OMP Domain Profile: AI Governance and Accountability Evidence for US Housing Finance Under FHFA Bulletin 2025-16 and GSE AI/ML Model Risk Governance
draft-veridom-omp-fhfa-00
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| Document | Type | Active Internet-Draft (individual) | |
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| Authors | Tolulope Adebayo , Oluropo Apalowo , Festus Makanjuola | ||
| Last updated | 2026-04-06 | ||
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draft-veridom-omp-fhfa-00
Internet Engineering Task Force T. Adebayo
Internet-Draft O. Apalowo
Intended status: Informational F. Makanjuola
Expires: 7 October 2026 Veridom Ltd
5 April 2026
OMP Domain Profile: AI Governance and Accountability Evidence for US
Housing Finance Under FHFA Bulletin 2025-16 and GSE AI/ML Model Risk
Governance
draft-veridom-omp-fhfa-00
Abstract
This document defines a domain profile of the Operating Model
Protocol (OMP) for AI and machine learning (ML) systems deployed in
US housing finance contexts subject to the Federal Housing Finance
Agency (FHFA) Bulletin 2025-16 (effective March 3, 2026), which
establishes a comprehensive AI governance framework for Fannie Mae,
Freddie Mac, and the Federal Home Loan Banks (the GSEs), requiring
transparency, accountability, and ethical stewardship for AI/ML
systems used in housing finance decisions.
The profile -- designated HomeMark -- specifies how OMP's
deterministic routing invariant, Watchtower enforcement framework,
and three-layer cryptographic integrity architecture satisfy the AI
governance evidence requirements of FHFA Bulletin 2025-16, including
per-decision accountability, named individual responsibility, model
risk governance documentation, fair lending evidence, and
representation and warranty compliance for mortgage origination,
credit decisioning, property valuation, and loan servicing.
The OMP core specification is defined in the Operating Model Protocol
Internet-Draft (draft-veridom-omp).
Status of This Memo
This Internet-Draft is submitted in full conformance with the
provisions of BCP 78 and BCP 79.
Internet-Drafts are working documents of the Internet Engineering
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3
2. Terminology . . . . . . . . . . . . . . . . . . . . . . . . . 4
3. FHFA AI Governance Framework Analysis . . . . . . . . . . . . 5
3.1. FHFA Bulletin 2025-16 . . . . . . . . . . . . . . . . . . 5
3.2. GSE AI/ML Model Risk Governance . . . . . . . . . . . . . 5
3.3. Fair Lending Obligations and Disparate Impact . . . . . . 5
3.4. Representation and Warranty Framework . . . . . . . . . . 5
3.5. FHFA Examination Authority . . . . . . . . . . . . . . . 6
3.6. Convergent Requirements . . . . . . . . . . . . . . . . . 6
4. OMP HomeMark Profile . . . . . . . . . . . . . . . . . . . . 6
4.1. Routing States Under This Profile . . . . . . . . . . . . 6
4.2. Named Accountable Officer: The Responsible Individual . . 7
4.3. Watchtower Definitions . . . . . . . . . . . . . . . . . 7
4.3.1. WT-FHFA-01: Housing Finance Decision Floor Gate . . . 7
4.3.2. WT-FHFA-02: Fair Lending Override Gate . . . . . . . 7
4.3.3. WT-FHFA-03: Fair Lending Flag Gate . . . . . . . . . 8
4.3.4. WT-FHFA-04: AVM / AUS Training Limitation Gate . . . 8
4.3.5. WT-FHFA-05: Model Performance Anomaly Gate . . . . . 8
4.3.6. WT-FHFA-06: R&W Eligibility Verification Gate . . . . 9
4.4. Audit Trace Schema Extensions . . . . . . . . . . . . . . 9
5. Representation and Warranty Evidence Architecture . . . . . . 10
6. Fair Lending Evidence Package . . . . . . . . . . . . . . . . 11
7. The HomeMark Invariant . . . . . . . . . . . . . . . . . . . 11
8. Security Considerations . . . . . . . . . . . . . . . . . . . 12
9. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 12
10. References . . . . . . . . . . . . . . . . . . . . . . . . . 12
10.1. Normative References . . . . . . . . . . . . . . . . . . 12
10.2. Informative References . . . . . . . . . . . . . . . . . 13
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Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 14
1. Introduction
AI and machine learning systems are now foundational to US housing
finance operations: in automated underwriting systems (AUS) for
mortgage origination, automated valuation models (AVMs) for property
assessment, loss mitigation decisioning in loan servicing, and loan
acquisition models in the secondary market. The GSEs operate at
national scale -- Fannie Mae and Freddie Mac collectively support the
majority of US mortgage originations -- meaning that AI/ML governance
failures have systemic implications for housing access, fair lending,
and financial stability.
