audit that changed a launch decision or model card.

03
Evaluation factor

Research judgement

20% weight

Test how they choose which harms to chase when evidence is thin: sampling a harm taxonomy, scoping red-teaming, deciding when a system should not be deployed.

Evidence to listen for

  • Chooses problems by tractability and value, not novelty alone
  • Knows when to abandon a line of work
  • Reads and evaluates others' results critically
  • Can say what would falsify their own approach

Five-point scoring guide

1
Poor

Chases novelty; no sense of tractability or when to stop.

2
Needs Improvement

Weak problem selection; persists past the point of value.

3
Satisfactory

Reasonable judgement within a defined programme.

4
Very Good

Selects problems well and knows when to abandon a line.

5
Excellent

Prioritises harms by severity and reversibility, admits open questions, and describes a case where they recommended halting or restricting deployment.

04
Evaluation factor

Explaining it to non-specialists

15% weight

Judge whether they can move engineers, legal counsel, and executives without jargon: policy memos, ethics review boards, disclosure language for users and regulators.

Evidence to listen for

  • Explains the work to an engineer, an executive, or a funder without either mystifying or dumbing it down
  • Writes clearly
  • Collaborates across disciplines
  • Makes the case for resources in terms the audience cares about

Five-point scoring guide

1
Poor

Cannot communicate outside their specialism.

2
Needs Improvement

Explanation is either impenetrable or hollow.

3
Satisfactory

Adequate with technical peers; less effective with lay audiences.

4
Very Good

Explains clearly to specialists and non-specialists alike.

5
Excellent

Translates alignment or bias findings into decisions and trade-offs a product lead grasps, with evidence a recommendation was adopted.

Evidence-led prompts

Interview questions for a AI Ethicist

Use these prompts to surface evidence for the weighted factors above and compare candidates against the same role-specific criteria.

  1. 01

    Can you describe a time when you identified an ethical risk in a project?

  2. 02

    Can you give an example of how you would address bias in a system?

  3. 03

    What steps would you take to reduce the potential misuse of a technology?

  4. 04

    What frameworks do you use to evaluate the ethical implications of a system?

  5. 05

    How would you evaluate a new application before it is built?

See the complete AI Ethicist question set
Put this rubric to work

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