Fair selection starts with measuring what matters, in a way that is equivalent for every candidate. Bias and fairness are key concepts in this regard. Bias refers to systematic distortion in outcomes or decision-making; fairness is about the extent to which those outcomes are just and equal for different groups. This article explains what forms of bias can occur in skill tests, how fairness can be measured, and what technical and procedural controls organizations can use to test and guarantee the fairness of their selection tools. It also explains how this is organized in practice in testing environments such as Selection Lab: with standardized measurements, psychometric controls, and transparent reports, focused on objectivity and reproducibility.
What do we mean by bias and fairness in skill tests?
- Bias refers to any systematic error that leads to scores or decisions treating candidates unequally, regardless of their actual ability or suitability. Bias can arise in test content, item wording, interface, standardization, scoring, cut-offs, and interpretation. There are usually several sources of bias...
- Fairness is the property of a test and the decision-making process that gives candidates comparable opportunities with equal underlying ability, regardless of characteristics such as gender, age, or migration background. Fairness is therefore both a psychometric and a procedural issue.
How do you assess fairness? A pragmatic four-layer framework
1. Item level
At the item level, you test whether individual questions "work differently" for different groups, while the underlying skill level is the same. This is done using DIF (Differential Item Functioning) analyses: for dichotomous items, Mantel-Haenszel and logistic regression are commonly used, whereby you explicitly distinguish between uniform DIF (structural advantage/disadvantage) and non-uniform DIF (the effect differs per skill level). For polytomous items or more complex scales, you often use IRT-based DIF, comparing item parameters (such as discrimination and thresholds) between groups. Items with robust DIF signals are reformulated or removed, as they are likely to measure something other than the intended skill (at least in part).
At Selection Lab, you can translate item-level findings into practical design choices that reduce the likelihood of item bias, such as combining different assessment formats (video, games, hard skills) so that you are not dependent on a single format that unintentionally favors one group. In addition, it helps not to view items in isolation from the assessment context: by first allowing candidates to get used to the format (practice-like entry) and by giving short, clear instructions, you prevent "interface dexterity" from influencing the answer per item. This is in line with Selection Lab's emphasis on frictionless, candidate-friendly formats such as mobile-oriented game assessments.
2. Test and scale level
At the test level, you check whether the test as a whole measures the same construct for groups and measures it with the same precision. You report reliability per subgroup, for example omega and, where possible, test-retest correlations, because large differences can indicate instability or differential measurement precision.
You then test measurement invariance with multi-group CFA: first configural (same factor structure), then metric (equal factor loadings), and then scalar (equal intercepts), using changes in fit indices such as ΔCFI/ΔRMSEA to assess whether the equality restrictions are tenable. For tests that are IRT-suitable, calibrate items on a single latent scale and check parameter stability per group; in addition, perform timing and device analyses to see whether speed or error patterns per device deviate in a way that is not construct-relevant.
At Selection Lab, you make this concrete by explicitly linking the chosen skill tests to role requirements and by combining multiple measurement sources, so that the construct measurement is less "single-source" and therefore more robust. In practice, this means that if device analyses show that a component performs differently on mobile than on desktop, you can solve the problem by adjusting the interface or by reweighting the mix (more task-related hard skills, fewer speed-sensitive components), instead of "explaining away" groups.
3. Decision level
At the decision level, you look not only at scores, but at what scores do in your funnel. You calculate adverse impact ratios on throughput (preferably with confidence intervals, because small samples give unstable ratios) and you simulate alternative cut-offs to see how sensitive your outcomes are to threshold choices. Then you test predictive fairness: you relate test scores to later outcomes (e.g., onboarding success or role KPIs) per group and examine whether regression lines are comparable in slope and intercept. Calibration curves per score decile show whether "the same score" represents the same chance of success; error profile analyses (false positives/false negatives) reveal whether one group is systematically rejected or accepted more often than it should be.
Selection Lab supports this type of decision-making primarily by bringing structure and explainability to the follow-up steps, so that decisions are less dependent on intuition after the test. The AI-generated interview guides are explicitly intended to conduct structured, role-specific interviews, which helps to consistently translate scores into evidence in conversations and to reduce "anchoring on a single score." In addition, you can use objective assessment data earlier in the process, allowing you to track and adjust for adverse impact at the first measurement points, rather than only discovering it after subjective CV filters.
4. Process level
You can assess process fairness by checking whether the assessment conditions and candidate experience are equivalent: standardized instructions, fixed sequences, controlled timing, and accessibility options where appropriate. You monitor start and completion rates, drop-off per step, device effects, and technical incidents, because unevenly distributed drop-off can be a fairness signal (not just a conversion problem). For high-stakes situations, you can use proctoring to ensure score integrity, but you must always monitor the trade-off between integrity and new barriers (privacy, tech requirements) and organize a clear retake and appeal process in case of technical issues.
Step-by-step plan for testing fairness in your organization
- Define fairness goals: specify which fairness dimensions you consider important (e.g., measurement invariance, adverse impact below 0.80, prediction equality) and set thresholds and evaluation intervals.
- Collect representative data: ensure sufficient sample size per subgroup and record context variables (device, time, language proficiency, professional experience) for later corrections.
- Perform item and scale analyses: start with DIF screening and measurement invariance; revise or replace suspicious items; confirm reliability per subgroup.
- Simulate decision rules: compare alternative cut-offs, banding, and combinations with other selection components; assess impact on diversity and predictive validity.
- Validate predictive: link test data to performance indicators; check regressions per group and calibration of success probabilities.
- Monitor and improve: publish periodic fairness reports, set maintenance cycles for item banks and standards, and ensure an escalation process for adjustments.
Fairness in skill tests can be assessed and controlled when approached systematically: start with construct purity, test each item for DIF, ensure measurement invariance at scale level, evaluate adverse impact and predictive equality at decision level, and monitor the whole process continuously. In a professionally designed environment, such as at Selection Lab, these steps are combined with standardized administration, objective scoring, and structured interviews, so that candidates with equal abilities are given equal opportunities. This results in a selection process that is both fairer and more predictive.
