Lesson 01 / 12 · 40 min including practice
A policy problem, not a technology
- Separate a public problem from a proposed technical solution
- Write a decision question with a realistic scope
Start with a decision
An AI policy team earns its keep by helping someone make a better decision. “What should we do about AI?” is an area of interest. It is not yet a workable assignment. Name the decision-maker, the people affected, the harm or opportunity, and the decision that can actually be taken.
Throughout this course you advise the fictional Northbridge Employment Partnership. It funds local employment services and has heard complaints about automated recruitment screening. Its chair wants a recommendation in six weeks. You have two analysts and a small research budget. All organisations, numbers and incidents in this case are invented for learning; they are not findings about a real council or supplier.
Your initial case file contains three claims: employers report too many applications; applicants say rejections are difficult to challenge; suppliers say screening improves consistency. These claims can all be true. Your task is not to choose which group you like most, or to decide whether AI is good in general.
Describe the mechanism
Follow a concrete pathway: a candidate submits an application; a screening system scores it; someone uses that score to decide who gets interviewed; a rejection affects the candidate's opportunities. Ask where a preventable problem could enter that pathway. A model can be accurate at predicting historic hiring decisions while reproducing an employer's past mistakes. A transparent rejection can still be unfair. A slow manual process can also exclude people.
Distinguish the observed problem, your explanation, and the response. “Applicants cannot obtain a review” describes an experience. “Employers rely on automated scores without a challenge route” is a hypothesis about why it happens. “Require a review mechanism” is an intervention to test. Mixing these together makes your preferred policy appear inevitable.
Set boundaries
Choose a population, use case, place and time horizon. For Northbridge, begin with screening for jobs advertised through partnership-funded services over the next twelve months. This is narrower than regulating every recruitment product in the UK, but it gives the partnership a plausible lever. Record the wider questions you are setting aside, rather than pretending they do not matter.
A useful decision question is: What conditions, if any, should Northbridge attach to funded recruitment services to improve access to review without making the service unusable? That question leaves several answers open. Carry it into your research rather than starting with a demand to ban or subsidise a technology.
Work the case
Case evidence: the case documents, numbers and operating constraints are invented. External findings and methods are attributed separately. Keep illustrative methods examples separate from observations in the final case update.
Three documents, three different claims
The chair’s email asks for “fairer screening within funded services”, not a new national regulation. Three documents accompany it:
| Document | What it contains | What it leaves out |
|---|---|---|
| Adviser note | Six accounts of being unable to obtain a rejection reason | How applicants were selected; prevalence |
| Provider log | 18 review requests among 600 applications | Phone requests; rejection count; unmet need |
| Supplier brochure | Faster-shortlisting claim | Comparator, complete workflow cost and challenge outcomes |
None of these establishes regional prevalence or which actor caused a failure. The accounts justify investigating an access problem; the log tells you about recorded demand in one process; the brochure offers a claim to test. Keep that distinction when writing your scope. Ask whether the alleged barrier also exists in manual recruitment. If it does, a technology-only policy could miss the mechanism.
Connect the reading to your practice
Use the Green Book's objectives-led appraisal alongside the Service Standard's user-needs perspective. Before proposing a recruitment tool, ask what would improve applicants' experience and what Northbridge can actually decide.
Sources for this comparison: HM Treasury: The Green Book (2026); Government Digital Service: Service Standard. The application to this fictional case is AIPEX's teaching synthesis; it is not an endorsement by the source authors.
Your first exercise
Try the method.
Write a 150–200 word scoping note for Northbridge. Include the decision-maker, population, observed problem, a causal hypothesis, the decision deadline and one explicit boundary. End with a decision question that leaves at least two responses open. Use the three documents to name one supported observation, one untested explanation and one missing denominator. Keep your note to 150–200 words.
Review criteria
- Names a decision Northbridge can take
- Separates an observation from an explanation
- Defines who and what is in scope
- Does not build a preferred intervention into the question
- Uses the packet accurately and separates observations, illustrative assumptions and unresolved questions
Create an account and enrol free to save your answer, take the checks and track your progress.
Create an account to startCompare with a worked answer
Northbridge's chair must decide within six weeks whether to change conditions for partnership-funded recruitment services. The initial concern is that applicants rejected after automated screening cannot get a meaningful review. We do not yet know its frequency or whether automation, staffing or employer policy causes it. One hypothesis is that staff lack access to the information needed to challenge a score. Scope is screening for jobs advertised through funded services, with effects assessed over twelve months. General national product regulation and decisions by employers outside the funded network are outside this initial recommendation. Our question is: which changes to funded-service conditions would improve access to review while preserving a viable recruitment service? We will compare a better manual process, notice and review requirements, and a restricted pilot before recommending a response.
Work through the packet
The six accounts identify an experience worth investigating, but their selection is unknown. Eighteen recorded requests out of 600 applications is 3% recorded demand, not the proportion of applicants needing review: phone requests and people deterred from asking are missing. We would ask whether rejection notices describe the route and whether manual recruitment has the same barrier. Northbridge can investigate funded services now; any change to conditions depends on the governing agreements. The supplier's speed claim does not answer the chair's access question. This scope remains open to manual improvement, a funding condition or another response supported by the investigation.
This is one defensible approach. Different recommendations can be sound if your reasoning, evidence and conditions support them.
Guided reading
- HM Treasury: The Green Book (2026) ↗
Read the rationale and objectives discussion. Rewrite Northbridge's assignment without assuming an AI solution.
- Government Digital Service: Service Standard ↗
Read points 1 and 2 on user needs and whole problems. Identify an applicant need missing from the supplier's framing.
Selected sections of external guidance and research. These are reading tasks, not additional completion checks. Frameworks, research and legal requirements have different authority; check current guidance and its scope for a real decision.