Preventing algorithmic discrimination in public administration in informal settlements
open
Germany, Europe
WS05373
As machine learning enters welfare, policing, housing, tax, and licensing systems, bias can become harder to detect and contest. The open problem is creating enforceable standards for fairness, appeals, and independent review across all public-sector uses of automated decision-making. The challenge is especially acute in informal settlements, where legacy systems, coordination failures, and weak oversight can magnify harm.