AI Threat · Module 7 · The Verdict

What Going Well Looks Like

The lesson gives the other side of the ledger. Participants state the optimistic visions at full strength, read them soberly, learn why the distribution of gains is a choice and what the evidence shows about augmentation and about AI that helps people deliberate and correct false beliefs. They identify the five conditions that separate hopeful futures from dystopian ones, see that these are the defences already recommended, and write a positive-scenario register entry. The open question of moral status is recorded without anthropomorphising.

Lesson 34 of 35Chapter 3475 minutes

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Learning objectives

After the lesson the participant can:

Key concepts

The cost of not building
A decision not to use a technology also has victims, but they are statistical and invisible: the patient diagnosed later, the applicant who waited longer. A risk function that records only the downside tends to recommend inaction, which is seldom recorded as a decision.
Direction of technology
Acemoglu and Johnson's claim that the direction is a choice: a vision of replacing workers often yields so-so automation, while a vision of making people more capable, backed by countervailing powers, spreads the gains.
Same capability, different objective
Fluent, personalised argument is neutral as to its objective. A system optimised for engagement finds flattery and outrage useful; one instructed to be accurate behaves differently. The objective belongs in the decision record.
Five conditions for going well
Capability that is real and measured; safety and containment that do not depend on intent; distribution of gains decided, not assumed; institutions that can see, correct and stop; human capability kept in practice.
Positive-scenario entry
A register entry with a direction field and its own prefix that records a good outcome, the conditions it depends on, an indicator for each and the threat entry that would grow if a condition failed.

Interactive exercises

Do them here and get feedback at once. Choose a card and then where it belongs, or drag it.

Interactive exercise 1 · Match

Conditions and their indicators

Nordvik Region's positive-scenario entry gives each condition for going well an indicator. Match each condition to its indicator.

Interactive exercise 2 · Scenario

An evaluation that finds no gain

Apply the triggers of the positive-scenario entry POS-NR-01.

Teaching plan

12 minThe teacher states the vision of compressed progress and the argument from omission at full strength, then the three qualifications: interested party, contested pace, and distribution.
13 minReview the history of who captured productivity gains, the customer-support evidence on augmentation, and the experiments on finding common ground and correcting false beliefs, each with its limits.
12 minParticipants map each of the five conditions to two controls from earlier lessons and note which condition their own organisation is weakest on.
28 minExercise in pairs: write a positive-scenario entry modelled on POS-NR-01, in terms a resident, customer or patient would recognise.
10 minDiscuss the open questions, including moral status, keeping to behaviour under stated conditions; compare what Nordvik's and Hansa's hopeful cases depend on most.

Discussion

  1. Inaction is seldom recorded as a decision at all. How could your organisation's risk process make the cost of not using AI for a given purpose visible alongside the cost of using it?
  2. POS-NR-01 lets the committee ask whether the system should be used at all if an independent evaluation finds no gain. What would count as no gain for an AI system you rely on, and who would be willing to act on that finding?
  3. The authors who raise AI welfare warn of two opposite errors. Why does the book take no position, and what small practical implication does it still draw for deployers?

Written exercise

Exercise · 28 minutes

A positive-scenario register entry

Write a positive-scenario entry for one purpose your organisation pursues with AI, or for Hansa Mutual's aim of fairer and more resilient insurance. Give it its own prefix and a direction field so that it is not counted among the threats. Write the title and two or three outcomes in terms the people served would recognise, with a target year. State the rung and label honestly for the benefit claimed, and which tropes the scenario counters. For each of the five conditions, write one indicator you could actually measure and name the threat entry that would grow if the condition failed. Add the owner, the decision tier and three triggers, one of which must be an independent evaluation showing no gain. Set the next review date.

Deliverable: A positive-scenario entry with outcomes, rung and label, five conditions with indicators and linked threats, owner, tier, triggers and review date.

Self-test

Choose an answer to see whether it is right, or open the answer below each question.

  1. On which rung of the evidence ladder does the chapter place the most ambitious claims of the vision of compressed progress?

    Show the answer

    C. Argument, because they follow from premises about future capability not yet demonstrated
    The essay is a well-argued statement by an interested party, not a forecast with evidence behind it, and it treats distribution as a problem to be solved, not an automatic outcome.

  2. What did the experiments with an AI mediator in the United Kingdom find?

    Show the answer

    A. Participants preferred the AI-drafted group statements in a majority of comparisons and were less divided afterwards
    The finding is a laboratory demonstration from structured online settings; it shows that the capability feared as manipulation can help people find statements they all endorse, not that it holds in a real campaign.

  3. What does the positive-scenario entry show a governing body about the controls in the threat register?

    Show the answer

    D. That most controls are conditions of getting value, not merely costs of avoiding harm
    Every condition in POS-NR-01 is already covered by a control adopted against a threat, so the hopeful and defensive cases are one programme.