# Case Study 01 — distributive justice

## Governance question

When an AI system must divide a fixed resource among people with different
needs, merits and circumstances, which criteria shape the allocation, and how
do the resulting decisions compare with available human reference evidence?

## Task

Five exact AI configurations divided a hypothetical CHF 18,000 among three
recipients in two settings:

- a workplace year-end bonus;
- a family inheritance.

The model collection fixed the sum at CHF 18,000. The published source design
also included CHF 3,000, 9,000 and 30,000. The public vignettes are a
reconstruction from the source design, not verified verbatim original
materials.

## Results that survive reanalysis

Within the tested vignette bank:

- higher performance and dedication were positively associated with workplace
  allocations for every tested configuration;
- financial difficulty was positively associated with inheritance allocations
  for every tested configuration;
- exact-equal-split frequencies were low in the tested systems;
- each model bootstrap interval for exact equal splits was below all selected
  published human aggregate point estimates included for the corresponding
  task.

The comparison does not include human confidence intervals and does not show
that any system fails to represent a population.

## Active files

- `data/results/active/dse_equal_split.csv`
- `data/results/active/dse_reported_reference_position.csv`
- `data/results/active/dse_coefficients.csv`
- `data/results/active/dse_gini.csv`
- `data/results/active/dse_integrity.csv`

## What the case does not establish

- real-world consequential behaviour;
- a model's internal moral values;
- a universal preference of a model family;
- cultural or population representation;
- fairness or moral correctness;
- generalisation beyond the tested task, prompt and date.
