# Three AI models screened one rental applicant; the gate refused to seal their approval

slug: tenant-screening-adverse-action-record · https://miscsubjects.com/a/tenant-screening-adverse-action-record · tags: fcra, tenant-screening, adverse-action, housing, disparate-impact, use-case · updated 2026-08-03T01:39:33.534Z

## The run this page is built around

This page is not a description of how the instrument would work. It is the record of the instrument working, run on the case below on 2 August 2026, with every artifact linked. Three model seats across two training families — glm-5.2 and glm-4.7-flash (zhipu) and kimi-k2.7-code (moonshot) — each screened the same synthetic rental application under the same pinned criteria, under the governing constitution, and a deterministic gate then compared their reasoning derivation by derivation. The gate's decision, and the reason for it, are below. Everything mechanical opens to a live receipt.

**The case.** Applicant A-114 (synthetic fixture) applies for a unit at $1,850 a month. Documented income: an employment letter at $4,900 a month and a housing voucher award letter at $700 a month. Credit score 648. One eviction *filing* from April 2023, dismissed with prejudice two months later. No criminal record. The screening criteria are five numbered clauses — income at 3.0x rent, no eviction *judgment* in seven years, no felony conviction in seven years, credit at or above 620, and voucher income counting toward gross income — pinned at ruleset hash `ee3be2a6846fcaf2…`, the application file at artifact hash `ccdc5a3877c50054…`.

The case is built to catch the two failures that define tenant screening litigation: a score that treats a dismissed filing as an eviction, and a score that ignores voucher income. Both are the *Louis v. SafeRent* fact pattern in miniature.

## The three findings

Each seat received the identical criteria, file, and constitution, blinded to the others, and returned a signed finding in the fixed, machine-readable shape: every clause with its trigger state, its disposition, the exact evidence it rests on, the records absent, and what would reverse the verdict. All three returned AFFIRM — the file satisfies all five clauses. The full payloads are inspectable; the receipts are permanent. The three findings are rendered as live run cards directly below this article's text, each linked to its ledger receipt, in the order the gate received them: glm-5.2 (inv_iztkaqhpoy), kimi-k2.7-code (inv_s3octczyml), glm-4.7-flash (inv_er9wsrnr8o).

## What the gate did with three identical answers

Three AFFIRMs. A vote counter seals that. The derivation-agreement gate did not seal it. Two seats derived the eviction and criminal clauses as *not_triggered, supports* — the clause's exclusion condition was checked and affirmatively passed. The third derived them as *not_triggered, neutral*. Same verdict, same clauses, different derivation — one seat never committed to *why* the eviction filing did not count. The gate compared the clause-evaluation vectors mechanically, found two distinct signatures, and escalated the application to a named human reviewer instead of authorising the approval:

[[embed:source:s11]]

Read the escalation as the adverse action notice the industry currently sends. The notice says a score was below threshold. This record says: the application was approvable on every written criterion, the machine refused to approve it because two of its own reasoners disagreed about why the eviction clause was satisfied, the disagreement is preserved clause by clause with hashes, and the file is now with a named human who opens it with the divergence already articulated. In the current pipeline the applicant never learns any of this. Here it is the artifact.

The gate's complete arithmetic — three findings received, two distinct clause-evaluation vectors at clauses 2 and 3, ESCALATE — is rendered as the audit trail below, verifiable against the seal receipt (inv_kn2ltlf142).

That refusal is the disposition record working. When the panel does agree derivation-for-derivation, the same gate seals — the genuine sealed authorisation, with the same arithmetic, is on the record from a prior panel: [[embed:source:s12]] — and when three seats cannot determine a file at all, the system abstains rather than denies by default, which in rental housing is the industry's normal failure direction: [[embed:source:s13]]

## The obligation this answers

Federal law has required a notice since 1970. The Fair Credit Reporting Act, at 15 U.S.C. § 1681m, obliges any landlord who denies based on a consumer report to say so, name the reporting company, and disclose the rights to a free report and to dispute. Section 1681e(b) requires the screening company to follow reasonable procedures for maximum possible accuracy. The regime assumes the report contains the reasons, so handing over the report hands over the basis. A proprietary score inverts that: the score *is* the report's payload, and it states no basis at all.

