Interpretive Authority: The Fulcrum of Power (Part 1)
How the Right to Understand AI Is Becoming a New Kind of Power
1. Introduction
Mechanistic interpretability (MI) began as a set of tools for understanding how machine learning models process and transform information internally. At first, it was primarily a technical concern—something researchers used to investigate odd outputs or improve performance.
But as these systems become increasingly embedded in legal, economic, and social infrastructures, the ability to explain *why* they behave the way they do may carry new kinds of weight. It could influence not just technical refinement, but governance, trust, and policy.
What may be emerging is a form of **interpretive authority**—not just the capability to understand a model, but the social and institutional position to have that understanding shape decisions and norms.
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2. Mechanistic Interpretability in Practice
Current interpretability techniques—like causal tracing, activation patching, and logit lensing—are designed to peer inside the model's decision-making process. These tools help researchers isolate which components contribute to specific outputs. They allow us to ask:
- What information led to this prediction?
- Which part of the model reacted to a specific cue?
- How did different layers shape the final response?
These methods are vital for diagnosing errors and refining safety. But beyond their technical function, they raise questions about access, responsibility, and influence. As interpretability becomes more accessible, the implications of who interprets—and how—may extend well beyond the lab.
Technique Quick Guide:
- Causal Tracing: Follows how information moves through the network to pinpoint which paths influence the model’s output.
- Activation Patching: Replaces parts of a model’s internal state with those from another prompt to identify which regions are responsible for certain behaviors.
- Logit Lensing: Projects intermediate activations into the output space to reveal what the model is implicitly “thinking” at each step.
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3. From Capability to Interpretive Authority
Understanding a model is not the same as being heard. Interpretive authority refers to the recognized standing to explain what a system is doing and why—and to have that explanation matter.
It may shape:
- Who is trusted to assess high-stakes models.
- Whose interpretations guide regulation or public policy.
- Which explanations gain traction in institutional or public discourse.
This distinction may become more significant as interpretability becomes more contested. While more people could gain the skills to analyze AI systems, the systems themselves may become harder to interpret—especially as models grow in complexity and proprietary constraints tighten. In such a landscape, recognized authority may concentrate among a small number of institutions, particularly those with access to closed-source architectures. Interpretations from outside these circles, even if well-reasoned, may struggle to gain traction.
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4. A Familiar Pattern: Historical Analogies to Interpretive Authority
This dynamic—where the ability to interpret may become increasingly limited, even as its importance grows—has precedent in other domains:
a. Science: Experiments vs. Peer-Validated Knowledge
Anyone can experiment, but scientific legitimacy flows through publication, funding, and institutional credibility.
In interpretability, technical insight may increasingly rely on internal infrastructure, making trusted explanation a function of affiliation as much as capability.
b. Medicine: From Healer to Licensed Physician
Informal practitioners once played vital roles in healthcare. But over time, only licensed doctors were granted the legitimacy to diagnose and treat.
We may see something similar in AI, where only a handful of institutions maintain exclusive interpretive authority—particularly as systems become harder to understand without privileged access.
c. Law: Knowing the Code vs. Speaking in Court
Many can read the law, but only those with standing can argue it in court. Legal interpretation rests on role, not just understanding.
Even if model internals are visible, the capacity to influence their interpretation may remain restricted to credentialed or institutionally embedded actors.
d. Religion: Scriptural Literacy vs. Doctrinal Legitimacy
Historically, religious texts were interpreted through an institutional lens. Reform movements sought to decentralize that control.
A similar tension may develop around interpretability—where tools are technically available, but their interpretations are selectively sanctioned.
These analogies suggest that as models become more complex and closed, interpretive authority could increasingly depend on one's institutional proximity and perceived neutrality—raising questions about access, trust, and the boundaries of credible explanation.
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…To Be Continued…