Glossary

Compensation • 17AI • 21

• A

  Adjusted Pay Gap

  The difference in pay between groups after statistically controlling for variables that influence compensation such as job title, seniority, performance ratings, education, geography, and experience. This metric isolates pay disparities potentially caused by bias or discrimination rather than structural factors.

• Algorithmic Pay

  Algorithmic pay involves the use of algorithms or automated systems to determine or influence compensation decisions, often involving data-driven analysis and calculations to ensure consistency and objectivity.

• At-Risk Compensation

  At-risk compensation components are subject to a certain level of risk and performance conditions, such as bonuses, commissions, or equity-based rewards, dependent on achieving specific targets or goals.

• B

  Bias

  Systematic error that leads a model to make unfair predictions. In pay-equity contexts, algorithmic bias can occur if historical compensation data reflect past discrimination, perpetuating unequal outcomes.

• Bonus Gap

  The difference in bonus payments awarded to employees of different groups, typically reported separately from base pay. This measure highlights disparities in additional compensation elements such as bonuses or incentive pay.

• Broad Base Compensation

  Broad base compensation is a compensation strategy that focuses on providing competitive and equitable compensation across various job levels and employee groups within an organization, ensuring a fair distribution of rewards.

• C

  Classification

  A supervised learning task where a model assigns items into predefined categories. For example, classifying employees as "high-potential" vs. "low-potential" based on past performance data.

• Clustering

  An unsupervised technique that groups similar data points together. HR might use clustering to segment employees by engagement survey responses or performance metrics without pre-assigned labels.

• Compa-Ratio

  Compa-ratio is a measure used to compare an employee's current salary to the midpoint or target salary within their assigned pay range, expressed as a ratio or percentage. It helps assess how an employee's pay aligns with internal salary structures.

• Comparable Worth

  A principle stating that different jobs with equivalent value to the organization (measured in skills, effort, responsibility, and working conditions) should be compensated similarly, even if the job duties differ significantly.

• Control Variable

  An attribute held constant in statistical models to isolate the effect of other factors. In equal-pay regression, common controls include job level, location, and tenure to see if gender still predicts salary differences.

• Cost of Labor

  Cost of labor refers to the expenses associated with employing workers, including wages, benefits, taxes, and other direct and indirect costs related to labor.

• Cross-Validation

  A method for evaluating model performance by splitting data into multiple training/test folds. It helps ensure that a compensation-prediction model generalizes well beyond the original dataset.

• D

  Deep Learning

  A subset of machine learning using multi-layer neural networks to learn complex patterns. In HR, deep learning can power everything from resume-screening tools that parse unstructured text to pay recommendations based on market and internal data.

• E

  Equal Pay Audit

  A systematic analysis conducted by organizations to assess compensation practices, identify unjustifiable pay disparities, and develop corrective actions to ensure compliance with pay equity laws or standards.

• F

  Feature

  An input variable used by an algorithm to make predictions—e.g., years of experience, education level, performance rating.

• Feature Importance

  A metric indicating how much each feature contributes to a model's predictions. Helps compensation analysts understand which factors (e.g., department, tenure) drive pay disparities.

• G

  Gender Pay Reporting

  Mandatory or voluntary disclosure of aggregated pay data by gender, required by governments or regulatory bodies. Examples include annual reporting mandated by the UK's Gender Pay Gap regulations or Australia's Workplace Gender Equality Agency (WGEA).

• I

  Intersectional Pay Gap

  Disparities in pay observed when accounting for overlapping identities such as gender, race, ethnicity, sexual orientation, disability, or age. Intersectional analyses highlight how multiple dimensions of identity compound disadvantages in pay.

• L

  Label

  The "ground truth" outcome a model learns to predict—e.g., actual salary or bonus awarded.

• M

  Model

  A mathematical representation trained on data to forecast outcomes. Examples include linear regression models for pay prediction or decision-tree models for turnover risk.

• O

  Occupational Segregation

  The distribution of demographic groups disproportionately across different occupations or job levels, often a significant driver of unadjusted pay gaps due to systematic clustering of certain groups in lower-paid roles.

• Overfitting

  When a model learns noise in the training data instead of the underlying pattern, performing poorly on new data. In compensation analytics, an overfit model might "explain" pay gaps by trivial quirks rather than true drivers.

• P

  P-Value

  In hypothesis testing, the probability of observing an effect at least as extreme as the one in your data if the null hypothesis were true. A low p-value (e.g., < 0.05) suggests a statistically significant pay disparity after controls.

• Pay Equity or Equal Pay

  What women are paid vs their direct male peers, statistically adjusted for factors such as job, seniority, and geography. Often referred to in the context of "equal pay for equal work."

• Pay Gap or "Median Pay Gap"

  The difference between median earnings of women and men working full-time, expressed as a percentage of men's median pay. This unadjusted figure does not account for differences in roles, experience, or other influencing factors. For instance, in the U.S., women earned approximately 85¢ per every dollar earned by men in 2024 according to OECD standards.

• Pay Gap Quartiles

  Method used to report pay distributions by dividing an organization's workforce into four equal-sized groups ranked by pay. Quartile analyses help identify whether certain demographics (e.g., women) are disproportionately represented in lower-paid quartiles.

• Pay Parity

  The condition in which employees performing similar work receive equivalent compensation, typically measured after adjustments for role, level, geography, and relevant experience. Pay parity is often an organizational goal in achieving true pay equity.

• Pay Transparency

  Practices or policies aimed at openly sharing salary information within organizations, promoting accountability and fairness. This may include disclosing salary bands, providing salary ranges in job postings, or internally publishing pay scales.

• R

  R-Squared

  The proportion of variance in the dependent variable explained by the model. In pay models, a higher R² means your chosen factors account for more of the salary variation.

• Regression

  A statistical method estimating the relationship between a dependent variable (e.g., salary) and one or more independent variables (e.g., job level, performance score). Core to adjusted pay-gap analysis.

• S

  Simpson's Paradox

  A phenomenon in which a trend that holds within multiple subgroups of data reverses when those groups are combined into an aggregate. In a pay-equity audit, failing to stratify by department (or other key controls) can mask true within-group equity—or falsely suggest a disparity—if Simpson's Paradox is at play.

• Supervised Learning

  ML techniques that train models on labeled data (features + known outcomes). Used for tasks like predicting which employees are likely to leave or what bonus level to assign.

• T

  Test Data

  A subset of your dataset held back from training to evaluate model accuracy. Ensures your compensation-prediction model works on unseen data.

• Training Data

  The dataset on which a model is trained. In pay modeling, this would include historical salary and associated features.

• U

  Unsupervised Learning

  ML methods that infer structure from unlabeled data, like clustering. Useful for exploratory analyses, such as discovering natural employee cohorts.

• W

  Weights

  Numeric coefficients that a model assigns to features, indicating their impact on the prediction. In a regression of salary, a higher weight on "years of experience" means it has greater influence on pay estimates.