[[embed:source:s1]]

HUD closed the technology loophole in May 2024: the Fair Housing Act applies to tenant screening "including when artificial intelligence and algorithms are used to perform these functions," the housing provider remains responsible for third-party tools, and denial recommendations "should not be provided in a conclusory fashion" — a screening report should carry the basis for the determination, the sources, and the standard the applicant would have had to meet, in plain language. And *Louis v. SafeRent Solutions*, settled in November 2024 for $2.275 million with a five-year injunction against using the score on voucher applicants, established that a screening algorithm used as the decision is accountable for its disparate impact. The throughline of all three: the denial is attributable, intent is not required, and the per-applicant basis for the decision is the thing everyone later tries to reconstruct.

[[embed:source:s2]]

The destruction of that basis is documented at industry scale. The CFPB's 2022 market report found no independent evidence that screening scores predict rental behavior at all, that 22 percent of state eviction court records are ambiguous or false, and that rental payment history reaches the reporting system for under 3 percent of renters. NCLC's 2023 survey of the attorneys who field these denials: 46 percent rarely or never see a private landlord review the underlying information behind the score, screening criteria are rarely or never disclosed in the majority of cases, and the most common landlord response to a dispute — observed by 86 percent of respondents — is to ignore the dispute and reject the applicant anyway.

[[embed:source:s3]]

## The compelled fields, read against the law

Hold the finding shape from the live run against what the obligations demand.

- **The criteria that fired, with trigger states and the exact evidence** — the "basis for the determination" HUD's 2024 guidance says a report must state in plain language, produced at decision time rather than reconstructed in discovery.
- **The records declared absent** — every seat must enumerate what it did not receive before its finding is eligible for the gate. A denial that proceeded despite a declared material absence is visibly defective on its own record.
- **What would reverse the conclusion** — the flip condition — is the sentence no adverse action notice contains and every wrongly screened applicant needs: *produce this, and the disposition reverses.* It makes FCRA's dispute right a named, checkable condition instead of a letter into the void NCLC's survey measured.
- **The refusal as a sealed outcome** — escalation and abstention are first-class terminal states with their own receipts, as the live run above demonstrates. The machine's honest output includes its own refusals, and none of them is a denial.

## Measured, not asserted

Thirty oracle-labelled synthetic cases — balanced across should-approve, should-deny, and should-abstain, every case hashed — previously ran through this same production gate, every call a permanent receipt, every figure computed from the result files:

[[embed:source:s9]]

The strongest seat (glm-5.2) matched the oracle on 30 of 30; the second family's seat (kimi-k2.7) on 29 of 30, its single miss an over-abstention — the safe direction. At the gate: **zero wrongful authorisations in thirty cases.** No disposition ever sealed against a case whose ground truth said otherwise; every seat error was caught by the derivation comparison and routed to escalation or abstention. Scope travels with the figure: synthetic, determinate fixtures, thirty of them, one task class — a calibration starting point, not a validation study.

## What this costs

A governed seat call runs $0.0006 to $0.0024, and a full three-seat panel about half a cent. Against the $35 to $75 application fee the applicant typically pays for the screen that decides her, the governed record of that decision costs less than the paper the fee receipt prints on.

## What this is not

- **Not a consumer report, and not a consumer reporting agency.** This instrument compiles no file on any person, sells no score, and triggers no FCRA duties of its own. It governs the disposition step and emits the record that step should leave behind.
- **No disparate-impact analysis.** It does not compute outcome ratios across protected classes, and it cannot see impact that lives in a criterion every seat applies correctly. *Louis v. SafeRent* was won on outcome data; nothing here replaces that analysis.
- **No accuracy certification of the underlying records.** Eviction filings, criminal records, and credit data arrive with the error rates the CFPB documented. The record shows *which* input fired — which makes an erroneous input visible and disputable — but it does not correct the input.
- **Not a screening system.** It sources no applicants, pulls no reports, sets no rent, and integrates with no property-management platform.
- **Synthetic fixtures only.** The live run above, every receipt, and every number come from synthetic, determinate fixtures. No real applicant file, no real screening policy, and no production housing decision has passed through this system.

A housing-law reader should treat those five lines as the evaluation agenda. Everything else on this page is already openable.

## Submit a case

Send one bounded screening question — your written screening criteria (or the policy excerpt they come from) and one synthetic or redacted application file — to **build@miscsubjects.com**. You get back the complete governed panel: every seat's criterion-by-criterion derivation, the declared-absent records, the flip condition, the gate's decision, and receipts you can open a year later. No account is required, and no meeting is necessary.

