# Anand's Four-Moves Capstone — Chapter 14 Worked Example

> The Copilot-first-output through four responsible-AI moves trace behind Anand’s headcount-allocation dashboard review in Chapter 14 of *The Defensible Decision*. Provenance, Fairness, Decision-Boundary, Disclosure — each move’s actions, purpose, and surfaced issue locked in one dataset.

**Wholly synthetic.** Any resemblance to a specific real headcount-allocation dashboard is coincidental; the four-move pattern is drawn from analytics-governance patterns Chapter 14 catalogues.

## Files

- [`anand-headcount-fourmoves.csv`](anand-headcount-fourmoves.csv) — flat rows: one per move-action pair
- [`anand-headcount-fourmoves.json`](anand-headcount-fourmoves.json) — canonical scenario contract with source hash

## What it represents

Anand’s analyst produced a plausible one-page FTE-by-function dashboard in ~15 seconds from an eight-line Copilot brief. Aggregate attrition sits at 8% with a green treatment; the top-line looks calm. Anand runs the four responsible-AI moves in order before the CFO’s meeting: Provenance assembles the defensibility bundle; Fairness decomposes the aggregate attrition and reveals the under-30 cohort at 22% (2.75x aggregate); Decision-Boundary matches verification to role (recommendation tier + escalation clause for the archival risk); Disclosure amends the standard AI-directed footer to name the fairness callout as a deliberate post-generation addition. The pass surfaces two substantive issues that would have shipped without it.

## Invariants (locked)

The scenario carries executable invariants that Chapter 14’s release contract enforces:

- Exactly 4 moves in canonical order: Provenance (1), Fairness (2), Decision-Boundary (3), Disclosure (4).
- Every move has a purpose and at least one action; no action is empty.
- Fairness move surfaces an issue about the under-30 attrition cohort.
- Decision-Boundary move surfaces an issue about archival or escalation risk.
- Copilot first output has NO provenance footer.
- Aggregate attrition is 8% with a green treatment (the misleading calm top-line).
- Ship verdict is `ship` after the pass.
- At least 2 substantive issues surfaced by the pass.
- Both the fairness callout and the escalation clause would NOT have shipped without the pass.
- Defensibility bundle carries at least 6 items (matching Chapter 9’s bundle standard).

## Why the invariants matter

Chapter 14’s pedagogical point is that responsible AI in analytics is a four-move discipline, not a single «check for bias» step. If the moves collapsed into fewer than four, one class of failure (provenance / fairness / decision-boundary / disclosure) would go unaddressed. If the pass surfaced zero substantive issues, the moves would look like ceremony. If the fairness cohort were even mildly worse than aggregate (say 10% vs 8%), the callout would be a stretch; the under-30 22% figure is deliberately large enough that the callout is unambiguously load-bearing.

## What it does NOT represent

- Actual headcount data from any real financial-services firm.
- Specific demographic categories beyond the age-band decomposition (the fairness move can decompose along tenure, function, gender, ethnicity, etc.; the dataset models only the axis that surfaces the issue in this scenario).
- The CFO’s ultimate allocation decision (the chapter frames the artifact as the input to that decision, not the decision itself).

## Reproducibility

The dataset ships from the book source at `AlexBooks/applied-ai/ai-directed-viz/data/anand-headcount-fourmoves.json` and is re-emitted here by `AlexBooks/applied-ai/ai-directed-viz/scripts/generate-anand-fourmoves.mjs --site-root <site-root>`.

## License

Released for teaching use alongside *The Defensible Decision*. Use freely.