Combatting Bias via Contextual Visualization

Research projects within this theme examine and evaluate new visual analytics methods that aim to reduce threats to validity that arise from various bias effects during exploratory visual analysis. These threats often arise from unseen effects of contextual information—such as shifts in data distributions during data selection or assumptions during cognitive activity–which are often ignored within traditional visual analytics approaches.