From forecasting to trust: Engineering interpretability and accuracy metrics in predictive platforms

This article presents a framework for engineering interpretability and accuracy metrics into predictive forecasting platforms, addressing the trust deficit that emerges when stakeholders must make high-stakes decisions based on opaque predictions. The architecture implements origin tracking through a multi-dimensional data model that distinguishes between machine learning-generated, user-adjusted, and hierarchically aggregated forecasts. A historical accuracy tracking framework captures temporal snapshots, enabling assessment of predictive reliability across different timeframes and organizational levels. The user experience design employs layered information disclosure and structured feedback mechanisms that transform individual domain expertise into institutional knowledge. Empirical assessment reveals a non-linear trust development trajectory as users progress from initial skepticism to collaborative engagement with the system. While the framework successfully enhances transparency and decision confidence, limitations exist in capturing complex collaborative adjustments and addressing qualitative aspects of forecast quality. Potential applications extend to healthcare resource planning, supply chain optimization, financial risk assessment, and public sector planning, with future directions focusing on uncertainty visualization and rhetorical dimensions of forecast presentation.