How Faithful Is Attribution for Sales Forecasting? A Counterfactual Study

This paper investigates the reliability of attribution methods—techniques used to explain AI model predictions—in the context of sales forecasting through counterfactual analysis. The research evaluates whether popular attribution approaches accurately identify which input features truly drive forecasting decisions, finding potential gaps between claimed explanations and actual model behavior.

This paper investigates the reliability of attribution methods—techniques used to explain AI model predictions—in the context of sales forecasting through counterfactual analysis. The research evaluates whether popular attribution approaches accurately identify which input features truly drive forecasting decisions, finding potential gaps between claimed explanations and actual model behavior. The findings are relevant for enterprises deploying interpretable AI in regulated sectors where model explainability and decision traceability are critical compliance requirements.

Legal AI must preserve privilege, satisfy ethics rules, and keep client data within a defensible perimeter. QuettaMinds helps law firms and legal departments deploy AI that is structurally compliant, not just policy-compliant.