About Me

The challenge of comprehending philosophical (and other) arguments in their entirety is an essential driver of my interests. Often, I am under the impression that philosophical arguments involve some trickery or even magic, which I want to unveil. Fortunately, I got acquainted with applied formal logic and argument mapping, which provide precise tools to understand and evaluate arguments. These tools have become the connecting thread that permeates my interests and scientific work.

Currently, I am especially intrigued by the following questions:

1. Norms of Public Debate and Deliberation: How should we (as a society) shape public debate and deliberation in general? Which role should deliberation play in our collective decision making?
2. Measuring Public Debate: How can we employ argumentation-theoretic tools to understand better what happens in public debate?
3. Argumentation and Large Language Models: How can we use Large Language Models (LLMs) to analyse and foster public debate?

Norms of Public Debate and Deliberation

Public debate can be defined as the public exchange of diverging views about concrete political issues. It takes place through mass media, social media, democratic institutions, political events, and academic publications, to name a few. The concept of deliberation denotes rational argumentation aimed at collective decision-making. Deliberation is closely connected to the idea of deliberative democracy, in which rational argumentation among citizens plays a central role in democratic self-government. In a deliberative democracy, preference aggregation (in the form of voting) is only one part of collective decision making. Equally important is a reasonable exchange of arguments and an open-mindedness to revise one's view in the face of new information and sound arguments.

Both concepts (public debate and deliberation) are connected: Public debate often involves the exchange of reasons and arguments and, at least, indirectly influences the collective decision making process in a democratic society. In other words, public debate is frequently a form of deliberation. Political decisions can be sound or poor since they have ramifications for the well-being of many people (sometimes ranging over multiple countries and generations). Therefore, we should carefully consider how we shape public debate and deliberation to maximise well-being.

Ideally, participants in public debate discuss their views truthfully and faithfully to facts, respect one another and are willing to revise their views in light of new evidence; ideally, their discussion is constructive, reciprocal, and rational; ideally, everyone can participate equally. While these aspirations are laudable, it is unclear what they demand in specific contexts and how to resolve conflicts and tradeoffs between them (and other constraints).

Let's take the context of political talk shows as one specific example. Clearly, talk shows are part of the public debate. However, they are also part of an entertainment industry with a legitimate interest in maximising profit. Does the media have any obligations that talk shows come close to the deliberative ideal? Should the state regulate the media in some way? Who should and shouldn't be invited to political talk shows? Should the whole spectrum of public opinions be represented in talk shows? Should experts have a special say? How should the moderator guide the discussion?

Measuring Public Debate

Thinking about deliberative norms concerns normative questions—that is, questions about what we ought to do and how to evaluate actions based on moral considerations. Observing and understanding what happens in public debate is a connected but different endeavour since it relates to empirical questions. We could, for instance, analyse the extent to which public debate measures up to deliberative norms and try to identify explanations as to why our actual practice falls short of ideal deliberation.

Researchers can approach the empirical analysis of public debate from different academic disciplines, such as sociology, communication studies, psychology, linguistics, etc. With my background in argumentation theory and argument mapping, I am particularly interested in analysing the argumentative features of public debate. I am, for instance, interested in whether right-wing populism is connected to a specific form of argumentation: Do populists use certain argument types more often than other politicians? Are they prone to fallacies? What kind of argumentative strategies do they employ?

The researcher has to use empirical methods to answer such questions. In some sense, the researcher must "measure" features of public debate. The question is, which measurement instruments are suitable for this purpose. Methods from applied formal logic and informal logic work well for this purpose. However, they were not particularly envisaged to be used in socio-empirical contexts. As a result, they have difficulties satisfying certain scientific criteria. In particular, it is unclear how to fulfil the requirement of reproducibility. The analysis of natural-language argumentation is a subjective interpretational process. Accordingly, analysts' results can diverge, which poses a challenge: If we want to capture features of public debate, we have to be sure that diverging measurement results correspond to differences in what we measured. In other words, observed differences should imply a difference in the phenomena. However, in the face of interpretational leeway, diverging measurement results can have two causes: A difference in the phenomena or in the analysts' interpretational choices. In my PhD thesis, I offer one suggestion to solve this problem by advancing a statistical concept of reproducibility for the analysis of argumentation structure.

Argumentation and Large Language Models

Since the advance of Chat-GPT, many people have become very enthusiastic about the capabilities of large language models (LLMs). Undoubtedly, LLMs will revolutionise the way we create and analyse language. Accordingly, they have enormous potential to be used in the described normative and empirical context. So, how can we employ LLMs to improve and analyse public debate?

Improving public debate: Public debate involves text creation. Since LLMs excel in content creation, it stands to reason that LLM-based tools can be used to improve public debate. We could, for instance, think of tools that help citizens understand others' arguments better or formulate their own arguments. In this way, LLMs could help improve the quality of public debate, increase inclusion and equality of participation or even alleviate the prevailing problems in social media (such as toxic speech and misinformation). The research project KIdeKu aims to contribute to this line of employing LLMs.

Analysing public debate: Analysing public debate is a daunting task. It involves an annotation and categorisation of text that is often challenging. Annotators must be extensively trained, and the analysis takes time and effort. Accordingly, such a text analysis does not scale well. If LLMs could be used to perform some of the involved tasks, analysis of public debate could be applied to larger text corpora without relying on a swarm of annotators.