Warning: Contents of this paper may be upsetting. Public attitudes towards key societal issues, expressed on online media, are of immense value in policy and reform efforts, yet challenging to understand at scale. We study one such social issue: homelessness in the U.S., by leveraging the remarkable capabilities of large language models to assist social work experts in analyzing millions of posts from Twitter. We introduce a framing typology: Online Attitudes Towards Homelessness (OATH) Frames: nine hierarchical frames capturing critiques, responses and perceptions. We release annotations with varying degrees of assistance from language models, with immense benefits in scaling: 6.5x speedup in annotation time while only incurring a 3 point F1 reduction in performance with respect to the domain experts. Our experiments demonstrate the value of modeling OATH-Frames over existing sentiment and toxicity classifiers. Our large-scale analysis with predicted OATH-Frames on 2.4M posts on homelessness reveal key trends in attitudes across states, time periods and vulnerable populations, enabling new insights on the issue. Our work provides a general framework to understand nuanced public attitudes at scale, on issues beyond homelessness.

Homelessness is a social and health problem with great repercussions in Europe. Many non-governmental organisations help homeless people by collecting and analysing large amounts of information about them. However, these tasks are not always easy to perform, and hinder other of the organisations duties. The SINTECH project was created to tackle this issue proposing two different tools: a mobile application to quickly and easily collect data; and a software based on artificial intelligence which obtains interesting information from the collected data. The first one has been distributed to some Spanish organisations which are using it to conduct surveys of homeless people. The second tool implements different feature selection and association rules mining methods. These artificial intelligence techniques have allowed us to identify the most relevant features and some interesting association rules from previously collected homeless data.

The criminalization of poverty has been widely denounced as a collective bias against the most vulnerable. NGOs and international organizations claim that the poor are blamed for their situation, are more often associated with criminal offenses than the wealthy strata of society and even incur criminal offenses simply as a result of being poor. While no evidence has been found in the literature that correlates poverty and overall criminality rates, this paper offers evidence of a collective belief that associates both concepts. This brief report measures the societal bias that correlates criminality with the poor, as compared to the rich, by using Natural Language Processing (NLP) techniques in Twitter. The paper quantifies the level of crime-poverty bias in a panel of eight different English-speaking countries. The regional differences in the association between crime and poverty cannot be justified based on different levels of inequality or unemployment, which the literature correlates to property crimes. The variation in the observed rates of crime-poverty bias for different geographic locations could be influenced by cultural factors and the tendency to overestimate the equality of opportunities and social mobility in specific countries. These results have consequences for policy-making and open a new path of research for poverty mitigation with the focus not only on the poor but on society as a whole. Acting on the collective bias against the poor would facilitate the approval of poverty reduction policies, as well as the restoration of the dignity of the persons affected.

This paper presents a large anonymized dataset of homelessness alongside insights into the data-driven rehabilitation of homeless people. The dataset was gathered by a large nonprofit organization working on rehabilitating the homeless for twenty years. This is the first dataset that we know of that contains rich information on thousands of homeless individuals seeking rehabilitation. We show how data analysis can help to make the rehabilitation of homeless people more effective and successful. Thus, we hope this paper alerts the data science community to the problem of homelessness.

'San Fransicko' author Michael Shellenberger discusses the homeless crisis in California and how to solve it. A federal judge has issued a temporary ban on San Francisco clearing most homeless encampments amid an ongoing lawsuit against the city filed by advocacy groups seeking to stop police sweeps of homeless encampments. Last week, Magistrate Judge Donna M. Ryu in the U.S. District Court in Oakland questioned the tactics used by the city of San Francisco in its homeless encampment cleanups, suggesting that the city is not adhering to its own policies of providing shelter beds to individuals who are being asked to vacate a public area. In her decision, Ryu stated that the city did not offer shelter to homeless individuals before clearing encampments and confiscating their property. The judge also found the city's justification for taking enforcement actions to be "wholly unconvincing," stating that the defendants did not adequately dispute that they cleared people without first providing shelter.

Lee's technical explanations sit alongside Chen's fictional short stories to produce an exploration of the perils and possibilities of AI. This story, translated by Emily Jin, revolves around a Nigerian video producer who is recruited to make an undetectable deepfake. Touching on impending breakthroughs in computer vision, biometrics, and AI security, it imagines a future world marked by cat-and-mouse games between deepfakers and detectors, and between defenders and perpetrators. As the light-rail train inched into Yaba station, Amaka pushed a button next to the door of his carriage. Even before the train came to a complete stop, the doors opened with a whoosh and Amaka hopped off.

Lee's technical explanations sit alongside Chen's fictional short stories to produce an exploration of the perils and possibilities of AI. This story revolves around a Nigerian video producer who is recruited to make an undetectable deepfake. Touching on impending breakthroughs in computer vision, biometrics, and AI security, it imagines a future world marked by cat-and-mouse games between deepfakers and detectors, and between defenders and perpetrators. As the light-rail train inched into Yaba station, Amaka pushed a button next to the door of his carriage. Even before the train came to a complete stop, the doors opened with a whoosh and Amaka hopped off.

Banks have long embraced surveillance systems to prevent robbery. But they're also using the technology to monitor customers, workers, and homeless people. Several US banking giants are implementing AI cameras to analyze customer preferences, track what staff are doing, and observe activities around their premises, Reuters reports. The tools are being used for a variety of purposes. Wells Fargo is leveraging the tech to prevent fraud, while City National plans to deploy facial recognition near ATMs as authentication methods.

TORONTO – As makeshift tent cities spring up across Canada to house rough sleepers who fear using shelters due to COVID-19, one city is leveraging artificial intelligence (AI) to predict which residents risk becoming homeless. Computer programmers working for the city of London, Ontario, 170km southwest of the provincial capital Toronto, say the new system is the first of its kind anywhere – and it could offer insights for other regions grappling with homelessness. "Shelters are just packed to the brim across the country right now," said Jonathan Rivard, London's Homeless Prevention Manager, who works on the AI system. "We need to do a better job of providing resources to individuals before they hit rock bottom, not once they do," he told the Thomson Reuters Foundation. Canada is seeing a second wave of coronavirus cases, with Ontario's government warning the province could experience "worst-case scenarios seen in northern Italy and New York City" if trends continue.

When you hear the word "cyborg," scenes from the 1980s films RoboCop or The Terminator might spring to mind. But the futuristic characters made famous in those films may no longer be mere science fiction. We are at the advent of an era where digital technology and artificial intelligence are moving more deeply into our human biological sphere. Humans are already able to control a robotic arm with their minds. Cyborgs--humans whose skills and abilities exceed those of others because of electrical or mechanical elements built into the body--are already among us.