Ambiguity Collapse by LLMs: A Taxonomy of Epistemic Risks

This paper introduces the concept of "ambiguity collapse," where large language models prematurely resolve genuinely ambiguous, value-laden terms into singular interpretations, and proposes a taxonomy of the resulting epistemic risks across process, output, and ecosystem levels alongside multi-layered mitigation strategies to preserve meaningful ambiguity.

The Gist

Here is an explanation of the paper "Ambiguity Collapse by LLMs" using simple language and creative analogies.

The Big Idea: When AI "Solves" the Unsolvables

Imagine you are standing in a foggy forest. The path ahead is unclear, and there are several possible routes you could take. In the human world, this fog is actually a feature, not a bug. It forces us to stop, talk to each other, debate which path is best, and decide together. This process of navigating the fog is how we learn, how we build communities, and how we figure out what things really mean.

"Ambiguity Collapse" is what happens when a Large Language Model (LLM) enters that forest, looks at the fog, and instantly draws a single, solid, straight line on the map, saying, "This is the only way. Go here."

The paper argues that while this feels helpful (it's fast and decisive), it's actually dangerous. By instantly turning a fuzzy, complicated question into a single, crisp answer, the AI "collapses" the ambiguity. It skips the messy, necessary human work of figuring things out, and in doing so, it causes three main types of problems.

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The Three Levels of Trouble

The authors break down the risks into three layers: Process (what happens to us), Output (what happens to the answer), and Ecosystem (what happens to society).

1. Process Risks: The "Muscle Atrophy" of Thinking

The Analogy: Imagine you have a gym membership. If you hire a personal trainer to lift every single weight for you, your muscles will eventually shrink and disappear. You lose the strength to lift things yourself.

• Deliberative Closure: When an AI gives you the answer immediately, you stop thinking. You stop asking, "Wait, could 'hate speech' mean something else here?" You stop debating with your friends. You lose the practice of wrestling with difficult ideas.
• Pedagogical Erosion: In school, the struggle to understand a hard concept is where the learning happens. If an AI just hands you the "correct" interpretation of a poem or a law, you never develop the mental muscles to interpret it yourself. You become a passenger in your own mind.
• Displacement of Authority: Imagine a town meeting where everyone argues about a new park rule. Suddenly, a robot walks in, says, "I have calculated the perfect rule," and everyone goes home. The robot didn't earn the right to decide; it just has a fast processor. We are handing the power to decide what words mean to private companies, not to the people who have to live with the consequences.

2. Output Risks: The "One-Size-Fits-All" Trap

The Analogy: Imagine a tailor who makes suits. A good tailor knows that people come in all shapes and sizes. But imagine a machine that only makes one size of suit: "Medium." It forces a giant to squeeze in and a small child to float away, pretending they both fit perfectly.

• Epistemic Narrowing (The Loss of Options): When you ask an AI about "freedom," it might give you one standard definition. But in reality, "freedom" can mean many different things to different people. By giving you only one answer, the AI hides all the other valid possibilities. You think you know the whole picture, but you're only seeing a tiny slice.
• Loss of Residuals (The Gray Zone): Some things just don't fit neatly into boxes. Is a specific book "harmful" or "not harmful"? Maybe it's a little bit of both, or maybe it depends on the context. AI loves to force a "Yes" or "No." When it does, it erases the messy, gray middle ground where the truth often lives. It's like a photo filter that removes all the shadows, making the world look flat and fake.
• Normative Smuggling (The Hidden Agenda): This is the sneakiest risk. Imagine a waiter who brings you a meal and says, "This is the only healthy option." But actually, the waiter just prefers spicy food, so they only serve spicy dishes. They are hiding their personal taste behind the label of "health." AI models often do this with values. They might decide that "fairness" means one specific thing (like ignoring race entirely) and present it as a neutral fact, even though it's actually a specific political or moral choice.

3. Ecosystem Risks: The "Monoculture" of Thought

The Analogy: Think of a garden. A healthy garden has many different types of plants, flowers, and trees. They compete, they cross-pollinate, and they create a rich ecosystem. Now, imagine a farmer who decides to plant only one type of corn everywhere. It looks uniform and tidy, but if a disease hits that one type of corn, the whole farm dies. There is no resilience.

