ai-fairness-lenses

name	ai-fairness-lenses
description	Use FIRST when evaluating, auditing, or debating whether an AI/ML personnel assessment is "fair" or "unbiased" — to define and defend which meaning of fairness/bias applies before drawing conclusions. Covers the three lenses from Landers & Behrend (2023): individual attitudes (distributive/procedural/ interactional justice), legality-ethicality-morality, and technical domain-embedded meanings (statistics vs. machine learning vs. psychometrics). Triggers: "is this AI hiring tool fair/biased", "what does bias mean here", "algorithmic fairness", "disparate impact vs measurement bias in AI", "bias-variance tradeoff", "define fairness for the audit".
version	1.0.0
author	OpenMatter-Network
license	MIT
category	research
tags	["Community","io-psychology","ai-assessment","auditing"]
permissions	[]

AI fairness lenses

Before you can audit an AI assessment for "fairness" or "bias," you must define which meaning you are using and defend it. Fairness and bias have *very different meanings for different audiences and disciplines. Failing to articulate the precise standard "can render the results of an audit uninterpretable across disciplinary lines." This skill gives you the three lenses to choose among and combine.

Critically: the same word splits. A critic who calls an AI "unfair" may mean what another person calls "bias"; and "bias" is itself loaded. Name the lens explicitly in any audit.

Lens 1 — Individual attitudes (justice theory)

The most-invoked lens in public discourse. Use organizational justice (a tripartite perception framework — cognitive, perceptual, emotional) to structure "this feels unfair" claims:

• Distributive justice — perceived fairness of outcomes (who gets hired). Judged against rules of equality (same outcome to all), need (most to those who need most), or equity (outcomes proportional to inputs/contribution). People apply different rules to the same decision, informed by cultural and social values. Most public "AI is unfair" complaints are implicitly distributive.
• Procedural justice — perceived fairness of the rules and procedures used to decide. Five rules often implicated by AI decisions (Ford et al.):
  1. Opportunity to perform — candidate feels they got a fair chance to demonstrate their value.
  2. Job-relatedness / face validity — e.g., do people believe facial expressions in a video interview relate to future performance? (And is evidence of relevance required to justify inclusion?)
  3. Reconsideration / appeal — algorithmic decisions that can't be appealed violate this.
  4. Two-way communication — violated when AI replaces face-to-face human interaction.
  5. Propriety — some simply believe AI-based decisions are morally inappropriate.
• Interactional justice — perceived fairness of the interaction with decision-makers, split into interpersonal (treated with respect/dignity) and informational (given adequate information about the decision and how it was reached). Informational justice is directly affected by transparency about what is assessed and what is done with it; interpersonal justice by the presentation strategy (e.g., an explanatory video before data collection).

Use this lens to predict and diagnose candidate reactions — see ai-claims-and-stakeholder-audit (second-party effects).

Lens 2 — Legality, ethicality, and morality

Fairness as alignment with shared human values and established professional/legal guidelines — "governed by a sense of responsibility to others." Two streams:

• Ethical / moral. A fair system conforms to relevant and established professional guidelines.
  • The APA Ethics Code (2017) five principles bind any psychologist working on such a system: beneficence/nonmaleficence, fidelity/responsibility, integrity, respect for people's rights and dignity (treat people equitably regardless of personal/group characteristics), and justice (address and minimize one's own biases). Here "bias" ≈ a goal of impartiality, lacking individual prejudices and cognitive biases.
  • AI-specific codes — OECD Principles on AI (2019), Universal Guidelines for AI (UGAI, 2018) — reference fair/unbiased decisions but generally don't define them precisely; UGAI names reliability, validity, and data quality and uses bias / discrimination / unfairness interchangeably. These tend to leave "bias" vague so it stays applicable as standards evolve — effectively delegating the technical definition to Lens 3.
• Legal. Laws on discrimination in hiring/housing/admissions often have precise technical definitions built on statistical concepts and case law. For employment, legally establishing test bias generally relies on differential prediction (comparing regression lines across legally defined classes — race, sex, national origin), used to identify the source/justifiability of disparate (adverse) impact (differential selection rates). Contrast differential treatment — explicitly treating class members differently (e.g., awarding bonus points to a group, or modeling class membership as a predictor). Note: advocacy, bills, and even policy often muddle these decades-old distinct concepts, sometimes deliberately leaving "bias" vague.

Lens 3 — Technical domain-embedded meanings of "bias"

All technical definitions share a root: bias = inaccuracy in estimating a population value from sample data, where error splits into random vs. systematic. But the disciplines diverge:

• Statistics — sampling bias. Systematic error in sample representation vs. the population. A performance estimate (e.g., R² = .5) won't generalize if the training sample differs nonrandomly from the target population (the classic undersampled-minorities / facial-recognition database problem). Bias here is a consequence of improper sampling; fix by better/representative sampling or oversampling underrepresented groups (not guaranteed to work in practice).
• Machine learning — the bias-variance tradeoff. ML deliberately introduces bias (e.g., ridge, lasso, elastic net penalize large weights) to reduce overfitting and improve out-of-sample predictive accuracy. Here bias can be positive and desirable — a property of a well-engineered model. Prioritizing "unbiased estimates" above all (as mainstream psychology's low-bias/high-variance procedures do) can hurt out-of-sample prediction. The cost: individual coefficients are no longer cleanly interpretable.
• Psychometrics — measurement bias / invariance. Differences in measurement characteristics across identified groups, commonly assessed as measurement invariance (CFA latent factors) or IRT item parameters. Psychometric bias may or may not be problematic: if a measure is meant to assess a construct on which groups genuinely differ (e.g., educational attainment shaped by systemic opportunity differences), group differences are expected and the test may be biased-but-fair. Note the psychometric caution that differential prediction is not a sufficient condition for bias — a test can show differential prediction without problematic measurement properties (the "six sigma"/manager age example: younger applicants score lower because they've had less exposure, yet if managerial experience is job-related the differential prediction may still be considered fair).

How to use the lenses in an audit

1. State the claim being evaluated and who is raising the fairness concern.
2. Pick the lens(es) that match — concerns may invoke any or all three.
3. Define the standard precisely within that lens (which justice rule? which legal test? which technical bias?) and write it into the audit so conclusions are interpretable.
4. Don't equivocate — a finding of "no measurement bias" (Lens 3) does not answer a distributive or procedural-justice complaint (Lens 1) or a legal disparate-impact question (Lens 2).
5. Carry the chosen standard into the model, stakeholder, and meta audits.

Pitfalls

• Auditing "fairness" without naming a lens → uninterpretable results.
• Treating any group difference as "bias" (psychometrically it may be expected and fair).
• Assuming "bias is always bad" — in ML, intentional bias improves generalization.
• Conflating differential prediction, disparate impact, and differential treatment.
• Answering a procedural/distributive-justice complaint with a purely statistical result.

Checklist

• Fairness concern's source/audience identified
• Lens(es) selected and named in the audit
• Precise standard defined within each lens (justice rule / legal test / technical bias type)
• Cross-lens gaps acknowledged (statistical result ≠ justice answer)
• Standard documented and propagated to downstream audit components

See also

ai-audit-planning · ai-model-outputs-audit (subgroup differences, measurement bias) · ai-claims-and-stakeholder-audit (justice/candidate reactions) · fairness-and-bias-analysis (predictive vs. measurement bias in the Principles)

Source: Landers & Behrend (2023), "Defining Fairness and Bias"; Lenses 1–3; "Contrasting Statistics, Machine Learning, and Psychometrics Perspectives."