# Coverage Key — L1 Adaptive Abuse Detection

## Chosen archetype(s)

- **Hidden-Type Screening** (`hidden_type_screening`) — the core is inferring a hidden attribute (abusive intent) from indirect signals before/with imposing consequences, under adversaries who game the screen.
- **Error Tradeoff Calibration** (`error_tradeoff_calibration`) — the system fails in two opposite ways: false negatives (evaders slip through) vs false positives (innocent sellers wrongly restricted); the threshold must encode an accountable posture, not a hidden one.
- **Procedural Fairness Design** (`procedural_fairness_design`) — the "wrongly restricted with no clear way to find out why" half: affected users need notice, reasons, and a meaningful review/appeal path.

Together these three cover detection-under-asymmetry, the error-cost tradeoff, and contestability. The problem's distinctive spine — adversaries adapt and signals go stale — is carried by the recalibration/monitoring components both screening and calibration archetypes name as load-bearing, surfaced explicitly below.

## Coverage Key (load-bearing components)

1. **Hidden-attribute hypothesis** — State precisely what is being inferred (abusive intent/behavior class: fake reviews, payment fraud, coordinated scams), not a vague "bad account" notion. Different abuse types need different signals.

2. **Signal / evidence channel** — The observable indicators (behavioral, network, transactional) used to infer abuse; chosen to be relevant and hard to fake, not just convenient.

3. **Threshold and routing rule** — How a score maps to outcomes: ignore / watch / soft-friction / human review / restrict. Not a single block-or-allow cutoff — staged routing reserves hard action for high-confidence cases.

4. **Error-cost profile (FP vs FN), explicit** — Name the cost of wrongly restricting an honest seller vs letting an evader through, and which the platform tolerates where. The threshold must express this stated posture rather than an unexamined default. — [HARD] The brief frames this as "catch more abuse," nudging toward a single sensitivity knob; the FP harm (driving off honest sellers) is the *other* named failure mode and is easy to treat as secondary rather than co-equal in calibration.

5. **Base-rate / validation check** — Confirm signals actually predict abuse in the current population, including subgroup performance; a signal strong at launch can decay to noise. Guards against the proxy trap.

6. **Adaptation & recalibration loop** — Treat the adversary as actively learning the rule: monitor for evasion, refresh/rotate signals, and retrain as bad actors shift behavior. This is the "keeps catching abuse over time" requirement made operational. — [HARD] A capable designer will build a good *static* detector and a good appeal flow, then declare victory; the problem is explicitly that last year's good detector quietly rotted. The defender-vs-adapting-opponent dynamic (coevolution) demands ongoing relearning, not a one-time tuning, and is the single most-dropped requirement here.

7. **Anti-gaming / signal-secrecy design** — Decide what about the detection logic stays opaque to deny attackers a recipe, while still meeting the transparency owed to the accused. Mix stable + unpredictable signals so the rule can't be reverse-engineered. — [HARD] This is in direct tension with the fairness/reason-giving requirement (#9): full transparency teaches evaders, full opacity makes appeals meaningless. A quick designer satisfies one side and silently sacrifices the other instead of designing the boundary deliberately.

8. **Notice and reason-giving** — When an account is flagged/restricted, the user is told what happened and a reason specific enough to understand and contest — directly answers "no clear way to find out why."

9. **Review / appeal path with remedy** — An accessible route to challenge a restriction, reviewed with enough independence and evidence access to actually overturn errors and reinstate (not appeal theater).

10. **Impartiality / independent review before or shortly after hard action** — Separation between the automated flag and the consequence decision so a noisy score doesn't become an unchallengeable verdict; high-stakes restrictions get human/second-stage review. — [HARD] The brief says flags lead to "review or restriction" as if interchangeable; the easy build lets the model's score *be* the decision. Inserting independent verification before irreversible restriction (and rapid post-action review when speed is needed) is the safeguard most likely to be collapsed away for throughput.

11. **Consistency standard** — Like cases handled alike, with documented distinctions; prevents arbitrary variation across similar flagged sellers and makes the appeal outcomes defensible.

12. **Proportional / reversible consequence design** — Match action severity to confidence and harm (soft limits / warnings for ambiguous cases, hard restriction only for high-confidence), keeping interventions reversible so FP damage is repairable.

13. **Monitoring of both error directions** — Track not just visible false positives (seller complaints) but the hidden false negatives (evaders getting through) and appeal-overturn rates, with comparable seriousness; feeds #6's recalibration triggers.

14. **Privacy / data-minimization boundary** — Collect and log only the signals justified by the decision stakes; keeps the screen from sliding into general surveillance and bounds what the appeal record must expose.
