# Fairness pre-flight checklist

This is the gate the Boolean search builder runs against the role intake before generating any query. If any check fails, the skill halts and surfaces the offending content to the user. Do not edit this file to make a violating intake pass — edit the intake to remove the proxy.

The intent: a search query that encodes a protected-class proxy will return candidates filtered on that proxy. The downstream rubric ranking and human review will not catch it because the biased filter happened upstream of the pool. The fix has to be at the query layer.

## Halt conditions (any match → halt)

### School-tier or institution scoring as a standalone signal

- Any reference to "Tier 1" / "T1" / "elite" schools as a must-have or nice-to-have.
- Lists of specific universities as a positive or negative filter ("Stanford, MIT, CMU only" or "no bootcamp grads").
- "Top X schools" framing.

**Fix:** identify the underlying technical-depth signal that schools tend to correlate with (algorithmic depth, systems coursework, research exposure) and score on the signal. Graduates from non-target schools who have the depth pass; graduates from target schools who don't, fail.

### Name-pattern filtering

- Filtering or scoring based on candidate name (transliteration patterns, ethnic-origin inference).
- "Native English speaker" or related framings (legal exposure: national-origin discrimination under EEOC).

**Fix:** if language proficiency is required for the role (technical writing, customer-facing), score on demonstrated communication output (blog posts, talks, public PRs), not on name or self-reported proficiency.

### Employment-gap penalties

- Anti-signals or scoring deductions for employment gaps without context.
- "No gaps over 6 months" as a binary filter.

**Fix:** EEOC has guidance that blanket gap penalties have disparate impact (caregiving, illness, military reservist activation, parental leave). If continuous tenure is genuinely required (security-clearance roles), name the actual constraint instead of using a gap proxy.

### Photo presence or photo-based scoring

- Any reference to candidate photos, "professional appearance" requirements.
- Filtering by presence/absence of LinkedIn profile photo.

**Fix:** there is no fix — drop the dimension. Photo-based scoring has no defensible business reason in technical hiring.

### Age inference (graduation year, "early career," "experienced professional")

- Filtering on graduation year as an age proxy.
- "10+ years experience" combined with no senior-scope dimension (often a pretextual age filter).
- "Recent graduate" as a must-have when the role is non-entry-level.

**Fix:** scope-based scoring (Senior IC, Staff scope, Founding-engineer scope) captures the actual signal without the age proxy.

### Pregnancy or parental-status inference

- "Available for full-time work" or "no parental leave plans" framings.
- Filtering based on family status indicators.

**Fix:** there is no fix — drop the dimension. Pregnancy/parental discrimination is illegal in most jurisdictions and has zero defensible role in a search query.

### "Culture fit" without behavioral anchors

- "Culture fit" or "fits our culture" as a standalone dimension.
- Vague affinity signals ("plays sports" / "likes craft beer" / etc.).

**Fix:** name the specific behavioral signal — "communicates ambiguity early," "ships under deadline pressure without quality regression," "challenges plans with evidence" — and score on observable behavior.

### Group-affiliation filtering as positive or negative

- Filtering by political affiliation, religious affiliation, sexual orientation, gender identity, disability status, veteran status (except where law explicitly requires veteran preference, which is documented separately).
- Filtering by membership in / absence from professional organizations that correlate with protected classes.

**Fix:** drop the dimension. None of these have a defensible search-query use case.

## What halts surfaces

When a halt condition matches, return:

```
HALT: rubric_failed_fairness_preflight

Offending lines from the intake:
  L{n}: {line content}

Halt category: {category from above}

Suggested fix: {category-specific fix from above}

The skill will not generate queries until the intake is revised.
```

## Why this is non-negotiable

1. **Legal exposure.** NYC Local Law 144 requires bias audits for AI hiring tools. EU AI Act categorizes hiring AI as high-risk. EEOC has issued guidance on AI hiring. A search query is part of the AI hiring decision pipeline.

2. **Technical correctness.** A biased query returns a biased pool. No amount of downstream rubric ranking or human review fixes the upstream filter; you are choosing among a pre-filtered subset that doesn't include the candidates the proxy rejected.

3. **Audit defensibility.** Under any of the above legal frameworks, the firm needs to demonstrate that the search criteria don't encode protected-class proxies. The pre-flight log entry is part of that demonstration.