Uncovering Value Dynamics in Conversations Using Computational Methods

Abstract

This master’s thesis sits at the intersection of conversation as data and Natural Language Processing (NLP) as a method — asking what hidden insights might emerge when we treat everyday dialogue not just as noise or narrative, but as a space where values are shaped, shared, and sometimes lost. In collaborative, high-stakes settings, decisions are rarely made on logic alone; they are entangled with the values of those at the table — often unspoken, often misaligned.

The research introduces the Value Expression Gap — the disconnect between what people claim to value and what actually surfaces in how they speak. This gap became both a conceptual lens and a design target, shaping the development of a low-fidelity NLP prototype that detects value cues in conversation using sentence embeddings and semantic similarity scoring.

Through a Research through Design (RtD) process — spanning prototyping, real-world deployment, workshop observation, and iterative refinement — the project explored how values appear implicitly in tone, metaphor, emotional framing, or silence. Each stage contributed to improving how values could be surfaced computationally without flattening human nuance.

As the method was tested with organizational leaders and decision-makers, new forms of relevance emerged. Making values visible not only helped reflect on alignment and culture — it opened space for more strategic dialogue, tension awareness, and ethical negotiation. The tool was seen not as a truth-teller, but a reflective companion — prompting deeper questions and exposing patterns otherwise missed.

Rather than offering final answers, this thesis proposes a shift: toward tools that reveal, not resolve; that prompt reflection, not prediction. In doing so, it opens up a new role for AI in design and decision-making — one grounded in transparency, human judgment, and the evolving language of values.