FHFA Bulletin 2025-16 [FHFA-2025-16] (effective March 3, 2026)
establishes four governance pillars: transparency (GSEs must explain
AI/ML decisions to regulators, counterparties, and borrowers at the
individual loan level); accountability (named individuals must bear
documented responsibility for AI/ML outcomes at scale); ethical
stewardship (AI/ML systems must not produce discriminatory outcomes
inconsistent with the GSEs' statutory mission); and model risk
governance (AI/ML systems must be subject to rigorous MRM frameworks
including decision-level reconstructability).
These requirements converge on a per-decision accountability problem
that OMP [I-D.veridom-omp] is specifically designed to address: for
any individual mortgage credit decision, property valuation, or
servicing action influenced by AI/ML, the entity must demonstrate
what the AI/ML recommended, what data it used, which named individual
bore accountability, and whether the record has remained intact.
This document defines the HomeMark profile: the domain-specific
instantiation of OMP for FHFA-regulated housing finance AI/ML
deployments. HomeMark denotes that every AI/ML-assisted housing
finance decision is cryptographically marked against the entity's
FHFA Bulletin 2025-16 obligations, producing a tamper-evident
accountability record at the loan level.
Related OMP domain profiles include the Employment ADS profile
[I-D.veridom-omp-employ] and the EU AI Act Article 12 profile
[I-D.veridom-omp-euaia]. Audit Trace payloads are canonicalized per
[RFC8785]. The OMP specification is also archived at [ZENODO-OMP].
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
document are to be interpreted as described in [RFC2119] [RFC8174].
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2. Terminology
This document uses the terminology defined in [I-D.veridom-omp]. In
addition:
* Government-Sponsored Enterprise (GSE): Fannie Mae, Freddie Mac, or
a Federal Home Loan Bank, as regulated by FHFA under the Housing
and Economic Recovery Act of 2008.
* Automated Underwriting System (AUS): A GSE-operated or GSE-
approved AI/ML system that evaluates mortgage applications and
provides a credit recommendation (Approve/Eligible, Refer, Refer
with Caution, or Ineligible). Includes Fannie Mae Desktop
Underwriter (DU) and Freddie Mac Loan Product Advisor (LPA).
* Automated Valuation Model (AVM): An AI/ML system that generates an
estimate of a property's market value based on comparable sales
data, property characteristics, and market conditions.
* Consequential Housing Finance Decision: An AI/ML-assisted decision
that directly affects a borrower's mortgage application status,
loan terms, property valuation, or loan servicing outcome.
Subject to the HomeMark Invariant.
* Responsible Individual (RI): The named individual within a GSE,
lender, servicer, or counterparty who bears documented
accountability for an AI/ML-assisted housing finance decision. In
OMP terms, the Named Accountable Officer for ASSISTED and
ESCALATED interactions.
* Representation and Warranty (R&W): The representations and
warranties made by mortgage originators and sellers to the GSEs
regarding loan quality, eligibility, and compliance. Where AI/ML
contributed to a loan-level decision, R&W obligations require the
ability to demonstrate that the AI/ML operated correctly and
consistently with applicable guidelines.