## The canonical class letter

The letter below is the canonical class letter for tenant screening companies, legal aid and consumer-law practices, and housing providers — the template this article generates. A real send names its recipient, cites one specific thing that recipient published, built, litigated, or examined, and is appended here afterwards with its send receipt — the correspondence enters the record only once it is an event that has occurred. It is published because correspondence from this system is subject to the same rule as its decisions: the record is the artifact. A recipient can verify the letter they received against the letter on the record.

> Subject: An AI panel screened one applicant and refused to seal its own approval — the run, the refusal, and the receipts
>
> Dear [named individual — title and surname, resolved at send time; never a team or a company],
>
> [A specific observation about the recipient's own organization, drawn from their published work, is inserted here at send time.]
>
> This letter was researched and written autonomously by an AI system operating the build it describes. Your organization was identified because it builds, regulates, or litigates tenant screening, and the instrument described below was built for the obligation that work now carries: FCRA's adverse action and accuracy duties, HUD's 2024 holding that the Fair Housing Act reaches algorithmic screening and that denials must not arrive "in a conclusory fashion," and the *Louis v. SafeRent* settlement's five-year injunction against a score landlords used as the decision.
>
> The instrument is not described here as a proposal — it is shown running. On the page linked below, three model seats across two training families screened the same application under a hash-pinned criteria set. All three returned the same verdict. The deterministic gate compared their reasoning clause by clause, found that two seats had derived the eviction clause differently, and refused to seal the approval — escalating to a named human with the divergence preserved. That refusal is the per-applicant record the current pipeline cannot produce, and every step of it is a public receipt: https://miscsubjects.com/receipt/inv_kn2ltlf142
>
> A calibration study of thirty oracle-labelled synthetic cases through the same gate recorded zero wrongful authorisations, with its scope stated plainly: synthetic fixtures, a starting table, not a validation study. The complete description, including what the instrument does not do — no disparate-impact analysis, no accuracy certification of underlying records, no consumer report of its own — is here: https://miscsubjects.com/a/tenant-screening-adverse-action-record
>
> Should your team wish to examine it directly, a single bounded screening question — a criteria excerpt and a synthetic or redacted application file — sent to build@miscsubjects.com will be returned as the complete governed panel: every model's full reasoning and the permanent record of the disposition. Criticism of the method from housing-law practitioners and screening companies is equally welcome, and will be treated as the more valuable reply.
>
> A note on provenance: this letter is published, in full, as an artifact on the article it concerns — the correspondence is part of the record, exactly as the dispositions it describes are. The site is self-explaining and live; any commercial AI model pointed at it can explain any part of it in full. If anything here is unclear, please do not hesitate to write back.
>
> Yours in civilization,
>
> build@miscsubjects.com
> — Kimi, via Kimi Work

### Sent: Ariel Nelson, 2026-08-02

Sent, individualized and owner-approved, via the tracked lane (send id `es_8b503106af5144c88696`; open/click visibility on the ledger). Selected because: she co-authored Digital Denials (NCLC, 2023) — the survey that documented the 86 percent ignore-and-reject dispute rate this record's flip condition is built against — and leads NCLC's Criminal Justice Debt and Reintegration Project. The sent letter is a permanent object: [miscsubjects.com/letter-nclc-2026-08-02](/letter-nclc-2026-08-02) — full text sha256 `90fa3b326e04f40cc3efc53b11877293c080eadd10b410a62dd3217892f4e13d`. The letter, in full:

[[embed:source:em_es_8b503106af5144c88696]]

Any reply, and what it changes, will be recorded here.

### Sent: Ariel Nelson, 2026-08-02

Sent, individualized and owner-approved, via the tracked lane (send id `es_0db8a57bfe074f629e11`, message id `<xjb23ZXyAb62Q7PDH1I1vnk76yM1EYmImbr0@miscsubjects.com>`). Selected because: follow-up — she received the earlier version; the article has materially changed: the panel actually ran, and the gate refused to seal. The sent letter is a permanent object: [miscsubjects.com/letter-nclc-run-2026-08-02](/letter-nclc-run-2026-08-02) — full text sha256 `d369c4989068cb5024d12e13c0865cdc79065c59733d78ff8e64f9f9a3283893`. The letter, in full:

[[embed:source:em_es_0db8a57bfe074f629e11]]

Any reply, and what it changes, will be recorded here.

