Influencing the Habermas Machine

In my previous post Towards the Habermas Machine, I talked about a large language model (LLM) developed by Google DeepMind for generating a consensus position from a collection of individual views, named after Jürgen Habermas.

Given that democratic deliberation relies on knowledge of various kinds, followers of Habermas might be interested in how knowledge is injected into discourse. Habermas argued that mutual understanding was dependent upon a background stock of cultural knowledge that is always already familiar to agents. but this clearly has to be supplemented by knowledge about the matter in question.

For example, we might expect a discussion about appropriate speed limits to be informed by reliable or unreliable beliefs about the effects of a given speed limit on journey times, accident rates, pollution, and so on. In traditional discussion forums, it is extremely common for people to present themselves as having some special knowledge or authority, which supposedly gives extra weight to their opinions, and we might expect something similar to happen in a tech-enabled version.

For many years, the Internet has been distorted by Search Engine Optimization (SEO), which means that the results of an internet search are largely driven by commercial interests of various kinds. Researchers have recently raised a similar issue in relation to large language models, namely Generative Engine Optimization (GEO). Meanwhile, other researchers have found that LLMs (like many humans) are more impressed by superficial jargon than by proper research.

So we might reasonably assume that various commercial interests (car manufacturers, insurers, oil companies, etc) will be looking for ways to influence the outputs of the Habermas Machine on the speed limit question by overloading the Internet with knowledge (regime of truth) in the appropriate format. Meanwhile the background stock of cultural knowledge is now presumably co-extensive with the entire Internet.

Is there anything that the Habermas Machine can do to manage the quality of the knowledge used in its deliberations?

Footnote: Followers of Habermas can't agree on the encyclopedia entry, so there are two rival versions.

Footnote: The relationship between knowledge and discourse goes much wider than Habermas, so interest in this question is certainly not limited to his followers. I might need to write a separate post about the Foucault Machine.

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Pranjal Aggarwal et al, GEO: Generative Engine Optimization (arxiv v3, 28 June 2024)

Callum Bains, The chatbot optimisation game: can we trust AI web searches? (Observer, 3 November 2024)

Alexander Wan, Eric Wallace, Dan Klein, What Evidence Do Language Models Find Convincing? (arxiv v2, 9 August 2024)

Stanford Encyclopedia of Philosophy: Jürgen Habermas (v1 2007) Jürgen Habermas (v2 2023)

Towards the Habermas Machine

Google DeepMind has just announced a large language model, which claims to generate a consensus position from a collection of individual views. The name of the model is a reference to Jürgen Habermas’s theory of communicative action.

An internet search for Habermas machine throws up two previous initiatives under the same name. Firstly an art project by Kristopher Holland.

The Habermas Machine (2006–2012) is a conceptual art experience that both examines and promotes an experiential relation to Jürgen Habermas’ grand theory for understanding human interaction. The central claim is that The Theory of Communicative Action can be experienced, reflected upon and practised when encountered within arts-based research. Habermas’ description of how our everyday lives are founded by intersubjective experience, and caught up in certain normative, objective and subjective contexts is transformed through the method of conceptual art into a process of collaborative designing, enacting and articulating. This artistic reframing makes it possible to experience the communicative structure of knowledge and the ontological structure of intersubjectivity in a practice of non-discursive ‘philosophy without text’. Feiten Holland Chemero

And secondly, an approach to Dialogue Mapping described as a device that all participants can climb into and converse with complete communicative rationality, contained in a book by @paulculmsee and Jailash Awati, and mentioned in this Reddit post Why is Dialogue Mapping not wide spread? Dialogue Mapping was developed by Jeff Conklin and others as an approach to addressing wicked problems. See also Issue Based Information Systems (IBIS).

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Update

Christopher Summerfield, one of the authors of the DeepMind paper, spoke at the Royal Society on October 29th 2024. https://www.youtube.com/live/cW1Wq7_8v1Y?si=oqo8Lw7479x4QqKt&t=18890

All the examples shown in his talk were policy matters that could be reduced to Yes/No questions. Such questions would traditionally be surveyed by asking people to place themselves on a scale from Strongly Agree to Strongly Disagree, and it is easy to see how a language-based method such as the Habermas Machine offers some advantages over a numerical scale. But not clear how this works for more provocative questions, let alone wicked problems.

Someone in the audience asked if this method would work in what he called a compromised democracy, and Summerfield acknowledged that the method assumes what he called a good faith scaffold. Obviously all democracies in the real world are imperfect, and he didn't go into the question as to how sensitive or vulnerable the method might be to such imperfections, but the method might conceivably help to overcome some of these imperfections under some conditions: for example, Summerfield referred specifically to the tyranny of the majority.

While the performance of the Habermas machine in their study compared favourably with the performance of human mediators, Summerfield suggested that we should move away from thinking about AI in these terms. The point is not to create AI-based agents that can behave like intelligent people but to build intelligent institutions - tools for creating social order and fostering cooperation. As my regular readers will know, orgintelligence has long been an important theme for this blog. See for example my post On Organizations and Machines (January 2022).

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Jeffrey Conklin, Dialogue Mapping: Building Shared Understanding of Wicked Problems (Wiley 2006). See also CogNexus website.

Paul Culmsee and Jailash Awati, The Heretic's Guide to Best Practice (2013)

Nicola Davis, AI mediation tool may help reduce culture war rifts, say researchers (Guardian, 17 October 2024)

Tim Elmo Feiten, Kristopher Holland and Anthony Chemero, Doing philosophy with a water-lance: art and the future of embodied cognition (Adaptive Behavior 2021) 

Michael Tessler et al, AI can help humans find common ground in democratic deliberation (Science, 18 October 2024)

Beyond the symbols vs signals debate (The Royal Society, 28-29 October 2024)

Wikipedia: Issue Based Information Systems (IBIS), Wicked Problem

See also Influencing the Habermas Machine (November 2024)