Assessing Predictive Models for Fairness Based on Activity-Space Patterns
Assessing the spatial fairness of predictive models involves establishing whether they are statistically penalizing (favoring) individuals associated with certain geographical locations. Literature on this topic makes the fundamental assumption that each individual is assigned to a single geographical location (e.g., place of residence). However, fairness with respect to the set of regions where one regularly spends time, i.e., the individual's activity space, also matters when fairness is considered. Consequently, we argue that it is necessary to generalize the notion of spatial fairness to also account for such activity-space patterns, leading to the novel problem of assessing predictiv...
| Authors | Francesco Lettich, Mario A. Nascimento, Chiara Pugliese et al. |
|---|---|
| Primary category | cs.LG |
| Published | 22 May 2026 |
| Last updated at source | 10 September 2026 |
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