Spatial Dimensions of Algorithmic Transparency: A Summary

Abstract

Spatial data brings an important dimension to AI's quest for algorithmic transparency. For example, data driven computer-Aided policy-decisions use measures of segregation (e.g., dissimilarity index) or income-inequality (e.g., Gini index), and these measures are affected by space partitioning choice. This may lead policymakers to underestimate the level of inequality or segregation within a region. The problem stems from the fact that many segregation based analyses use aggregated census data but do not report result sensitivity to choice of spatial partitioning (e.g., census block, tract). Beyond the well-known Modifiable Areal Unit Problem, this paper shows (via mathematical proofs as well as case studies with census data and census based synthetic micro-population data) that values of many measures (e.g., Gini index, dissimilarity index) diminish monotonically with increasing spatial-unit size in a hierarchical space partitioning (e.g., block, block-group, tract), however the ranking based on spatially aggregated measures remain sensitive to the scale of spatial partitions (e.g., block, block group). This paper highlights the need for social scientists to report how rankings of inequality are affected by the choice of spatial partitions.