Attrition Prediction Models
Learn how AI models predict employee attrition risk, explore the key factors that drive turnover, and build intervention plans that actually reduce it.
Risk Factor Explorer
Explore the top predictors of employee attrition. Click any factor to see its relative weight in prediction models, data source, and correlation strength.
Employee Risk Dashboard
A mock dashboard showing sample employees with AI-generated risk scores, key drivers, and recommended interventions. Click any employee to expand their profile.
Model Explainability
Understanding how a prediction model reaches its conclusions is essential for trust and ethical use. Explore the key components of an explainable attrition model.
Intervention Planner
Select the risk factors you want to address and get suggested interventions with estimated impact, cost, and timeline.
Key insight: The ethical line between prediction and surveillance is the most critical guardrail in attrition modelling. Employees must know that data is being used, predictions must never be used punitively, and individuals should not be penalised for a statistical probability. Best practice requires transparency about what data feeds the model, giving employees access to their own risk factors, and ensuring predictions trigger supportive interventions rather than management suspicion. Without these guardrails, attrition models erode the very trust they aim to protect.