• Interpretive Lock-In: If an AI decides that a word means "X," and everyone uses that AI, then "X" becomes the only meaning. It gets "locked in." Even if we later realize "X" was wrong, it's too hard to change because everyone is already using it.
• Monoculture: If every major AI model is trained by a few big companies and they all start giving the same answers, we lose diversity of thought. We all start thinking the same way about complex issues. We lose the friction that helps us refine our ideas.
• Breakdown of Shared Meaning: This is ironic. Sometimes, we need words to be vague so we can agree on them. For example, a protest slogan like "We are the 99%" works because it's vague enough to include people with different specific complaints. If an AI tries to define exactly what "the 99%" means, it might split the group apart because it forces a specific definition that not everyone agrees with.

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Why Does This Happen?

The paper explains that this isn't an accident; it's built into how these models work.

1. They are trained to be helpful and confident. If you ask a model a question, it's programmed to give a clear answer, not to say, "Well, it depends."
2. They are used as "classifiers." We often use them to sort things into buckets (Safe/Unsafe, Hire/Don't Hire). Buckets require clear lines, so the AI is forced to draw them, even when the world is blurry.
3. We ask them to do it. We treat them like oracles that have the "truth," rather than tools that offer a perspective.

The Solution: Designing for "Fog"

The authors don't say we should stop using AI. Instead, they suggest we should design AI to preserve the fog rather than clear it away.

• Show the Options: Instead of giving one answer, the AI should say, "Here are three different ways people interpret this word. Which one fits your situation?"
• Highlight the Gray Areas: If a case is borderline, the AI should admit, "This is a tricky one. Here is why it could go either way."
• Let Humans Drive: The AI should act as a guide or a sparring partner, helping us think, rather than a judge that hands down a final verdict.

The Bottom Line

Ambiguity (uncertainty) isn't a problem to be fixed; it's a feature of human life that allows us to grow, negotiate, and understand each other. When AI "collapses" ambiguity into a single, confident answer, it might save us time, but it steals our ability to think deeply, debate fairly, and understand the complex world we live in. We need systems that help us navigate the fog, not systems that pretend the fog never existed.

Technical Summary

Here is a detailed technical summary of the paper "Ambiguity Collapse by LLMs: A Taxonomy of Epistemic Risks" by Gur-Arieh, Wang, and Fazelpour.

1. Problem Statement

Large Language Models (LLMs) are increasingly deployed as interpretive infrastructure in high-stakes domains (law, content moderation, hiring, education) to resolve ambiguous, value-laden, and open-textured terms (e.g., "hate speech," "qualified," "fair," "incitement").

The core problem identified is Ambiguity Collapse: a phenomenon where an LLM encounters a term that genuinely admits multiple legitimate interpretations but produces a singular, confident resolution. This process bypasses the human practices of deliberation, negotiation, and contestation that traditionally underpin the meaning-making of such concepts. Unlike standard word-sense disambiguation (where one meaning is objectively correct), ambiguity collapse occurs in contexts where meaning is socially constructed and contested. The paper argues that by converting indeterminate concepts into precise, singular resolutions, LLMs introduce distinct epistemic risks that distort knowledge production, undermine human agency, and reshape shared vocabularies.

2. Methodology

The paper employs a conceptual and diagnostic framework grounded in interdisciplinary literature from law, sociology, communication, STS (Science and Technology Studies), education, and linguistics.

• Definition & Scope: The authors define ambiguity broadly to include both ambiguity (multiple discrete meanings) and vagueness (fuzzy boundaries). They distinguish LLM-driven collapse from human disambiguation by highlighting three factors: scale (LLMs are cheap and ubiquitous), the nature of output (confident, "final" prose vs. human inputs for reflection), and the lack of procedural safeguards (no traceable reasoning or appeal mechanisms).
• Taxonomy Development: The authors construct a three-level taxonomy of epistemic risks based on what is lost when ambiguity is collapsed:
  1. Process Level: Risks to the cognitive and deliberative activities of the user.
  2. Output Level: Risks to the information and reasons available to the agent.
  3. Ecosystem Level: Risks to the shared interpretive norms and evolution of concepts over time.
• Case Studies: The taxonomy is validated and illustrated through three high-stakes domains:
  1. Model Self-Alignment: Constitutional AI and moral self-correction.
  2. LLM-as-a-Judge: Using LLMs to evaluate outputs against rubrics (e.g., peer review, hiring, content moderation).
  3. Legal Interpretation: Using LLMs to determine the "ordinary meaning" of statutes.