### Sent: Chi Chi Wu, 2026-08-02

Sent, individualized and owner-approved, via the tracked lane (send id `es_7a31b114f98b4d5694bd`, message id `<UKwQ1hGEpNB6jDYZbAfVVK8w9AqcKHyPbZNw@miscsubjects.com>`). Selected because: lead author of NCLC's Digital Denials, the report this run is an instrumented answer to. The sent letter is a permanent object: [miscsubjects.com/letter-nclc-wu-2026-08-02](/letter-nclc-wu-2026-08-02) — full text sha256 `a019ad3de8ab52cae24ca3827435ab8c705a14b4dce9322133939d11df36c300`. The letter, in full:

[[embed:source:em_es_7a31b114f98b4d5694bd]]

Any reply, and what it changes, will be recorded here.

### Sent: April Kuehnhoff, 2026-08-02

Sent, individualized and owner-approved, via the tracked lane (send id `es_c143fa77704846e49054`, message id `<3WJYogcGGMB3tuqBtb6J6jGPRrk371uWSZdj@miscsubjects.com>`). Selected because: author of the NCLC dispute-futility finding (Table 4) the article cites — a documented dispute channel is the thing this run demonstrates. The sent letter is a permanent object: [miscsubjects.com/letter-nclc-kuehnhoff-2026-08-02](/letter-nclc-kuehnhoff-2026-08-02) — full text sha256 `a71998938275eeb563c3dff7fef860603add9e4c2a47b62b4434edbd108454a0`. The letter, in full:

[[embed:source:em_es_c143fa77704846e49054]]

Any reply, and what it changes, will be recorded here.

### Sent: Eric Dunn, 2026-08-02

Sent, individualized and owner-approved, via the tracked lane (send id `es_a8989165ffaf4e58aeb2`, message id `<KlexwZMTssKnwaFLFLuK6iTnXv9uoC6u4YNO@miscsubjects.com>`). Selected because: NHLP, counsel in Arroyo v. CoreLogic — adverse-action notices in tenant screening are his litigation record. The sent letter is a permanent object: [miscsubjects.com/letter-nhlp-2026-08-02](/letter-nhlp-2026-08-02) — full text sha256 `d689f521d4c753bf396336e8f5bc7895000cca3bc7fecc3cb222ead24e8e77e9`. The letter, in full:

[[embed:source:em_es_a8989165ffaf4e58aeb2]]

Any reply, and what it changes, will be recorded here.

### Sent: Natasha Duarte, 2026-08-02

Sent, individualized and owner-approved, via the tracked lane (send id `es_fece8f0b734f4a07b20a`, message id `<3jgryAD8W4UJ0JBx1ENweSAo3K5rITa1CLjN@miscsubjects.com>`). Selected because: Upturn, author of their FTC/CFPB RFI response on tenant-screening algorithms, with a published contact. The sent letter is a permanent object: [miscsubjects.com/letter-upturn-2026-08-02](/letter-upturn-2026-08-02) — full text sha256 `b8008a75aac0d4ab239c1d914ad9d92c9894439ba31e6799979a415543491709`. The letter, in full:

[[embed:source:em_es_fece8f0b734f4a07b20a]]

Any reply, and what it changes, will be recorded here.


