Transparency Trust in Predictive Analytics

Abstract

Predictive analytics is increasingly important for businesses seeking to improve decision-making and operational efficiency. However, the widespread adoption of these methods is often impeded by issues related to transparency and trust. Many stakeholders view predictive models as overly complex "black boxes," resulting in skepticism and hesitancy to fully integrate these tools into their processes. This thesis explores the critical challenges that hinder transparency and trust in predictive analytics within business contexts.