Computable·Authority
← Curriculum

Module 1 · Days 1–7

The Policy Problem

Why documents written for human judgement break down the moment a machine has to act on them — and why an autonomous machine breaks them further.

6 lectures

  1. Lecture 1.1

    Why Policies Fail Machines

    Modern institutions rely on policies as the primary mechanism for expressing authority. However, policies were designed for human interpretation rather than machine execution. As artificial intelligence systems increasingly participate in institutional decisions, a fundamental challenge emerges: how can authority be represented in a form that machines can execute and independent parties can verify?

    Read lecture →

  2. Lecture 1.2

    The Limitations of Documents

    The document is the dominant carrier of institutional authority, and it is a carrier with no defined behaviour. This lecture examines what a document can and cannot do as a governance instrument, and why format modernisation — PDF to HTML to XML — does not by itself close the gap.

    Read lecture →

  3. Lecture 1.3

    Human-Readable vs Machine-Readable Authority

    Machine-readability is often treated as a downgrade of legal text. This lecture argues the opposite: the machine-readable form is a second, disciplined expression of the same authority, and the two forms must be governed together.

    Read lecture →

  4. Lecture 1.4

    Rules Are Not Authority

    A machine-executable rule says what follows when its conditions are met. It does not say who is entitled to cause that outcome, under whose delegation, against which version, or on what evidence. This lecture draws the line the rest of the discipline is built on: encoding a rule and authorising an actor to act under it are different institutional functions.

    Read lecture →

  5. Lecture 1.5

    What Autonomous Agents Change

    Traditional automation runs a path a human designed: if X, do Y. An autonomous agent interprets the objective, chooses the tools, calls the APIs, and picks among actions the designer never enumerated. This lecture examines why that shift moves the authority question from design time to runtime — and why knowing which agent is acting is not the same as knowing it is allowed to.

    Read lecture →

  6. Lecture 1.6

    Why AI Creates Urgency

    Interpretation has always been unrecorded, and institutions absorbed the resulting variance because human throughput was low. Autonomous systems remove that slack. This lecture examines why the authority layer becomes load-bearing precisely when execution becomes machine-speed.

    Read lecture →

Back to the curriculumGlossary of terms