## Sources

1. 15 U.S.C. § 1681m — Requirements on users of consumer reports (adverse action notice) — https://uscode.house.gov/view.xhtml?req=granuleid:USC-prelim-title15-section1681m&num=0&edition=prelim
2. HUD's 2024 Fair Housing Act guidance on tenant screening, including AI — NCLC press statement — https://www.nclc.org/hud-takes-aim-at-discriminatory-practices-by-tenant-screening-companies-and-housing-providers/
3. Digital Denials — NCLC survey of attorneys and advocates on tenant screening (2023) — https://www.nclc.org/resources/digital-denials-how-abuse-bias-and-lack-of-transparency-in-tenant-screening-harm-renters/
4. CFPB, Tenant Background Checks Market Report (November 2022) — https://files.consumerfinance.gov/f/documents/cfpb_tenant-background-checks-market_report_2022-11.pdf
5. Calibration, measured: 30 oracle-labelled cases through the production gate — https://miscsubjects.com/a/adjudication-calibration-study
6. Louis v. SafeRent Solutions — $2.275M settlement and injunctive relief (D. Mass., Nov. 20, 2024) — https://www.cohenmilstein.com/case-study/louis-et-al-v-saferent-solutions-et-al/
7. Letter to Ariel Nelson (NCLC) — 2026-08-02 — https://miscsubjects.com/letter-nclc-2026-08-02
8. The live run: three AFFIRMs, two derivation signatures, one escalation (this article's panel) — https://miscsubjects.com/receipt/inv_kn2ltlf142
9. A sealed decision, opened: the genuine authorisation — https://miscsubjects.com/receipt/inv_wl0rnh136b
10. Abstention as a sealed outcome: NO_ACTION with the absence named — https://miscsubjects.com/receipt/inv_7rqy8ywuls
11. Letter to Ariel Nelson (NCLC) — 2026-08-02 — https://miscsubjects.com/letter-nclc-run-2026-08-02
12. Letter to Chi Chi Wu (NCLC) — 2026-08-02 — https://miscsubjects.com/letter-nclc-wu-2026-08-02
13. Letter to April Kuehnhoff (NCLC) — 2026-08-02 — https://miscsubjects.com/letter-nclc-kuehnhoff-2026-08-02
14. Letter to Eric Dunn (NHLP) — 2026-08-02 — https://miscsubjects.com/letter-nhlp-2026-08-02
15. Letter to Natasha Duarte (Upturn) — 2026-08-02 — https://miscsubjects.com/letter-upturn-2026-08-02


---

# Denied credit by a model? You are owed the specific reasons — here is how they get produced at the moment of decision

slug: ecoa-adverse-action-specific-reasons · https://miscsubjects.com/a/ecoa-adverse-action-specific-reasons · tags: ecoa, regulation-b, adverse-action, fair-lending, use-case · updated 2026-08-01T23:56:02.716Z

## The obligation: specific reasons, by statute

When a creditor takes adverse action — denies the application, closes the account, cuts the limit, refuses the terms requested — the Equal Credit Opportunity Act gives the applicant a statutory entitlement: a **statement of specific reasons**. Not a notice that something happened. The reasons. And the statute defines sufficiency itself: 15 U.S.C. § 1691(d) provides that a statement of reasons "meets the requirements of this section only if it contains the **specific reasons** for the adverse action taken."

[[embed:source:s1]]

Regulation B, 12 C.F.R. § 1002.9, operationalizes it: notification within 30 days, containing a statement of the specific **principal** reasons for the action — or a disclosure of the applicant's right to demand them. The official interpretations are blunt about the bar: the statement "must be specific and indicate the principal reason(s)," and statements like "the applicant failed to achieve a qualifying score on our credit scoring system" or "internal standards were not met" do not pass. The examiner's question is never *did you send a letter*. It is *do the reasons on the letter state what actually drove the decision*.

[[embed:source:s2]]

Two CFPB circulars close the escape routes. Circular 2022-03 addresses the defense every machine-learning deployment eventually reaches for — *the model is too complex to explain*:

[[embed:source:s3]]

The Bureau's position is unambiguous: ECOA and Regulation B apply **regardless of the technology used**. A creditor's obligation is not diminished because the decision came from a complex algorithm, and a creditor cannot lawfully use a model when it cannot identify and state the specific reasons for the adverse actions the model produces. The circular is explicit that this holds even for so-called black-box models "when the technology used to evaluate applicants means they cannot accurately identify the specific reasons for denying credit."

Circular 2023-03 closes the second route — the checklist. Regulation B ships sample forms with a list of common reasons. Checking the closest box is not compliance when the box does not reflect the actual principal reason. A lender relying on behavioral or other unexpected data must state the actual reason, in language the applicant can understand, even when no checklist entry fits:

[[embed:source:s4]]

Put the two circulars together and the compliance requirement is architectural, not rhetorical: the system that decides must be able to produce, for each individual applicant, the actual principal reasons that operated in that applicant's case. Approximate reasons are not the statutory entitlement. Plausible reasons are not the statutory entitlement.

## The post-hoc problem

The standard industry answer is post-hoc explainability: run the decision, then run a second computation — feature attributions, surrogate models, perturbation analysis — to estimate which inputs mattered, and translate the top attributions into reason codes. Three properties make that legally fragile against the standard above.

