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When your mailbag brims with 25,000 letters and emails every day, deciding which to answer first is daunting. When lurking within are pleas for help from some of the country's most vulnerable people, the stakes only get higher. That is the challenge facing the Department for Work and Pensions (DWP) as correspondence floods in from benefit applicants and claimants – of which there are more than 20 million, including pensioners, in the UK. The DWP thinks it may have found a solution in using artificial intelligence to read it all first – including handwritten missives. Human reading used to take weeks and could leave the most vulnerable people waiting for too long for help.

Like it or not, the choose-your-own-adventure period of the pandemic is upon us. Some free testing sites have closed. Whatever parts of the United States were still trying to collectively quell the pandemic have largely turned their focus away from communitywide advice. Now, even as case numbers begin to climb again and more infections go unreported, the onus has fallen on individual Americans to decide how much risk they and their neighbors face from the coronavirus -- and what, if anything, to do about it. For many people, the threats posed by COVID-19 have eased dramatically over the two years of the pandemic.

There are many recent examples of Artificial Intelligence (AI) systems being used for vulnerable people in humanitarian and disaster response contexts, with serious ethical and security-related implications. In particular, vulnerable populations are put at further risk through biases inherently built into AI systems. There are security concerns regarding their personal information being exposed and even used for persecution purposes. Yet rarely do they have a choice when it comes to the consent of surrendering such information. Now, as AI adoption grows rapidly, this report aims to develop AI principles and recommendations that would be specific to vulnerable people in the humanitarian field.

Ground-breaking AI technology being developed in Scotland will soon enhance Police Scotland's use of remotely piloted aircraft systems (RPAS) to find missing and vulnerable people. The technology, thought to be the first of its kind used by police forces in the UK, is a form of machine learning that provides real-time image analysis for identifying humans in rural areas. It has been developed by a consortium of partners – CENSIS, Thales UK, University of the West of Scotland and Police Scotland. With core AI development work complete and trials of the new system already underway, the project team expects the technology to be deployed in searches for missing and vulnerable people in Scotland in the near future. The technology identifies where a human being is located, rather than an individual.

This opinion piece was written by Petra Molnar and Samer Muscati, of the International Human Rights Program at the University of Toronto. It also appears on our refugees and migration newsfeed. The detention of migrants at the U.S.-Mexico border in every single case presented; the wrongful deportation of 7,000 foreign students accused of cheating on a language test; racist or sexist discrimination based on social media profile or appearance -- what do these seemingly disparate examples have in common? In every case, an algorithm made a decision with serious consequences for people's lives. Algorithms and artificial intelligence (AI) are starting to augment human decision-making in Canada's immigration and refugee system, with significant implications for the fundamental human rights of those subjected to these technologies.

Nathan Stone (NS) and Peter McLean (PM) are a team both professionally at Haven Power, a business energy supplier in Ipswich, UK, and also off the clock when they recently collaborated to create SafeHaven, the third place winner in the AWS DeepLens Challenge. SafeHaven was designed to protect vulnerable people, by enabling them to identify "who is at the door?" using an Alexa Skill. Unknown visitors trigger SMS or email alerts to relatives or carers, via an SNS subscription. Nathan, a BI Developer and Pete, a Data Architect, have been using AWS services to design and build the BI platform at Haven Power. However, prior to using AWS DeepLens they had no machine learning (ML) experience and didn't know where to begin.