Homelessness in the United States has surged to levels unseen since the Great Depression. However, existing methods for monitoring it, such as point-in-time (PIT) counts, have limitations in terms of frequency, consistency, and spatial detail. This study proposes a new approach using publicly available, crowdsourced data, specifically 311 Service Calls and street-level imagery, to track and forecast homeless tent trends in San Francisco. Our predictive model captures fine-grained daily and neighborhood-level variations, uncovering patterns that traditional counts often overlook, such as rapid fluctuations during the COVID-19 pandemic and spatial shifts in tent locations over time. By providing more timely, localized, and cost-effective information, this approach serves as a valuable tool for guiding policy responses and evaluating interventions aimed at reducing unsheltered homelessness.

This thesis explores the impact of the Climate Emergency movement on local government climate policy, using computational methods. The Climate Emergency movement sought to accelerate climate action at local government level through the mechanism of Climate Emergency Declarations (CEDs), resulting in a series of commitments from councils to treat climate change as an emergency. With the aim of assessing the potential of current large language models to answer complex policy questions, I first built and configured a system named PALLM (Policy Analysis with a Large Language Model), using the OpenAI model GPT-4. This system is designed to apply a conceptual framework for climate emergency response plans to a dataset of climate policy documents. I validated the performance of this system with the help of local government policymakers, by generating analyses of the climate policies of 11 local governments in Victoria and assessing the policymakers' level of agreement with PALLM's responses. Having established that PALLM's performance is satisfactory, I used it to conduct a large-scale analysis of current policy documents from local governments in the state of Victoria, Australia. This thesis presents the methodology and results of this analysis, comparing the results for councils which have passed a CED to those which did not. This study finds that GPT-4 is capable of high-level policy analysis, with limitations including a lack of reliable attribution, and can also enable more nuanced analysis by researchers. Its use in this research shows that councils which have passed a CED are more likely to have a recent and climate-specific policy, and show more attention to urgency, prioritisation, and equity and social justice, than councils which have not. It concludes that the ability to assess policy documents at scale opens up exciting new opportunities for policy researchers.

Now, his WeChat feed and industry group chats tell a different story. Traders are more discreet in their dealings, and prices have come back down to earth. Meanwhile, two data center projects Li is familiar with are struggling to secure further funding from investors who anticipate poor returns, forcing project leads to sell off surplus GPUs. "It seems like everyone is selling, but few are buying," he says. Just months ago, a boom in data center construction was at its height, fueled by both government and private investors.

Japanese regional banking group Senshu Ikeda Holdings' entry into the reservation-based transit bus business is aimed at stimulating the regional economy, President and CEO Atsushi Ukawa said in a recent interview. "Even regional banks in urban areas must think about serving the local community," Ukawa said of the first reservation bus operations by a regional bank in Japan. He said that the Osaka-based company will work with local governments to expand the operation area to complement public transport. Senshu Ikeda operates an "on-demand bus," which uses artificial intelligence to run according to users' desired dates, times and locations. It partnered with companies, including auto parts maker Aisin, to launch the bus operations on a trial basis in four municipalities in Osaka Prefecture in January 2023.

More than 5 million children under five years die from largely preventable or treatable medical conditions every year, with an overwhelmingly large proportion of deaths occurring in underdeveloped countries with low vaccination uptake. One of the United Nations' sustainable development goals (SDG 3) aims to end preventable deaths of newborns and children under five years of age. We focus on Nigeria, where the rate of infant mortality is appalling. In particular, low vaccination uptake in Nigeria is a major driver of more than 2,000 daily deaths of children under the age of five years. In this paper, we describe our collaboration with government partners in Nigeria to deploy ADVISER: AI-Driven Vaccination Intervention Optimiser. The framework, based on an integer linear program that seeks to maximize the cumulative probability of successful vaccination, is the first successful deployment of an AI-enabled toolchain for optimizing the allocation of health interventions in Nigeria. In this paper, we provide a background of the ADVISER framework and present results, lessons, and success stories of deploying ADVISER to more than 13,000 families in the state of Oyo, Nigeria.

The United States Environmental Protection Agency blocked its employees from accessing ChatGPT while the US State Department staff in Guinea used it to draft speeches and social media posts. Maine banned its executive branch employees from using generative artificial intelligence for the rest of the year out of concern for the state's cybersecurity. In nearby Vermont, government workers are using it to learn new programming languages and write internal-facing code, according to Josiah Raiche, the state's director of artificial intelligence. The city of San Jose, California, wrote 23 pages of guidelines on generative AI and requires municipal employees to fill out a form every time they use a tool like ChatGPT, Bard, or Midjourney. Less than an hour's drive north, Alameda County's government has held sessions to educate employees about generative AI's risks--such as its propensity for spitting out convincing but inaccurate information--but doesn't see the need yet for a formal policy.

Financial distress of municipalities, although comparable to bankruptcy of private companies, has a far more serious impact on the well-being of communities. For this reason, it is essential to detect deficits as soon as possible. Predicting financial distress in municipalities can be a complex task, as it involves understanding a wide range of factors that can affect a municipality's financial health. In this paper, we evaluate machine learning models to predict financial distress in Italian municipalities. Accounting judiciary experts have specialized knowledge and experience in evaluating the financial performance, and they use a range of indicators to make their assessments. By incorporating these indicators in the feature extraction process, we can ensure that the model is taking into account a wide range of information that is relevant to the financial health of municipalities. The results of this study indicate that using machine learning models in combination with the knowledge of accounting judiciary experts can aid in the early detection of financial distress, leading to better outcomes for the communities.

As artificial intelligence grows more ubiquitous, its potential and the challenges it presents are coming increasingly into focus. How we balance the risks and opportunities is shaping up as one of the defining questions of our era. In much the same way that cities have emerged as hubs of innovation in culture, politics, and commerce, so they are defining the frontiers of AI governance. Some examples of how cities have been taking the lead include the Cities Coalition for Digital Rights, the Montreal Declaration for Responsible AI, and the Open Dialogue on AI Ethics. Others can be found in San Francisco's ban of facial-recognition technology, and New York City's push for regulating the sale of automated hiring systems and creation of an algorithms management and policy officer.

Martin has built his AI startup within the Microsoft ecosystem. He shares interesting perspectives on how to leverage Microsoft. Sramana Mitra: Let's start by having you introduce yourself as well ICS. Sramana Mitra: What path have you traveled before coming here? Martin Neale: I am a seasoned campaigner both in bringing many new technologies into the market and commercializing them by mostly working directly with Microsoft.

Three years ago, the San Francisco Board of Supervisors made history by becoming the first city in the nation to ban use of facial recognition technology by local government. Last night, the board went in a different direction, giving police the right to kill a criminal suspect with a teleoperated robot if they believe there is an imminent threat of death to police or members of the public. Assistant police chief David Lazar said ahead of the vote that killer robots might be needed in scenarios involving mass shootings or suicide bombers, citing the Mandalay Bay shooting in Las Vegas in 2017 and the killing of five police officers in Dallas, Texas, in 2016. Dallas police ultimately used explosives strapped to a Remotec F5A bomb disposal robot--a model also possessed by the San Francisco Police Department--to kill that suspect. The new administrative code requires a police chief to authorize use of deadly force involving a robot and to first consider de-escalation or an alternative use of force.