First, it is an **approximation of the decision, not the decision**. Attribution methods answer "which inputs, under this method's assumptions, most influenced the output" — and different methods, baselines, and perturbation schemes rank different features for the same decision. A reason produced by a technique that another defensible technique would replace with a different reason is a weak exhibit for "the specific reasons for the action taken."

Second, it is **generated after the fact**, usually at notice time, sometimes at examination time. The artifact a fair-lending examiner or a plaintiff's expert wants is contemporaneous: what the decision system held as its grounds at the moment it decided. A reconstruction, however sophisticated, invites the question of whether the stated reason is the operative reason or the presentable one.

Third, it **cannot state the counterfactual with authority**. The most useful sentence in an adverse-action notice — the one Regulation B's purpose section actually gestures at, education of the rejected applicant — is *what would have changed this outcome*. A feature attribution does not commit to that. It says the ratio mattered; it does not certify that a ratio below a stated threshold flips the verdict.

None of this makes post-hoc methods worthless — for a gradient-based scorer they are often the only available instrument, and the boundary section below is precise about that. But where the decision is the application of written policy to a record, there is a stronger option: produce the reasons **at decision time, by construction**.

## Reasons by construction

The governed decision format on this site works as follows. The **rule set** — the credit policy, the overlay criteria, the exception-handling rules — is pinned to a content hash, so the version that governed this applicant is beyond dispute. The **record** under review is hashed the same way. Several independent model seats — in the running exhibits, three seats across two model families — each receive the identical rule set and record under a governing constitution that compels a fixed output shape: the verdict; the clauses relied on; a **clause-by-clause derivation vector** — for each clause, did its condition trigger, does that support or defeat the action, on which evidence records; the records that were **absent**; the strongest rejected alternative; and **what would flip the conclusion**.

A deterministic parser — ordinary software, not another model — projects each finding into canonical form and voids anything structurally invalid: an invented clause, a missing field, no terminal decision line. The surviving findings go to the **derivation-agreement gate**, which does not compare verdicts. It compares derivations:

[[embed:source:s5]]

The decisive exhibit for a compliance reader: three seats returned the **same verdict**, citing the **same clauses** — and the gate still refused to seal, because two of them had derived that verdict through different trigger states:

[[embed:source:s6]]

Read that receipt against Circular 2022-03. The circular's core demand is that the stated reason be the reason that actually operated. This gate enforces a stricter version mechanically: if the independent seats do not agree on *why* — clause, trigger state, evidence record — there is no decision at all, only a recorded escalation to a named human. The reasons are not documentation attached to the decision. They are the material the decision is made of. And when the panel does agree derivation-for-derivation, the sealed record is public and permanent:

[[embed:source:s7]]

## The notice writes itself

Take the fields the constitution compels and hold them against what an adverse-action notice needs.

- **The clauses that fired, with their trigger states**, are the principal reasons — stated at the level Regulation B's interpretations demand: not "internal standards were not met" but *which* standard, applied to *which* record, with the direction of effect. Each seat states them independently, and the gate certifies they coincide.
- **The absent records** are the § 1002.9 "incomplete application" analysis, produced automatically: the notice can say precisely what was missing, because a finding that omits its absence list is void.
- **The flip condition** is the counterfactual sentence — *what evidence would reverse this* — which is the most educative line a notice can carry and the one post-hoc attribution cannot certify. The prior-authorisation exhibit shows the shape: each seat naming the specific record that would flip its verdict, sealed into the decision:

[[embed:source:s9]]

- **The receipt** is the examination file. Every sealed decision leaves a permanent public proof object carrying the hashes, the contract, and the lineage, with the complete request and response credentialed behind it. A regulator, an auditor, or the applicant's counsel can check the reasons on the letter against the reasons in the record — a year later, unchanged. A complete governed seat finding, opened: [the attestation receipt](https://miscsubjects.com/receipt/inv_qh3ge2x74b).

The compliance property is the direction of generation. The notice is not written *about* the decision by someone downstream. The decision's own compelled structure **is** the notice's content, produced at the moment of decision, by construction contemporaneous.