* Fair Lending Flag: A field indicating that the AI/ML
recommendation involves a borrower demographic profile or
geographic area identified in the entity's fair lending analysis
as requiring heightened review for potential disparate impact
under ECOA [ECOA] or the Fair Housing Act [FHA-1968].
* HomeMark Invariant: The two-property invariant defined in
Section 7: every Consequential Housing Finance Decision generates
a sealed HomeMark Audit Trace independently verifiable by FHFA
examiners, counterparties, and auditors.
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3. FHFA AI Governance Framework Analysis
3.1. FHFA Bulletin 2025-16
FHFA Bulletin 2025-16 requires transparency (individual loan-level
documentation explaining AI/ML decisions, contemporaneous not
retrospective), accountability (named Responsible Individuals with
documented responsibility for AI/ML system governance and decision
outcomes), ethical stewardship (per-decision fair lending monitoring
and disparate impact assessment), and model risk governance
(decision- level reconstructability, ongoing performance monitoring,
and human oversight at defined thresholds).
3.2. GSE AI/ML Model Risk Governance
GSE MRG frameworks, informed by Bulletin 2025-16 and SR 11-7
[SR-11-7], require decision- level reconstructability (for any loan-
level decision, the entity must reconstruct the model's input data,
version, and output consistent with the specific loan record);
ongoing monitoring for performance degradation, distributional shift,
and fair lending risk; and human oversight documentation at defined
thresholds.
3.3. Fair Lending Obligations and Disparate Impact
The GSEs operate under ECOA and the Fair Housing Act, prohibiting
both intentional discrimination and AI/ML practices producing
unjustified disparate impact. FHFA Bulletin 2025-16 requires GSEs to
assess and document disparate impact in AI/ML- assisted housing
finance decisions. The per-decision HomeMark Audit Trace provides
the loan-level evidence fair lending examinations require: what the
AI/ML recommended, what data it used, whether a fair lending flag was
triggered, and what human oversight was applied.
3.4. Representation and Warranty Framework
Where an AI/ML system contributed to loan origination or eligibility
determination, the seller's R&W obligations require the ability to
demonstrate that the AI/ML operated correctly and consistently with
applicable guidelines at origination. HomeMark Audit Traces
generated at origination provide this loan-level evidence: the RFC
3161 [RFC3161] timestamp proves the AI/ML recommendation was
generated at origination (not reconstructed retrospectively), the
interaction_hash proves input data integrity, and the
ai_ml_system_version documents which AUS version was in effect.
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3.5. FHFA Examination Authority
FHFA has broad examination authority over GSEs and their
counterparties. FHFA examiners may request AI/ML decision process
documentation, model risk governance evidence, and fair lending
monitoring data at the individual loan level. The HomeMark FHFA
Examination Package is designed to satisfy examiner requests within
the 30-second production capability specified in this profile.
3.6. Convergent Requirements
FHFA Bulletin 2025-16, GSE MRG frameworks, ECOA/FHA obligations, and
the R&W framework converge on a structure mapping to OMP's three
routing states: AI/ML decisions where the RI reviewed the
recommendation and bears documented accountability correspond to
ASSISTED; decisions where a Fair Lending Flag triggered, confidence
fell below the housing finance floor, or a model governance concern
was detected correspond to ESCALATED; fully autonomous AUS-eligible
transactions are permitted under AUTONOMOUS subject to Section 4.1
constraints, but HomeMark Audit Traces MUST be generated even for
AUTONOMOUS routing.
4. OMP HomeMark Profile
4.1. Routing States Under This Profile
* AUTONOMOUS: Permitted for standard AUS-eligible mortgage
transactions where: the AUS recommendation is Approve/Eligible;
the Confidence Score meets the AUTONOMOUS threshold; no Watchtower
has triggered; the Fair Lending Flag has not been set; and the
loan falls within the AUS's validated operating envelope. Even
under AUTONOMOUS routing, the HomeMark Audit Trace MUST be
generated and sealed for every loan-level interaction, consistent
with FHFA Bulletin 2025-16's transparency and reconstructability
requirements.