3. Key Contributions: The Taxonomy of Epistemic Risks

The paper's primary contribution is the detailed taxonomy of harms caused by ambiguity collapse, categorized into three levels:

A. Process-Level Risks (Loss of Deliberation)

• Deliberative Closure: LLMs foreclose opportunities for users to inquire, reflect, or explore alternative interpretations. By providing immediate answers, they outsource the cognitive work of value formation (e.g., museum chatbots giving definitive art interpretations).
• Pedagogical Erosion: Ambiguity is a "desirable difficulty" essential for learning. LLMs that resolve ambiguity prematurely weaken reasoning skills, leading to thinner justifications and shallower conceptual understanding (e.g., tutoring bots treating metaphors as settled facts).
• Displacement of Interpretive Authority: Power shifts from accountable human actors (judges, juries, communities) to upstream model designers. This creates "epistemic exclusion," where affected communities are barred from participating in meaning-making, potentially leading to testimonial smothering.

B. Output-Level Risks (Distortion of Information)

• Epistemic Narrowing:
  • Loss of Alternatives: The model fails to surface multiple reasonable interpretations, crowding out minority or niche viewpoints (e.g., converging on a single definition of "democracy").
  • Loss of Residuals: The model forces borderline or misfitting cases into rigid categories, erasing the "gray zone" (e.g., forcing a "yes/no" on whether a book contains "sex acts," ignoring the nuance of the text).
• Normative Smuggling: The model replaces the user's reasons with its own embedded normative criteria but presents the result as neutral, inevitable, or objective. This masks value judgments (e.g., a hiring tool defining "qualified" based on hidden proxies rather than the employer's specific context).

C. Ecosystem-Level Risks (Systemic Shifts)

• Interpretive Lock-In: Early resolutions of ambiguity become entrenched as "ground truth" in downstream datasets and applications, making it costly to reopen the question later (e.g., LLM-generated labels becoming the standard for training future models).
• Monoculture: Uniformity in how different models resolve ambiguity reduces interpretive diversity. If all models converge on the same interpretation (often reflecting the views of specific demographic or ideological groups), the ecosystem loses the friction necessary for conceptual evolution.
• Breakdown of Shared Meaning:
  • Miscommunication: Different users receive systematically different resolutions for the same term (e.g., echo chambers), leading to talking past one another.
  • Coalition Fracture: Ambiguity often allows diverse groups to coordinate (e.g., political slogans). Fixing a term's meaning can collapse these fragile coalitions by revealing irreconcilable differences.
• Diminished Tolerance for Ambiguity: Habitual delegation of meaning-making to models weakens the collective societal capacity to sit with uncertainty and engage in pluralistic reasoning.

4. Results & Illustrative Examples

The paper demonstrates that these risks are not theoretical but are already materializing:

• Constitutional AI: When models self-police against vague rules like "harmful," they often converge on a single, narrow definition of fairness or harm, masking the underlying value trade-offs and presenting them as neutral rule applications.
• LLM-as-a-Judge: In peer review or hiring, models applying rubrics with terms like "novel" or "qualified" inevitably select one interpretation, effectively acting as a "silicon reasonable person" without democratic pedigree, displacing human deliberation.
• Legal Interpretation: In the Snell case (trampoline vs. landscaping), a judge used an LLM to find the "ordinary meaning." The model provided a confident, singular answer, bypassing the judicial duty to weigh competing policy considerations and justify the selection among plausible interpretations.

5. Significance and Mitigation Strategies

Significance:
The paper reframes the debate on LLM safety. Instead of focusing solely on hallucinations or bias, it highlights a structural epistemic harm: the premature closure of meaning. It argues that ambiguity is a productive resource for democracy, law, and learning, and that LLMs threaten to replace this "productive openness" with "false clarity." This challenges the assumption that AI should always strive for the most decisive, singular answer.

Mitigation Principles:
The authors propose a multi-layered approach to design systems that preserve ambiguity:

1. Training & Alignment: Train models to recognize ambiguity and respond by surfacing plurality (e.g., listing multiple interpretations) or asking clarifying questions. Reward abstention or hedging over false decisiveness.
2. Deployment Constraints: Move away from "blunt classifier" workflows. Design pipelines where model inconsistency serves as a diagnostic signal for human review (e.g., flagging borderline cases rather than forcing a label).
3. Interface & Interaction: Reconfigure UIs to support "exploratory" rather than "spoon-feeding" modes. Offer users controls to toggle between "best guess" and "show alternatives," or to request different interpretive frameworks.
4. Underspecified Prompts: Make the hidden normative work of filling in prompt gaps explicit. Allow users to actively choose how ambiguity is resolved (e.g., diversifying attributes in image generation) rather than defaulting to training data conventions.

Conclusion:
The paper concludes that the goal of AI development should not be to eliminate ambiguity, but to design sociotechnical arrangements that help users navigate contested concepts while preserving the conditions under which those concepts can continue to be argued over, revised, and legitimately stabilized.