A Recommendation and Risk Classification System for Connecting Rough Sleepers to Essential Outreach Services
Rough sleeping is a chronic problem faced by some of the most disadvantaged people in modern society. This paper describes work carried out in partnership with Homeless Link, a UK-based charity, in developing a data-driven approach to assess the quality of incoming alerts from members of the public...
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| creator | Wilde, Harrison Chen, Lucia Lushi Nguyen, Austin Kimpel, Zoe Sidgwick, Joshua De Unanue, Adolfo Veronese, Davide Mateen, Bilal Ghani, Rayid Vollmer, Sebastian |
| description | Rough sleeping is a chronic problem faced by some of the most disadvantaged
people in modern society. This paper describes work carried out in partnership
with Homeless Link, a UK-based charity, in developing a data-driven approach to
assess the quality of incoming alerts from members of the public aimed at
connecting people sleeping rough on the streets with outreach service
providers. Alerts are prioritised based on the predicted likelihood of
successfully connecting with the rough sleeper, helping to address capacity
limitations and to quickly, effectively, and equitably process all of the
alerts that they receive. Initial evaluation concludes that our approach
increases the rate at which rough sleepers are found following a referral by at
least 15\% based on labelled data, implying a greater overall increase when the
alerts with unknown outcomes are considered, and suggesting the benefit in a
trial taking place over a longer period to assess the models in practice. The
discussion and modelling process is done with careful considerations of ethics,
transparency and explainability due to the sensitive nature of the data in this
context and the vulnerability of the people that are affected. |
| doi_str_mv | 10.48550/arxiv.2007.15326 |
| format | Article |
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people in modern society. This paper describes work carried out in partnership
with Homeless Link, a UK-based charity, in developing a data-driven approach to
assess the quality of incoming alerts from members of the public aimed at
connecting people sleeping rough on the streets with outreach service
providers. Alerts are prioritised based on the predicted likelihood of
successfully connecting with the rough sleeper, helping to address capacity
limitations and to quickly, effectively, and equitably process all of the
alerts that they receive. Initial evaluation concludes that our approach
increases the rate at which rough sleepers are found following a referral by at
least 15\% based on labelled data, implying a greater overall increase when the
alerts with unknown outcomes are considered, and suggesting the benefit in a
trial taking place over a longer period to assess the models in practice. The
discussion and modelling process is done with careful considerations of ethics,
transparency and explainability due to the sensitive nature of the data in this
context and the vulnerability of the people that are affected.</description><identifier>DOI: 10.48550/arxiv.2007.15326</identifier><language>eng</language><subject>Statistics - Applications ; Statistics - Machine Learning</subject><creationdate>2020-07</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2007.15326$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2007.15326$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Wilde, Harrison</creatorcontrib><creatorcontrib>Chen, Lucia Lushi</creatorcontrib><creatorcontrib>Nguyen, Austin</creatorcontrib><creatorcontrib>Kimpel, Zoe</creatorcontrib><creatorcontrib>Sidgwick, Joshua</creatorcontrib><creatorcontrib>De Unanue, Adolfo</creatorcontrib><creatorcontrib>Veronese, Davide</creatorcontrib><creatorcontrib>Mateen, Bilal</creatorcontrib><creatorcontrib>Ghani, Rayid</creatorcontrib><creatorcontrib>Vollmer, Sebastian</creatorcontrib><title>A Recommendation and Risk Classification System for Connecting Rough Sleepers to Essential Outreach Services</title><description>Rough sleeping is a chronic problem faced by some of the most disadvantaged
people in modern society. This paper describes work carried out in partnership
with Homeless Link, a UK-based charity, in developing a data-driven approach to
assess the quality of incoming alerts from members of the public aimed at
connecting people sleeping rough on the streets with outreach service
providers. Alerts are prioritised based on the predicted likelihood of
successfully connecting with the rough sleeper, helping to address capacity
limitations and to quickly, effectively, and equitably process all of the