* ASSISTED: Required where: AUS recommendation is Refer or Refer
with Caution; transaction exceeds the significance threshold
requiring RI review; a Fair Lending Flag is set; or a model
governance concern is detected. The RI's identity, review
timestamp, and decision basis are sealed in the HomeMark Audit
Trace.
* ESCALATED: Triggered by: HARD_BLOCK from WT-FHFA-02, confidence
failure below the housing finance decision floor (WT-FHFA-01),
model performance anomaly (WT-FHFA-05), or regulatory override
requirement. AI/ML recommendation MUST NOT be acted upon until
the RI has reviewed and documented a disposition.
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4.2. Named Accountable Officer: The Responsible Individual
The Named Accountable Officer under this profile is the Responsible
Individual: the named person who bears documented accountability for
the AI/ML-assisted housing finance decision. Required fields:
* ri_employee_id: stable identifier consistent throughout the
relevant loan warranty period;
* ri_role: role in the AI/ML governance or decision chain (e.g.,
"underwriter", "credit_officer", "AUS_governance_lead");
* ri_review_timestamp: ISO 8601 UTC of the RI's review action;
* ri_decision: one of PROCEED_WITH_AI_RECOMMENDATION,
PROCEED_MODIFIED, OVERRIDE, DENY_APPLICATION, APPROVE_APPLICATION,
REFER_TO_MANUAL_UNDERWRITING;
* ri_decision_basis: REQUIRED for all values other than
PROCEED_WITH_AI_RECOMMENDATION.
4.3. Watchtower Definitions
4.3.1. WT-FHFA-01: Housing Finance Decision Floor Gate
*Trigger:* Composite Confidence Score falls below the housing finance
decision floor. For AUS: a Refer or Refer with Caution
recommendation signals the loan is outside AUS approval parameters.
*Action:* FORCE_ASSISTED. RI reviews the AI/ML recommendation before
any credit action. Loan file MUST reflect the RI's documented
review.
*Rationale:* FHFA Bulletin 2025-16 requires human oversight of AI/ML
decisions below defined confidence thresholds. An AUS Refer
recommendation is itself a signal that human underwriting review is
required.
4.3.2. WT-FHFA-02: Fair Lending Override Gate
*Trigger:* AI/ML recommendation involves a borrower demographic
profile, geographic area, or loan characteristic identified in the
entity's fair lending analysis as requiring heightened review for
potential disparate impact under ECOA or the Fair Housing Act.
*Action:* FORCE_ASSISTED for standard heightened review. HARD_BLOCK
where the AI/ML recommendation conflicts with a pre-identified fair
lending risk pattern in the entity's corrective action plan.
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*Rationale:* ECOA and the Fair Housing Act prohibit disparate impact
in mortgage credit decisioning. WT-FHFA-02 ensures loan applications
in identified heightened-review categories receive documented human
oversight, sealed in the Audit Trace for FHFA examination.
4.3.3. WT-FHFA-03: Fair Lending Flag Gate
*Trigger:* Ongoing HomeMark Audit Trace monitoring identifies that
the AI/ML recommendation falls within a demographic or geographic
segment exhibiting an approval rate or pricing disparity above the
entity's configured fair lending alert threshold.
*Action:* FORCE_ASSISTED. fair_lending_flag set to true. RI review
and decision basis REQUIRED.
*Rationale:* Continuous per-decision fair lending monitoring enables
entities to identify emerging disparate impact before it reaches the
threshold of a CFPB or FHFA examination finding. WT-FHFA-03 converts
a periodic audit obligation into a continuous per-decision flag.
4.3.4. WT-FHFA-04: AVM / AUS Training Limitation Gate
*Trigger:* Property or loan characteristics match a known validation
limitation of the AVM or AUS model (e.g., property type with limited
comparable sales data; geographic market where the AUS was not
validated; loan product feature outside the validated operating
envelope).
*Action:* FORCE_ASSISTED. HomeMark Audit Trace records the specific
training limitation triggered and the RI's disposition.
