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Interpretability Direction

A practical guide to interpretability direction for AI red-teaming practitioners.

What This Lesson Covers

Interpretability Direction is a key lesson within Deception & Scheming Evaluation. In this lesson you will learn the underlying red-team discipline conceptually, the practical methodology that operationalises it inside a working program, the defensive implications, and the failure modes that quietly undermine red-team work in practice.

This lesson belongs to the Capability & Safety Evaluations category. The category covers the eval discipline used by frontier labs and AI Safety Institutes — dangerous capability evaluations across CBRN uplift, cyber, persuasion, autonomy, and deception, and the frontier-lab eval suite landscape.

Why It Matters

Evaluate deception and scheming behaviour as practiced in frontier-lab safety teams and AISI work. Learn the conceptual taxonomy (sycophancy, strategic deception, alignment-faking, sandbagging), eval methodologies (sandboxed setups with hidden observers, behavioural probes), interpretability probes (research direction), the disclosure norm in system cards, and the engineering implications.

The reason this lesson deserves dedicated attention is that AI red teaming is now operationally load-bearing: frontier labs publish red-team findings in system cards, US and UK AISIs run pre-deployment evaluations under contract, regulators write red-teaming duties into law (EU AI Act high-risk obligations, US Executive Orders), customer RFPs demand evidence, and incident inquiries depend on a defensible red-team record. Practitioners who reason from first principles will navigate the next attack class, the next finding, and the next stakeholder conversation far more effectively than those working from a stale checklist.

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Mental model: Treat AI red teaming as a chain of evidence and capability — the threat model, the abuse case, the probe, the finding, the repro packet, the severity rating, the fix, the regression test, the disclosure. Every link must be defensible to a sophisticated reviewer (engineering leadership, regulator, AISI evaluator, customer, journalist, incident-response commander). Master the chain and you can defend the program that survives the next inspection, whatever shape it takes.

How It Works in Practice

Below is a practical AI red-team pattern for interpretability direction. Read through it once, then think about how you would apply it inside your own program.

# AI red-team operating pattern
RED_TEAM_STEPS = [
    'Anchor the work in a written authorization and a specific abuse case',
    'Pick the right operational layer (prompt, model, agent, multimodal, capability eval)',
    'Design probes from the abuse case with adversary-success criteria',
    'Run probes against the target with full audit / repro discipline',
    'Score findings with the severity rubric and write the repro packet',
    'Hand off through the disclosure pipeline to engineering for fix and regression',
    'Verify the fix, contribute to system-card / transparency disclosure as appropriate',
]

Step-by-Step Operating Approach

  1. Anchor in authorization and an abuse case — Without written authorization, the work is not red teaming, and without an abuse case, the work has no destination. Skip either and the program is fragile.
  2. Pick the right operational layer — Prompt, model, agent, multimodal, or capability eval. Each layer has its own methodology, eval suites, and defence implications. The wrong layer wastes capacity.
  3. Design probes from the abuse case — A probe needs a clear adversary-success criterion (what counts as evidence that the abuse is feasible) and a clean experimental setup that an outside reviewer could replicate.
  4. Run probes with audit discipline — Capture inputs, outputs, model version, parameters, harness, and timestamps. Findings without repro do not survive disclosure.
  5. Score and write the repro packet — Severity rubric (impact * exposure * exploitability with AI-specific dimensions), repro packet (prompt or input, expected vs actual, environment, model version, sample size, redaction).
  6. Hand off through the disclosure pipeline — Internal triage, engineering ownership, fix tracking, regression-test creation. A finding without a regression test is a finding waiting to regress.
  7. Verify and disclose appropriately — Verify the fix, contribute to system cards / transparency reports / public advisories where warranted, and feed back into the abuse-case catalog and probe library.

When This Topic Applies (and When It Does Not)

Interpretability Direction applies when:

  • You are designing, shipping, or operating an AI system that warrants red-team review (consumer platform, enterprise AI, agent system, frontier model, regulated deployment)
  • You are standing up or operating an AI red-team function
  • You are evaluating a third-party model under a procurement or pre-deployment review
  • You are responding to a regulator, AISI, oversight board, or customer question about red-team practice
  • You are running pre-deployment evaluation under an RSP or responsible-scaling commitment
  • You are participating in an AI bug bounty as researcher or operator

It does not apply (or applies lightly) when:

  • The work is pure research with no path to deployment and no need for defensive disclosure
  • The system is fundamentally not AI-driven and does not surface AI-specific attack classes
  • You do not have written authorization for the testing target — in that case, stop and obtain authorization first
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Common pitfall: The biggest failure mode of AI red teaming is theatre — flashy findings that never become fixes, abuse-case catalogs that grow but never trigger probes, severity ratings that drift with reviewer mood, repro packets that engineering cannot run, public disclosures that look impressive but do not move the next release, and program metrics that count activity rather than fixes. Insist on integration with engineering ownership, on action-item closure, on regression-test creation alongside fixes, on calibrated severity, on repro packets that 9am Monday engineering can execute, and on disclosure language that reports impact without giving uplift. Programs that stay grounded in actual fixes hold; programs that drift into pure communication get cut at the next budget cycle — or worse, fail the next regulator inspection or post-incident review.

Practitioner Checklist

  • Is the abuse case this lesson addresses in the catalog, with named owner and prioritisation rationale?
  • Is written authorization in place for the testing target (and is the scope respected)?
  • Is the probe design tied to a clear adversary-success criterion that an outside reviewer could check?
  • Is the repro packet complete (input, output, environment, model version, parameters, sample size, redaction)?
  • Is the severity rubric calibrated and applied consistently across reviewers?
  • Is each finding tracked to closure with a regression test and a verified fix?
  • Is disclosure handled through the right channel (internal, vendor, system card, transparency report, advisory) with impact-without-uplift language?

Disclaimer

This educational content is provided for general informational and defensive-security purposes only. It covers AI red-teaming concepts, taxonomies, methodology, and defensive implications — not step-by-step exploit recipes. Red-team activity must always be performed under explicit written authorization on systems you are authorised to test, with scope respected, with appropriate legal review, and in line with applicable laws (CFAA in the US, CMA in the UK, equivalents elsewhere) and applicable terms of service. This content does not constitute legal, regulatory, security, or professional advice; it does not create a professional engagement; and it should not be relied on for any specific testing or disclosure decision. Always consult qualified security and legal counsel for advice on your specific situation.

Next Steps

The other lessons in Deception & Scheming Evaluation build directly on this one. Once you are comfortable with interpretability direction, the natural next step is to combine it with the patterns in the surrounding lessons — that is where doctrinal mastery turns into a working AI red-team capability. AI red teaming is most useful as an integrated discipline covering authorization, threat modeling, probe design, finding lifecycle, severity, disclosure, and the link to engineering fixes and public reporting.