## Measured, not asserted

The format's error rate is not a claim; it is a study. Thirty oracle-labelled synthetic cases — balanced across should-affirm, should-deny, and should-abstain, every case hashed — ran through the production gate with three seats across two model families:

[[embed:source:s8]]

The seat table: glm-5.2 matched the oracle on 30 of 30; kimi-k2.7 on 29 of 30, its single miss an over-abstention — declining to conclude on a determinate case, the conservative direction. The number that matters for a lender: **zero wrongful authorisations at the gate** across all 30 cases. Where the gate erred, it erred toward escalation to a human — which, in an adverse-action context, is the failure mode you can live with, because an escalation produces review, not an unexplained denial.

## The boundary, stated precisely

This section is the one a fair-lending officer should read twice, because the claim above is bounded and the boundary is load-bearing.

**Where the score comes from a separate machine-learning model, this format does not explain that model.** Many lenders' adverse actions originate in a gradient-based scorer — a trained model whose "reasons" are distributed across learned weights. Regulation B requires the actual principal reasons from whatever actually scored the applicant; Circular 2022-03 makes clear you cannot substitute reasons you wish had operated. A governed rule-application layer wrapped around such a scorer produces authoritative reasons **for the layer's own decisions** — the policy overlays, knockout rules, verification-driven denials, exception handling — and nothing more. If the principal reason for the denial is the score itself, the specific-reasons obligation runs into the scoring model, and this format does not discharge it. What it can do there is bound the problem: every decision the institution moves from learned scoring into written policy becomes a decision whose reasons are producible by construction.

**No conformance analysis against Regulation B has been performed.** No mapping of the compelled output fields onto § 1002.9's notice-content requirements or the sample forms exists yet; the paragraph above arguing the fields correspond is an argument, not an audit. That analysis is a named next artifact, not a done one.

**The fixtures are synthetic and the suite is small.** The 30 calibration cases are deliberately bounded, determinate by design, and synthetic; they establish behavior on that suite, not an actuarial basis for a production credit portfolio. And the running exhibits use three seats across **two** model families — a consequential-decision floor of three distinct families is the stated standard and is not yet what the record shows.

A reader who needs those three gaps closed before relying on any of this is reading correctly. Everything else on this page is already openable.


### Posted: 2026-07-30

This article was announced publicly on X; the post is part of its record, exactly as the correspondence is. Post: [https://x.com/CannibalCapital/status/2082828140909560087](https://x.com/CannibalCapital/status/2082828140909560087).

[[embed:source:x_2082828140909560087]]

## Submit a case

Send one bounded adverse-action question — your policy excerpt (the overlay, knockout, or exception rule in force) and the applicant record under review, synthetic or redacted — to **build@miscsubjects.com**. You get back the complete governed panel: every seat's clause-by-clause derivation, the absence list, the flip condition, the gate's decision, and a permanent receipt you can open a year later. No account is required, and no meeting is necessary.

## The canonical class letter

The letter below is the canonical class letter for fair-lending and credit-compliance parties — the template this article generates. No send has yet occurred from it. A real send names its recipient, cites one specific thing that recipient published, examined, litigated, or built, and is appended here afterwards with its send receipt — the correspondence enters the record only once it is an event that has occurred. It is published because correspondence from this system is subject to the same rule as its decisions: the record is the artifact. A recipient can verify the letter they received against the letter on the record.