alerts that they receive. Initial evaluation concludes that our approach
increases the rate at which rough sleepers are found following a referral by at
least 15\% based on labelled data, implying a greater overall increase when the
alerts with unknown outcomes are considered, and suggesting the benefit in a
trial taking place over a longer period to assess the models in practice. The
discussion and modelling process is done with careful considerations of ethics,
transparency and explainability due to the sensitive nature of the data in this
context and the vulnerability of the people that are affected.</description><subject>Statistics - Applications</subject><subject>Statistics - Machine Learning</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotj8tqwzAURLXpoqT9gK6iH7ArP_TwMpj0AYGAk725lq8TEVsKkhKav6-bdBYzMAMDh5C3jKWl4py9g_8x1zRnTKYZL3LxTMYVbVC7aULbQzTOUrA9bUw40XqEEMxg9KPf3ULEiQ7O09pZizoae6CNuxyOdDcintEHGh1dh4A2Ghjp9hI9gp5n9FejMbyQpwHGgK__uSD7j_W-_ko228_verVJQEiRVIOCkikuOFSFYmXHukwWs6tSyUwwIfIMeCVxVseFZv28CIlVxRiqHooFWT5u77jt2ZsJ_K39w27v2MUvOh1Tbg</recordid><startdate>20200730</startdate><enddate>20200730</enddate><creator>Wilde, Harrison</creator><creator>Chen, Lucia Lushi</creator><creator>Nguyen, Austin</creator><creator>Kimpel, Zoe</creator><creator>Sidgwick, Joshua</creator><creator>De Unanue, Adolfo</creator><creator>Veronese, Davide</creator><creator>Mateen, Bilal</creator><creator>Ghani, Rayid</creator><creator>Vollmer, Sebastian</creator><scope>EPD</scope><scope>GOX</scope></search><sort><creationdate>20200730</creationdate><title>A Recommendation and Risk Classification System for Connecting Rough Sleepers to Essential Outreach Services</title><author>Wilde, Harrison ; Chen, Lucia Lushi ; Nguyen, Austin ; Kimpel, Zoe ; Sidgwick, Joshua ; De Unanue, Adolfo ; Veronese, Davide ; Mateen, Bilal ; Ghani, Rayid ; Vollmer, Sebastian</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a676-9f8a408565a93804b0b173b0b84871606621a597eeeeb56c0db8467e9900e8da3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Statistics - Applications</topic><topic>Statistics - Machine Learning</topic><toplevel>online_resources</toplevel><creatorcontrib>Wilde, Harrison</creatorcontrib><creatorcontrib>Chen, Lucia Lushi</creatorcontrib><creatorcontrib>Nguyen, Austin</creatorcontrib><creatorcontrib>Kimpel, Zoe</creatorcontrib><creatorcontrib>Sidgwick, Joshua</creatorcontrib><creatorcontrib>De Unanue, Adolfo</creatorcontrib><creatorcontrib>Veronese, Davide</creatorcontrib><creatorcontrib>Mateen, Bilal</creatorcontrib><creatorcontrib>Ghani, Rayid</creatorcontrib><creatorcontrib>Vollmer, Sebastian</creatorcontrib><collection>arXiv Statistics</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Wilde, Harrison</au><au>Chen, Lucia Lushi</au><au>Nguyen, Austin</au><au>Kimpel, Zoe</au><au>Sidgwick, Joshua</au><au>De Unanue, Adolfo</au><au>Veronese, Davide</au><au>Mateen, Bilal</au><au>Ghani, Rayid</au><au>Vollmer, Sebastian</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Recommendation and Risk Classification System for Connecting Rough Sleepers to Essential Outreach Services</atitle><date>2020-07-30</date><risdate>2020</risdate><abstract>Rough sleeping is a chronic problem faced by some of the most disadvantaged
people in modern society. This paper describes work carried out in partnership
with Homeless Link, a UK-based charity, in developing a data-driven approach to
assess the quality of incoming alerts from members of the public aimed at
connecting people sleeping rough on the streets with outreach service
providers. Alerts are prioritised based on the predicted likelihood of
successfully connecting with the rough sleeper, helping to address capacity
limitations and to quickly, effectively, and equitably process all of the
alerts that they receive. Initial evaluation concludes that our approach
increases the rate at which rough sleepers are found following a referral by at
least 15\% based on labelled data, implying a greater overall increase when the
alerts with unknown outcomes are considered, and suggesting the benefit in a
trial taking place over a longer period to assess the models in practice. The
discussion and modelling process is done with careful considerations of ethics,
transparency and explainability due to the sensitive nature of the data in this
context and the vulnerability of the people that are affected.</abstract><doi>10.48550/arxiv.2007.15326</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Statistics - Applications Statistics - Machine Learning |
| title | A Recommendation and Risk Classification System for Connecting Rough Sleepers to Essential Outreach Services |
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