*Rationale:* GSE model risk governance frameworks require entities to
document model limitations and ensure decisions outside the validated
envelope receive human review. WT-FHFA-04 gives this requirement
structural enforcement at the per-decision level.
4.3.5. WT-FHFA-05: Model Performance Anomaly Gate
*Trigger:* AI/ML recommendation deviates from expected operating
parameters suggesting model degradation, distributional shift, or
data quality failure.
*Action:* FORCE_ESCALATED plus model performance anomaly alert to the
entity's model risk governance team.
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*Rationale:* AI/ML models in housing finance can experience
distributional shift as housing market conditions evolve. Early
detection prevents systematic portfolio-level impact from a degraded
model operating at scale.
4.3.6. WT-FHFA-06: R&W Eligibility Verification Gate
*Trigger:* For loans destined for GSE sale: the AI/ML recommendation
or loan data presents a characteristic requiring specific
verification for GSE representation and warranty compliance (e.g.,
loan type requiring additional documentation; data field outside AUS
verified input range; characteristic identified in recent GSE quality
control findings as a common R&W breach source).
*Action:* FORCE_ASSISTED. RI verifies eligibility before the loan
proceeds to GSE sale. HomeMark Audit Trace records eligibility
verification and RI confirmation.
*Rationale:* GSE R&W obligations require originators to represent
that AUS input data was accurate. WT-FHFA-06 creates a sealed per-
loan eligibility verification record supporting R&W compliance and
providing evidence in any subsequent repurchase demand.
4.4. Audit Trace Schema Extensions
The following fields are REQUIRED under the HomeMark profile, in
addition to core fields in [I-D.veridom-omp] Section 7:
* ri_employee_id: string, REQUIRED for Consequential Housing Finance
Decisions.
* ri_role: string, REQUIRED.
* ri_review_timestamp: string, ISO 8601 UTC, REQUIRED for ASSISTED
and ESCALATED.
* ri_decision: string, REQUIRED for ASSISTED and ESCALATED. One of:
PROCEED_WITH_AI_RECOMMENDATION, PROCEED_MODIFIED, OVERRIDE,
DENY_APPLICATION, APPROVE_APPLICATION,
REFER_TO_MANUAL_UNDERWRITING.
* ri_decision_basis: string, OPTIONAL for
PROCEED_WITH_AI_RECOMMENDATION; REQUIRED for all other values.
* loan_identifier: string, REQUIRED. Unique loan number or mortgage
ID, enabling per-loan audit trail retrieval for FHFA examination,
R&W review, and fair lending investigation.
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* ai_ml_system_id: string, REQUIRED. Identifier of the AI/ML system
(e.g., "DU_v11.0", "LPA_v5.2").
* ai_ml_system_version: string, REQUIRED. Version in effect at time
of interaction. Critical for model risk governance and R&W
compliance.
* ai_ml_recommendation: string, REQUIRED. The AI/ML output: for
AUS, one of "approve_eligible", "refer", "refer_with_caution",
"ineligible"; for AVM, the estimated value and confidence
interval; for servicing, the recommended loss mitigation option.
* fair_lending_flag: boolean, REQUIRED. True if WT-FHFA-02 or WT-
FHFA-03 triggered.
* fair_lending_basis: string, REQUIRED if fair_lending_flag is true.
The demographic or geographic basis for the flag.
* housing_decision_category: string, REQUIRED. One of:
"mortgage_origination", "automated_valuation", "loan_servicing",
"loss_mitigation", "secondary_market_acquisition".
* rw_eligibility_verified: boolean, REQUIRED for loans destined for
GSE sale. True if WT-FHFA-06 evaluated and confirmed R&W
eligibility.
* fhfa_bulletin_version: string, REQUIRED. Set to "FHFA-2025-16"
for deployments under Bulletin 2025-16.
* profile_version: string, REQUIRED. MUST be "VERIDOM-HOMEMARK-
v1.0".