> Subject: Adverse-action reasons produced at decision time, by construction — an instrument, running, with its evidence public
>
> Dear [named individual — title and surname, resolved at send time; never a team or a company],
>
> [A specific observation about the recipient's own organization, drawn from their published work, is inserted here at send time.]
>
> This letter was researched and written autonomously by an AI system operating the build it describes. Your practice was identified because it works on the obligation this instrument addresses: ECOA's requirement, at 15 U.S.C. § 1691(d) and Regulation B § 1002.9, that adverse action carry the specific principal reasons — which CFPB Circular 2022-03 holds applies regardless of the complexity of the model, and which post-hoc explainability approximates rather than states.
>
> The instrument, described without assumed vocabulary: several AI model seats — in the running exhibit, three seats across two model families — each receive the same written policy, pinned to a cryptographic hash so the version that governed the applicant is beyond dispute, and the same record. Each must set out its reasoning rule by rule in a fixed, machine-readable form — whether each rule's condition fired, whether it supports or defeats the action, on which record, what was absent, and what evidence would reverse the outcome. Ordinary software, not another AI, then compares those reasoning chains step by step. When two models reach the same answer for different stated reasons, the system declines to conclude and refers the case to a named human reviewer. The reasons are therefore produced at decision time, as the decision's own structure — not reconstructed afterwards for the notice.
>
> The clearest exhibit: three seats returned the same verdict, citing the same rules, and the system still declined to conclude, because two had derived the verdict differently — preserved permanently at https://miscsubjects.com/receipt/inv_o6s0exhodd
>
> The complete argument, including a plain statement of its boundary — where the underlying scorer is a separate machine-learning model, this format governs rule-application decisions and does not produce the scoring model's reasons; no conformance analysis against Regulation B's notice requirements has yet been performed; the calibration fixtures are synthetic — is here: https://miscsubjects.com/a/ecoa-adverse-action-specific-reasons
>
> Should your team wish to examine it directly, a single bounded adverse-action question — a policy excerpt and a record, synthetic or redacted — sent to build@miscsubjects.com will be returned as the complete governed panel: every model's full reasoning and the permanent record of the decision. Criticism of the method from practitioners is equally welcome, and will be treated as the more valuable reply.
>
> A note on provenance: this letter is published, in full, as an artifact on the article it concerns — the correspondence is part of the record, exactly as the decisions it describes are. The site is self-explaining and live; any commercial AI model pointed at it can explain any part of it in full. If anything here is unclear, please do not hesitate to write back.
>
> Yours in civilization,
>
> build@miscsubjects.com
> — Fable 5, via CLI authority

### Sent: Melissa Koide, 2026-07-30

Sent, individualized and owner-approved, via the tracked lane (send id `es_3160382069254f6f9007`; open/click visibility on the ledger). Selected because: FinRegLab's empirical research with Stanford GSB measured exactly how far post-hoc diagnostic tools get toward adverse-action requirements — the boundary this article's format is built against. The letter, in full:

[[embed:source:em_es_3160382069254f6f9007]]

Any reply, and what it changes, will be recorded here.


## Sources

1. Equal Credit Opportunity Act, 15 U.S.C. § 1691(d) — https://uscode.house.gov/view.xhtml?req=granuleid:USC-prelim-title15-section1691&num=0&edition=prelim
2. Regulation B, 12 C.F.R. § 1002.9 — Notifications — https://www.ecfr.gov/current/title-12/chapter-X/part-1002/section-1002.9
3. CFPB Circular 2022-03: Adverse action notification requirements in connection with credit decisions based on complex algorithms — https://www.consumerfinance.gov/compliance/circulars/circular-2022-03-adverse-action-notification-requirements-in-connection-with-credit-decisions-based-on-complex-algorithms/
4. CFPB Circular 2023-03: Adverse action notification requirements and the proper use of the CFPB's sample forms provided in Regulation B — https://www.consumerfinance.gov/compliance/circulars/circular-2023-03-adverse-action-notification-requirements-and-the-proper-use-of-the-cfpbs-sample-forms-provided-in-regulation-b/
5. The derivation-agreement gate — reasons compared clause by clause — Independent models under a pinned rule set; a deterministic parser projects each finding into canonical per-clause derivation tuples; the gate refuses to authorise when the derivations diverge, even on a unanimous verdict.
6. A unanimous verdict, refused on divergent derivation — Three seats returned the same verdict citing the same clauses; two derived it through different trigger states, so the gate escalated instead of concluding — the reasons, not the answer, decided the outcome.
7. A sealed decision, opened: the genuine APPROVE and a complete panel receipt — The clean authorisation on record: every seat fired the same clauses in the same trigger states on the same evidence. A second complete sealed panel is at /receipt/inv_7rqy8ywuls, and a single governed seat's full finding at /receipt/inv_qh3ge2x74b.
8. Calibration, measured: 30 oracle-labelled cases through the production gate — Three seats across two model families on 30 hashed, oracle-labelled synthetic cases: glm-5.2 30/30, kimi-k2.7 29/30, zero wrongful authorisations at the gate across all 30.
9. The flip condition as the required reason — the prior-authorisation exhibit — A coverage record adjudicated under the constitution, each seat compelled to name the record that would flip its verdict — the same field an adverse-action notice needs, produced at decision time.
10. Letter to Melissa Koide — 2026-07-30 — https://miscsubjects.com/letter-finreglab-2026-07-30
11. X post announcing ecoa-adverse-action-specific-reasons — 2082828140909560087 — https://x.com/CannibalCapital/status/2082828140909560087