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Foreign rough-sleepers could be deported from the UK

“The Home Office has talked of becoming a more ‘fair, humane and compassionate’ department. Immediately scrapping this cruel rule on rough sleeping would be a good place to start.” This is how Amnesty International UK’s director Kate Allen has condemned powers within new immigration rules, which took effect on 1 December and establish that non-UK nationals who are rough sleeping could face deportation beginning 1 January, 2021.

According to Section 4 of the Statement of Changes in Immigration Rules, published by the Home Office in October, any permission held by someone to stay in the country may be cancelled where the decision-maker is satisfied that the specific person has been rough sleeping in the UK.

To make things even worse, such changes have been introduced amid the coronavirus pandemic, as temperatures plunge towards zero and people are at greater risk of infection from the virus. Furthermore, the legislation has come into force even before the end of the Brexit transition period.

Soon after the publication of the official document on the government website, the Mayor of London Sadiq Khan urged ministers to rethink their decision:  “The injustice and cruelty exhibited by the proposed new immigration laws is a chilling reminder of how the most vulnerable people in our society can be targeted when those in power don’t believe anyone will notice or care. It is not too late for the Government to act and show some compassion that is desperately needed in these difficult times.”

In a letter addressed to Home Secretary Priti Patel and Housing Minister Robert Jenrick and signed by 27 representatives of local authorities and London-based charities, Khan criticised the new legislation, which is likely to deter already vulnerable people from seeking help in rebuilding their lives.

Among the signatories to the letter, Crisis, Housing Justice and Migrants’ Rights Network, who pointed out that the measures taken so far this year by City Hall, charities and councils have resulted in very low COVID-19 infection rates among rough sleepers in the capital. Without additional measures that allow all those unable to self-isolate, including non-UK nationals, to access adequate support, COVID-19 infection rates among people without a roof over their head could soar and subsequently spread to the wider community.

Moreover, people who sleep rough have often been forced onto the streets to flee exploitation, and the new immigration law could prevent them from approaching the authorities for help. Unscrupulous bosses may even use the threat of potential deportation to further exploit workers. Meanwhile, those who were already homeless could feel pressured to accept unfair work just to avoid sleeping on the streets. In some cases, people end up on the street because of errors in the Home Office’s decision-making process.

The five key proposals contained in the letter to tackle the coming crisis include a request to suspend all immigration-based exclusions from welfare and homelessness assistance to adapt to these exceptional times, as well as extending the deadline to apply for settled status to prevent Europeans living in the UK from becoming undocumented. Also on the list, is a request to adequately fund self-contained, COVID-secure accommodation to protect those who are houseless.

Online campaigning organisation 38 Degrees is urging the general public to sign a number of petitions to defy the new law by making sure all main cities in the UK commit to not sharing sensitive personal data of rough sleepers with the Home Office to prevent deportations.

“Many people who moved to the UK have worked and raised families here, but when they fall on hard times their immigration status means they have ‘no recourse to public funds’ and are unable to access the support they need to keep a roof over their head,” reads the text of the petition.

So far nine London boroughs, including Islington, Lambeth and Newham, have decided to push back against the new rules.

Across the UK, there were over 4,600 people sleeping rough on a single night in autumn 2018, according to the Office for National Statistics. The figure was down 2 per cent from the  2017 peak, but it went up 165 per cent since 2010. The demographics suggest that 25 per cent were foreigners, 84 per cent were identified as male and 80 per cent of the total were over 26 years old.

This article has been published as part of an ongoing content partnership with FAIRPLANET.


In the U.K., AI will soon be used to tackle homelessness

A recently published research by the housing charity Shelter showed that there are over 320,000 homeless people in the U.K. At the same time, London rough sleeping hit a new record high, with an 18 percent rise between 2018 and 2019. The mayor of London, Sadiq Khan, described this as a “national disgrace” and blamed the crisis on welfare reforms and a lack of investment in social housing. As true as this statement is, what does this mean for those who are left to exist without a home? Can we, as a society and with the help of new technologies, fill our government and councils’ shoes and come up with a solution to this seemingly unsolvable growing problem?

Machine learning might be the solution. StreetLink, a homelessness charity that enables members of the public to connect people sleeping rough with local services that can support them, along with a team of data scientists backed by the Alan Turing Institute, has recently been looking into how machine learning could help improve the decision-making process that goes on in homelessness support. Very soon, AI could decide what to do when passersby report to StreetLink as they spot a person sleeping on the streets.

While AI interfering in charitable projects to deepen human interactions can sound worrying to some, in this case it could mean being more supportive of homeless people, and some cases even save their lives. Since StreetLink started offering its services across the U.K. in 2012, it has encountered the same problem: humans are too vague and sometimes unreliable when they call the company to report of rough sleepers.

When someone calls StreetLink to alert its team and network about a homeless person seen on the streets, they are requested to give a detailed description. Whoever is calling needs to include the rough sleeper’s gender, clothes, location and condition. Most of the time, the description is not complete enough. After receiving the call, StreetLink’s support team needs to go over the information again and make the tough call of whether or not to alert local response teams to find the rough sleeper.

At the moment, because the decision and analysis of the information given by callers is made by humans, just one in seven alerts processed by StreetLink actually results in the homeless person being found. When the weather conditions are particularly bad, there is a spike in the number of alerts, which puts StreetLink’s staff under an overwhelming amount of work. Machine learning could simplify and optimise the whole process.

Using information from past decisions, Alan Turing Institute’s team of data scientists has created a machine learning model that automatically categorises alerts. Once put into place, it would give StreetLink an immediate sense of which alerts should be prioritised—essentially taking over the decision making process from the staff. This model is set to be implemented for the first time in London over the coming months.

Meanwhile, in the U.S., Eric Rice had already seen the potential of machine learning in solving homelessness. As a professor of social work and co-founder of the University of Southern California Center for Artificial Intelligence in Society (CAIS), where he and engineering professor Milind Tambe developed predictive models for public health interventions, Rice works primarily on issues of housing and HIV prevention for homeless youth.

In Los Angeles, homelessness is measured on a much larger scale than what we are witnessing in the U.K.—an estimated 53,000 people experience homelessness on a given night, including 3,000 between the ages of 13 and 24. Just like StreetLink’s machine learning model, Rice’s one predicts and picks up the most at-risk cases. Rice, as well as members of many other community-based organisations, then go to the ‘selected’ rough sleeper to provide help.

CAIS has recently gained traction with a $1 million state grant to deploy its California HIV Research Program through 2020. The U.S. might soon adopt his model, but Rice is thinking bigger already. He shared with OZY that he’s now working on developing predictive models for suicide prevention on college campuses and endangered animal poaching in Africa.

StreetLink’s project would be a smaller-scale version of what is already being used in Los Angeles. And while the concerns surrounding AI making choices for marginalised communities should not be cast aside, it is clear that the world needs the help of innovative technologies, even if only to make these problems seem less insurmountable.