5. Representation and Warranty Evidence Architecture
The GSE R&W framework creates a retrospective evidence requirement:
years after origination, lenders may face repurchase demands
requiring demonstration that AI/ML was used correctly. HomeMark
Audit Traces provide three specific R&W properties: contemporaneity
(RFC 3161 timestamp proves the Audit Trace was generated at
origination, not retrospectively); input data integrity
(interaction_hash proves the AUS input data has not been altered);
and AI/ML system version documentation (ai_ml_system_id and
ai_ml_system_version prove which AUS version was in effect at
origination).
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Lenders delivering loans to GSEs SHOULD generate HomeMark Audit
Traces for all AUS-assisted originations and retain them for the full
R&W warranty period (typically seven years from the note date or loan
payoff, whichever is later).
6. Fair Lending Evidence Package
The HomeMark profile generates per-loan fair lending evidence (each
Audit Trace contains fair_lending_flag and fair_lending_basis) and
aggregate fair lending evidence (the Audit Trace stream can be
aggregated to compute approval rates by demographic segment,
disparate impact ratios, and pricing disparities for FHFA Bulletin
2025-16 monitoring and HMDA analysis).
The Fair Lending Evidence Package for a defined loan portfolio MUST
contain: all sealed HomeMark Audit Traces organised by
housing_decision_category and ai_ml_system_id; aggregate approval
rate data by fair lending segment; disparate impact ratio
calculations; count and disposition of WT-FHFA-02 and WT-FHFA-03
triggers; chain integrity proof (SHA-256 Merkle root); and RFC 3161
TimeStampToken verification from the OMP Reference Validator
[OMP-OPEN-CORE]. FHFA examiners can verify completeness and
integrity without relying on the entity's reconstructed data.
7. The HomeMark Invariant
Implementations of this profile MUST satisfy the following two-
property invariant:
* Property 1 (Housing finance decision accountability completeness):
Every Consequential Housing Finance Decision MUST generate a
sealed HomeMark Audit Trace containing: the AI/ML recommendation;
the RI's identity and review timestamp where ASSISTED or
ESCALATED; the RI's decision and basis where required; the Fair
Lending Flag evaluation; the AI/ML system identity and version;
and R&W eligibility verification where applicable.
* Property 2 (Immutable trail): The HomeMark Audit Trace MUST be
sealed with the three-layer integrity architecture defined in
[I-D.veridom-omp] Section 7. Any modification to any historical
Audit Trace record MUST be detectable by FHFA examiners, GSE
counterparties, or any third-party auditor without access to the
entity's or OMP implementer's infrastructure.
An entity satisfying the HomeMark Invariant can demonstrate, for any
Consequential Housing Finance Decision: the AI/ML recommendation and
input data; the AI/ML system identity and version; the RI's identity
and review timestamp; the RI's decision and independent basis; the
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Fair Lending Flag status; R&W eligibility verification where
applicable; and that the record has not been altered since sealing.
This satisfies the transparency, accountability, and model risk
governance requirements of FHFA Bulletin 2025-16, the fair lending
examination evidence standards of ECOA and the Fair Housing Act, and
the R&W compliance evidence standards of the GSE selling and
servicing frameworks.
8. Security Considerations
The security considerations of [I-D.veridom-omp] apply in full.
Borrower data sensitivity: HomeMark Audit Traces contain borrower PII
and financial data subject to GLBA privacy requirements. Operators
MUST implement GLBA-compliant safeguards. Fair lending demographic
data used in WT-FHFA-02 and WT-FHFA-03 MUST be segregated from credit
decision data consistent with ECOA's prohibition on using protected
characteristics in credit decisions.
AI/ML system version integrity: The ai_ml_system_version field is a
critical R&W and model risk governance element. Operators MUST
implement controls ensuring the version recorded matches the AUS or
AVM version actually in effect at decision time. Version
misrepresentation is a material R&W compliance issue.
Loan identifier uniqueness: The loan_identifier field MUST be
globally unique within the operator's deployment. Duplicate
identifiers would create ambiguity in per-loan evidence retrieval and
undermine R&W compliance documentation.
RI identity integrity: ri_employee_id MUST reflect the individual who
actually reviewed or was accountable for the AI/ML-assisted decision.
