A3: Black Hat / White Hat

Due date: May 05 (23:55)

This assignment was largely inspired by a great paper by Micheal Correll and Jeff Heer "Black Hat Visualization" that appeared in 2017. Based on this, Niklas Elmquist created an assignment from which we steal as well as from this adoption at MIT. We hope you enjoy.

Learning objectives

• deepen your knowledge and skills with Tableau
• a critical discourse of how visualizations (and hence the data) can be understood
• a critical discourse on the elements of visual encoding

Instructions

In this assignment, you will visualize a single dataset from a "white hat" and a "black hat" perspective. White hat vs. black hat are terms from computer security, where a white hat hacker is someone who uses their skills for good, i.e., finding vulnerabilities in software and systems to help companies and their customers. In contrast, a black hat hacker uses them for their own (or their organization’s or country’s) gain. More specifically, a white hat visualization would be one where:

• The visualizations are clear and easy to interpret for the intended audience (often the general population);
• Any transformations, filtering, and computations done to the data are clearly and transparently communicated, and
• The sources of the data, including potential bias, are communicated.

A black hat visualization, on the other hand, exhibits one or several of the following characteristics:

• The visual representation is intentionally inappropriate, overly complex, and/or too cluttered for the audience;
• Labels, axes, and legends are misleading; Titles are skewed to influence the viewer’s perception intentionally;
• The data has been transformed, filtered, or processed in an intentionally misleading way; or
• The data's source and provenance are unclear to the viewer.

In addition, we would like you to create two versions of the "white hat" visualization:

• a "neutral" version (Tufte style) respecting aspects such as simplicity, data-ink-ratio, chartjunk, etc.
• an affective version, using pathos, i.e., components where you are appealing to the reader's emotions, where you are trying to make the data relevant to your reader.

For this assignment, you will work with a data set on US Mass shootings between 1982--2023 provided by Mother Jones. We leave it up to you what part of the data you will pick to make your case as a 'black hat' or 'white hat' visualization designer. Since you are visualizing this from two (plus one) different perspectives (white hat vs. black hat), you will be generating three visualizations in total: two "white" and one "black". You are free to use any visualization technique to generate each submission. However, your work has to be done in Tableau. You must make your point (as a black or white hat designer) with one chart or a dashboard. Each visualization should consist of a single page with the following information:

• One or more visualizations;
• Title (short sentence) describing the visualization;
• One-paragraph description of what the visualization shows; and
• Legend (if necessary).

Mark each visualization with "white hat", "affective white hat", or "black hat". The additional information you provide for each visualization depends on whether your visualization is white hat or black hat. For example, if you are creating a white hat visualization, you may want to clearly explain the source of your data, the organization that collected it, and how you have transformed it. For each visualization page, add a short description explaining your motivation and design process in producing this visualization. Be sure to discuss the ethical considerations of your design choices. You will be graded on this description (and whether the visualization is convincing). Please consider aspects such as visual encodings, labels, legends, etc., in addition to data transformations. Some additional considerations:

• For white hat visualizations, it is not sufficient to create a standard visualization and be done with it; you need to actively work to make your visualization as clear and transparent as possible!
• You cannot include blatantly false data for black hat visualizations. You have to be creative in your deceptions. This can be done by playing with the coordinate axis (e.g., scales, etc). You might filter out inconvenient outliers or use favorable color maps and other inappropriate visual encodings.

Most of all, we hope you will have fun with it. Some helpful resources:

• Ge et al., "CALVI: Critical Thinking Assessment for Literacy in Visualizations", CHI 2023
• Correll and Heer, "Black Hat Visualization", Workshop on Dealing with Cognitive Biases in Visualisations (DECISIVe) 2017.
• Lee-Robins and Adar, "Affective Learning Objectives for Communicative Visualizations", IEEE VIS 2022
• Prantl et al. "Passionate Charts: Arguments for Empathetic Emotions in Data Vis", Arxiv 2023

Submission

Please submit a PDF-document (around 3 pages) containing

• Your name and student number
• Three screenshots and appropriate explanations
• A short paragraph reflecting on your experience of designing these three different visualizations

Submit the file via Moodle. Use the following naming scheme for your submission: "<studentnumber>_A3.pdf".

Grading

• 10%: Submitted according to the procedure (PDF with screenshots)
• 15%: for creativity of your Tufte-style white hat vis
• 10%: for your reasoning of your Tufte-style white hat vis
• 15%: for creativity of your affective white hat vis
• 10%: for your reasoning of your affective white hat vis
• 15%: for creativity of your black hat vis
• 10%: for your reasoning of your black hat vis
• 15%: Reflecting on the design process (what was easy/hard)

Late submission

Late Submissions are possible, you have a total of five grace days for all assignments. After these days are used up, remaining assignments must be submitted on time.

Academic Honesty