IEEE 7003-2024 — TwinEthos

Informational data, not legal advice. Summaries are TwinEthos's own words and rules have not been reviewed by a lawyer: check the official text before relying on any of it. A guard addresses an item; adding it is not a statement that your code meets any law.

Official text: standards.ieee.org.

Standard / soft law

Systems should document a proxy-variable / algorithmic-bias analysis

IEEE 7003-2024 Clause 5 (bias profiling) · official text · Soft law or guidance (not binding law)

IEEE 7003-2024 (Algorithmic Bias Considerations) provides a development framework to avoid unjustified differential outcomes: selection criteria for bias-validation datasets, establishing and communicating the algorithm's application boundaries, maintaining a 'bias profile' documenting bias-related decisions and mitigations, and user-expectation management (e.g. correlation vs. causation). Detect an algorithmic decision system with no documented bias profile or application-boundary definition. [Tier C: our own-words summary of the public scope; no verbatim standard text stored.]

Who it applies to

Duty falls on: developer
Systems covered: consequential decision
Organisations adopting IEEE 7003 for algorithmic-bias considerations. Voluntary standard.

The guard to add

Organizational artifact to keep (not verifiable from code); the guard is the record, its owner and its upkeep.

Keep a proxy-variable analysis for the decision system listing each input feature, the protected traits it could stand in for, the test run, and the decision taken.

An organizational record owned by the model's accountable owner, with review by whoever handles fairness or legal risk: a feature-risk register (docs/fairness/proxy-analysis.md or a section of the model card). It lists every input feature with its proxy risk, the evidence (correlation with protected attributes or inferred groups), the decision (keep with justification, coarsen, drop), the datasets used for bias validation and why they were chosen, and the system's application boundaries, meaning where it should not be used. It is updated whenever features or training data change and reviewed at each release; a CI check can confirm the record exists and names the current model version.

Where it goes: 2 data models, 12 repository artifacts, 13 tests and evals.

What this provision adds:

Maintain a bias profile of bias-related decisions and mitigations, record selection criteria for bias-validation datasets and the algorithm's application boundaries, and manage user expectations such as correlation versus causation.

Example (Feature-risk register (docs/fairness/proxy-analysis.yaml)), before:

model: tenant_screen_v2
features: [income, zip_code, years_at_address, school]

After:

model: tenant_screen_v2
reviewed: 2026-09-01
owner: risk-modeling-lead
application_boundary: residential rental screening only; not for employment or credit
bias_validation_data: holdout_2026q2 with BISG-inferred race/ethnicity (rationale: docs/fairness/data.md)
features:
  - name: zip_code
    proxy_for: [race, national_origin]
    evidence: strong association with inferred race in holdout
    decision: dropped
  - name: school
    proxy_for: [race, age]
    decision: coarsened to degree_level
  - name: income
    proxy_for: [sex]
    decision: kept; disparity tested, see reports/2026q2-fairness.html

Control: No documented analysis of proxy variables. The same guard addresses 1 item. Engineering guidance, not legal advice.

Related incidents

No guardrail sits on this exact control; these incidents are cited by guardrails on related controls.

Meta's automated moderation over-enforced Arabic and under-enforced Hebrew content (BSR due diligence) (2021-05; disclosed by the operator). An independent human rights due diligence by BSR, commissioned and published by Meta on September 22, 2022, found that during the May 2021 Israel-Palestine escalation Arabic content saw greater over-enforcement per user than Hebrew content and Hebrew content greater under-enforcement. BSR attributes this in part to Meta having an Arabic hostile-speech classifier but no Hebrew one, and to Arabic classifiers likely being less accurate for Palestinian Arabic. BSR found no intentional bias but 'various instances of unintentional bias' with different impacts on Palestinian and Arabic-speaking users. Meta committed to implement 10 of BSR's 21 recommendations and said it had since launched a Hebrew hostile-speech classifier. Source: Meta (operator response, 2022-09-22) · evidence grade: primary · cited by Check AI ranking, pricing, moderation, and ad targeting for disparities when features can stand in for protected traits
Toxicity classifiers rate African American English as more offensive (2019; confirmed). University of Washington researchers reported at ACL 2019 that tweets in African American English and tweets by self-identified African Americans were up to two times more likely to be labelled offensive by hate-speech models trained on widely used datasets, and that Jigsaw's public Perspective API showed similar racial bias, rating AAE phrases as more toxic than non-AAE equivalents. Source: Sap et al., 'The Risk of Racial Bias in Hate Speech Detection', ACL 2019 (original researchers) · evidence grade: primary · cited by Check AI ranking, pricing, moderation, and ad targeting for disparities when features can stand in for protected traits
Ride-hailing fares higher in Chicago neighborhoods with more non-white residents (researcher audit) (2018-11; alleged (not proven)). George Washington University researchers analysing Chicago's public data on more than 100 million ride-hailing trips from November 2018 to September 2019 report that trips in neighborhoods with larger non-white populations, higher poverty, younger residents and more college-educated residents were significantly associated with higher fares. They attribute this to pricing algorithms learning from demand, supply and trip duration. The finding is an observational association from census-tract data; the pricing models themselves were not examined. Source: Pandey & Caliskan, 'Disparate Impact of Artificial Intelligence Bias in Ridehailing Economy's Price Discrimination Algorithms', AIES 2021 (original researchers) · evidence grade: primary · cited by Check AI ranking, pricing, moderation, and ad targeting for disparities when features can stand in for protected traits
Google ads suggesting arrest records served more often for Black-identifying names (2012; confirmed). Harvard researcher Latanya Sweeney searched 2,184 racially associated full names on google.com and reuters.com (a Google AdSense host) from September 24 to October 23, 2012 and found ads suggestive of an arrest record appeared more often for Black-identifying first names; on reuters.com a Black-identifying name was 25% more likely to get such an ad (statistically significant). Ads appeared regardless of whether the name had an arrest record in the advertiser's database. The paper does not determine whether the advertiser's templates or Google's click-based ad optimization caused the pattern; the advertiser, Instant Checkmate, told the author it gave Google the same ad text for groups of last names. Source: Sweeney, 'Discrimination in Online Ad Delivery' (original researcher, 2013-01-28) · evidence grade: primary · cited by Check AI ranking, pricing, moderation, and ad targeting for disparities when features can stand in for protected traits

Rule id ieee-7003.proxy-variable-analysis · review status: tier c citation only