Operators MUST implement technical controls preventing RI identity
assignment without the relevant individual's authenticated action.
9. IANA Considerations
This document has no IANA actions.
10. References
10.1. Normative References
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[I-D.veridom-omp]
Adebayo, T., Apalowo, O., and F. Makanjuola, "Operating
Model Protocol (OMP): A Deterministic Decision-Enforcement
Protocol with Externalized Proof-of-Integrity", Work in
Progress, Internet-Draft, draft-veridom-omp-00, March
2026, <https://datatracker.ietf.org/doc/html/draft-
veridom-omp-00>.
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119, March 1997,
<https://www.rfc-editor.org/info/rfc2119>.
[RFC3161] Adams, C., Cain, P., Pinkas, D., and R. Zuccherato,
"Internet X.509 Public Key Infrastructure Time-Stamp
Protocol (TSP)", RFC 3161, August 2001,
<https://www.rfc-editor.org/info/rfc3161>.
[RFC8174] Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC
2119 Key Words", BCP 14, RFC 8174, May 2017,
<https://www.rfc-editor.org/info/rfc8174>.
[RFC8785] Rundgren, A., Jordan, B., and S. Erdtman, "JSON
Canonicalization Scheme (JCS)", RFC 8785, June 2020,
<https://www.rfc-editor.org/info/rfc8785>.
10.2. Informative References
[ECOA] U.S. Congress, "Equal Credit Opportunity Act, 15 U.S.C.
1691 et seq.", 1974.
[FHA-1968] U.S. Congress, "Fair Housing Act, 42 U.S.C. 3601 et seq.",
1968.
[FHFA-2025-16]
Federal Housing Finance Agency, "Bulletin 2025-16:
Artificial Intelligence Governance Framework for the
Enterprises and Federal Home Loan Banks", March 2026.
[I-D.veridom-omp-employ]
Adebayo, T., Apalowo, O., and F. Makanjuola, "OMP Domain
Profile: Automated Decision Systems Accountability in
Employment Under California FEHC CRC Regulations, New York
City Local Law 144, and Related ADS Accountability
Obligations", Work in Progress, Internet-Draft, draft-
veridom-omp-employ-00, April 2026,
<https://datatracker.ietf.org/doc/html/draft-veridom-omp-
employ-00>.
Adebayo, et al. Expires 7 October 2026 [Page 13]
Internet-Draft OMP FHFA Housing Finance Profile April 2026
[I-D.veridom-omp-euaia]
Adebayo, T., Apalowo, O., and F. Makanjuola, "OMP Domain
Profile: EU AI Act Article 12 Logging and Traceability
Requirements for High-Risk AI System Operators", Work in
Progress, Internet-Draft, draft-veridom-omp-euaia-00,
April 2026, <https://datatracker.ietf.org/doc/html/draft-
veridom-omp-euaia-00>.
[OMP-OPEN-CORE]
Veridom Ltd, "OMP Open Core: Reference Validator and
Schema Library", Apache 2.0,
https://github.com/veridomltd/omp-open-core, 2026.
[SR-11-7] Board of Governors of the Federal Reserve System and
Office of the Comptroller of the Currency, "Guidance on
Model Risk Management (SR 11-7 / OCC 2011-12)", April
2011.
[ZENODO-OMP]
Adebayo, T., Apalowo, O., and F. Makanjuola, "OMP --
Operating Model Protocol: A Deterministic Routing
Invariant for Tamper-Evident AI Decision Accountability in
Regulated Industries", Zenodo DOI 10.5281/zenodo.19140948,
March 2026.
Authors' Addresses
Tolulope Adebayo
Veridom Ltd
London
United Kingdom
Email: tolulope@veridom.io
Oluropo Apalowo
Veridom Ltd
Awka
Nigeria
Email: ropo@veridom.io
Festus Makanjuola
Veridom Ltd
Toronto
Canada
Email: festus@veridom.io
Adebayo, et al. Expires 7 October 2026 [Page 14]