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Rough sleeping is a chronic problem faced by some of the most disadvantagedpeople in modern society. This paper describes work carried out in partnershipwith Homeless Link, a UK-based charity, in developing a data-driven approach toassess the quality of incoming alerts from members of the public aimed atconnecting people sleeping rough on the streets with outreach serviceproviders. Alerts are prioritised based on the predicted likelihood ofsuccessfully connecting with the rough sleeper, helping to address capacitylimitations and to quickly, effectively, and equitably process all of thealerts that they receive. Initial evaluation concludes that our approachincreases the rate at which rough sleepers are found following a referral by atleast 15\% based on labelled data, implying a greater overall increase when thealerts with unknown outcomes are considered, and suggesting the benefit in atrial taking place over a longer period to assess the models in practice. Thediscussion and modelling process is done with careful considerations of ethics,transparency and explainability due to the sensitive nature of the data in thiscontext and the vulnerability of the people that are affected.

A Recommendation and Risk Classification System for Connecting Rough Sleepers to Essential Outreach Services