Debate

• Who: Teams of 2-3
• Objectives: Theory; Research

Overview

The goal of this assignment is for you to build knowledge about research in VIS. This assignment is required for those enrolled in CS 541.

In two teams of 2-3 people, you will be debating opposing viewpoints on a given topic. Importantly, your argument should be supported by research!

Debate Structure

You should be approximately following the debate structure identified here.

Opening:

• Affirmative (A1): Pro Position (3 minutes)
  • 2 minute break (team meeting)
  • Opposing: Rebuttal (2 minutes)
• Opposing (O1): Con Position (3 minutes)
  • 2 minute break (team meeting)
  • Affirmative: Rebuttal (2 minutes)

Statements:

• Opposing (O2): Statements (3 minutes)
  • 2 minute break (team meeting)
  • Affirmative: Rebuttal (2 minutes)
• Affirmative (A2): Statements (3 minutes)
  • 2 minute break (team meeting)
  • Opposing: Rebuttal (2 minutes)

2 min break (team meeting)

Closing:

• Affirmative (A3): Closing (3 minutes)
• Opposing (O3): Closing (3 minutes)

Total time is 36 minutes. The remaining class time will be used to determine a winner. The whole class gets to vote on the winner. I'll also try to get external VIS professors to judge the debates.

Debate How-To

There is a lot of material online on how to debate well. For this class, the focus is not to train you to become a better debater. The only requirement is for you to use publications as evidence for your arguments! Use publications that are not too obscure (i.e. please use reputable conferences or journals). If you’re not sure if a reference is too obscure, contact me. Be prepared! Think of the evidence/publications that your opposing team could use and prepare for those arguments! Work together as a team!

Unlike typical debates, you are welcome -- nay, encouraged -- to use slides.

The two teams should schedule a meeting with me to go over the spirit of the debate. I will suggest possible papers or directions to pursue and answer any questions you might have. This should happen at least 1 week before the debate. You should have some ideas of the 3 key papers that you will use for your arguments.

Debate Topics

There are several possible debate topics to explore.

1. “Design guidelines considered harmful”: Visualization research has always had a strong (visual) design component. As a result, a number of design guidelines have emerged over the years.
   • Affirmative team: Design guidelines are necessary. They help practitioners know how to design effective visualizations. They also serve as the foundation of visualization research.
   • Opposing team: Design guidelines are rigid and have caused a great deal of harm to the community. There is no “good” or “bad” design – designs are inherently contextual and task dependent.
2. “The humans are dead”: This is the debate about automation versus human-user control. Data exploration, data analysis, and decision-making have increasingly become automated. Is a human-user still necessary?
   • Affirmative team: Human users are always necessary. Skynet anyone?
   • Opposing team: Automation is inevitable. Get with the program and start praying to our robot overlords.
3. “To interact or not to interact, that is the question”: Interaction has always been an important aspect of visual data exploration and data analysis. However, there is increasing evidence that interactions can lead to confusion (more options are not always better). In fact, if the purpose of visualization is to communicate important information, why leave it to a user to discover it?
   • Affirmative team: Interactions are necessary. One (static) visualization cannot tell the whole story. A user needs to interact with their visualization to feel engaged and empowered.
   • Opposing team: Interaction do not provide any cognitive benefits but only provides the façade of control. In many cases, taking away the control is the only way to ensure that users get the correct message.
4. “The future of data science is visualization”: Visualization is intrinsically tied to data management, data analysis, and generally “data science.” Often seen as a “tack-on,” traditionally visualization is used at the end of an analysis to generate a plot. However, increasingly visualization is becoming the interface to all of data science.
   • Affirmative team: Data science will become ubiquitous. However, not everyone will be trained to do data science. Visualization holds the key to making data science “usable” by the masses.
   • Opposing team: Statistics, AI, and database will be the way of the future. Visualization will stay as the “applied” interface to AI, ML, Stats, and DB techniques. As such, it can only react to the advances in AI/ML/etc but should not take lead.
5. “The future of visualization is LLMs”: This debate explores whether large language models will fundamentally redefine how we create and interpret visualizations. Advocates argue that conversational AI lowers barriers to entry, enables natural-language querying of data, automates insight discovery, and integrates narrative explanation with charts — shifting visualization from manual design toward AI-mediated sensemaking. Critics counter that visualization depends on human perception, spatial reasoning, and design expertise that cannot be replaced by token-based language models. They also raise concerns about hallucinations, automation bias, and overreliance on AI-generated interpretations. At stake is whether LLMs represent a paradigm shift in visualization or simply a powerful new layer atop existing practices.
   • Affirmative team: The future is in fact LLMs. LLMs represent a paradigm shift that fundamentally changes the way people rely on visualizations.
   • Opposing team: LLMs are a current fad that will fade, as has been the case with many different visualization technologies before it that never took root.
6. “Visualization is inherently political”: This debate asks whether data visualization is fundamentally shaped by power, values, and social context, or whether it can meaningfully aspire to objectivity. The core tension is whether neutrality is a myth or a professional ideal worth striving toward.
   • Affirmative team: Every decision — what data to collect, how to categorize it, what to include or omit, how to scale axes, what to highlight — embeds assumptions that shape interpretation and reflect institutional priorities. Visualizations do not simply display reality; they frame it.
   • Opposing team: Proponents of methodological rigor argue that transparency, ethical standards, and established design principles can minimize bias and support fair, evidence-based communication. .

Debate Assignments

Grading

This assignment is required for CS 541 students. CS 441 students are not eligible to participate in the debate, but should be present to vote on the winner.

• [10%] Selection and relevance of key papers
• [15%] Opening
• [15%] Statement
• [25%] Rebuttal
• [15%] Closing
• [20%] Group Evaluation (did you contribute to your team?)
• [+10%] To the winning team (which team presented the most convincing argument? -- based on Peer Evaluation)