Homelessness presents a long-standing problem worldwide. Like other welfare services,
homeless services have gained increased traction in Machine Learning (ML) research …

Homelessness is a humanitarian challenge affecting an estimated 1.6 billion people
worldwide. In the face of rising homeless populations in developed nations and a strain on …

Governmental and organizational policy increasingly claims to be data‐driven, data‐
informed, or knowledge‐driven. We explore the data practices of local governments and …

Although data quality is of paramount importance in algorithmic decision–making, most
existing methods for supervised classification use training data without ever questioning …

Poverty in the US is not invisible. A large number of Americans are low-income and
experience homelessness. This population relies on scarce-resourced public services for …

On any night in Canada, at least 35,000 individuals experience homelessness. These
individuals use emergency shelters to transition out of homelessness and into permanent …

Most supervised classification methods assume perfect training data, although this is not
usually the case in the real–world. Meanwhile, counterfactual data generation approaches …

One branch of machine learning algorithms is supervised learning, where the label is crucial
for the learning model. Numerous algorithms have been proposed for supervised learning …