Transparency & Bias in Data Communication and Presentation: Impact of Visualization & Design

The complicated interaction of transparency, bias, data visualization, and design in the context of data communication is investigated in this master's thesis. The study used a mixed-methods approach, incorporating surveys and interview discussions, to identify challenges and possibilities for preserving openness in data visualization. It also attempts to uncover common kinds of bias in visual data display and investigates the link between end-user perceptions and optimal data transmission techniques (Tufte, 2001; Heer et al., 2010; Few, 2009; Segaran & Hammerbacher, 2009). The findings provide in-depth insights into the restrictions and opportunities inherent in data visualization and presentation, emphasizing the critical role of design decisions in defining transparency and avoiding bias. Methodologically, the study follows a sequential explanatory mixed-methods design: a quantitative survey (n = 121) is followed by a qualitative interview strand that is analysed thematically and used to explain the survey findings.