Ethics in data storytelling isn’t just about technical accuracy; it’s about the choices we make in how we present the truth. Even when Power BI reports and dashboards are technically accurate, they can, in some cases, shape interpretation in ways that lead audiences toward misleading conclusions.

This session examines how interpretive risk emerges in Power BI visualisations despite correct datasets, calculations, and metrics. It focuses on two primary mechanisms that shape interpretation: sampling bias and the framing effect. You will see how sampling bias determines what data exists to be shown before visual design begins, and how framing effects shape interpretation through emphasis and context.

The session then addresses analytical responsibility in Power BI report development. Ethics is treated not as a final validation step, but as a series of decision points embedded throughout the design process. Attendees will learn how to surface data limitations responsibly, identify framing effects that shape audience interpretation and belief without altering the underlying data, and evaluate whether a report informs decision‑making fairly or unintentionally steers interpretation.

This session is intended for Power BI developers, data analysts, and report designers who want to reduce interpretive bias and strengthen trust in technically correct reporting.