The recent promotion of sustainable urban planning combined with a growing need for public interventions to improve well-being and health have led to an increased collective interest for green spaces in and around cities. In particular, parks have proven a wide range of benefits in urban areas. This also means inequities in park accessibility may contribute to health inequities. In this work, we showcase the application of classic tools from Operations Research to assist decision-makers to improve parks' accessibility, distribution and design. Given the context of public decision-making, we are particularly concerned with equity and environmental justice, and are focused on an advanced assessment of users' behavior through a spatial interaction model. We present a two-stage fair facility location and design model, which serves as a template model to assist public decision-makers at the city-level for the planning of urban green spaces. The first-stage of the optimization model is about the optimal city-budget allocation to neighborhoods based on a data exposing inequality attributes. The second-stage seeks the optimal location and design of parks for each neighborhood, and the objective consists of maximizing the total expected probability of individuals visiting parks. We show how to reformulate the latter as a mixed-integer linear program. We further introduce a clustering method to reduce the size of the problem and determine a close to optimal solution within reasonable time. The model is tested using the case study of the city of Montreal and comparative results are discussed in detail to justify the performance of the model.

Russia's savage, imperialistic and childish war on Ukraine has been said by democracies to be a battle between democracies and autocracies, the free world and the the unfree. And it is the opening of the battle to come between two very different sets of values. The other, more subtle, nefarious, insidious and perhaps deadlier in some ways, war is that of Artificial Intelligence. The abuse of AI has the capability to destroy human agency, take away any sense of free will, devastate human rights, divide societies and turn people under its thumb into automatons to serve the elites of corrupt, autocratic and dictatorial countries. To see how autocracies will use AI to subjugate and destroy any sense of human agency in their populations, we only have to look at how they've done so with social media.

DoNotPay, which describes itself as "the world's first robot lawyer," has been accused of practicing law without a license. It's facing a proposed class action lawsuit filed by Chicago-based law firm Edelson on March 3 and published Thursday on the website of the Superior Court of the State of California for the County of San Francisco. The complaint argues: "Unfortunately for its customers, DoNotPay is not actually a robot, a lawyer, nor a law firm. DoNotPay does not have a law degree, is not barred in any jurisdiction, and is not supervised by any lawyer." The lawsuit was filed on behalf of Jonathan Faridian, who said he'd used DoNotPay to draft various legal documents including demand letters, a small claims court filing, and a job discrimination complaint.

In recent years, there have been significant advances in artificial intelligence (AI) technology that have enabled computers to generate realistic images of human faces. One application of this technology is the creation of police sketches, which traditionally have been created by artists based on eyewitness descriptions. The use of AI to generate police sketches has the potential to speed up investigations and help police identify suspects more quickly. However, there are also concerns about the potential drawbacks of using this technology. One of the main concerns is accuracy.

In recent years fairness in machine learning (ML) has emerged as a highly active area of research and development. Most define fairness in simple terms, where fairness means reducing gaps in performance or outcomes between demographic groups while preserving as much of the accuracy of the original system as possible. This oversimplification of equality through fairness measures is troubling. Many current fairness measures suffer from both fairness and performance degradation, or "levelling down," where fairness is achieved by making every group worse off, or by bringing better performing groups down to the level of the worst off. When fairness can only be achieved by making everyone worse off in material or relational terms through injuries of stigma, loss of solidarity, unequal concern, and missed opportunities for substantive equality, something would appear to have gone wrong in translating the vague concept of 'fairness' into practice. This paper examines the causes and prevalence of levelling down across fairML, and explore possible justifications and criticisms based on philosophical and legal theories of equality and distributive justice, as well as equality law jurisprudence. We find that fairML does not currently engage in the type of measurement, reporting, or analysis necessary to justify levelling down in practice. We propose a first step towards substantive equality in fairML: "levelling up" systems by design through enforcement of minimum acceptable harm thresholds, or "minimum rate constraints," as fairness constraints. We likewise propose an alternative harms-based framework to counter the oversimplified egalitarian framing currently dominant in the field and push future discussion more towards substantive equality opportunities and away from strict egalitarianism by default. N.B. Shortened abstract, see paper for full abstract.

New emerging technologies powered by Artificial Intelligence (AI) have the potential to disruptively transform our societies for the better. In particular, data-driven learning approaches (i.e., Machine Learning (ML)) have been a true revolution in the advancement of multiple technologies in various application domains. But at the same time there is growing concern about certain intrinsic characteristics of these methodologies that carry potential risks to both safety and fundamental rights. Although there are mechanisms in the adoption process to minimize these risks (e.g., safety regulations), these do not exclude the possibility of harm occurring, and if this happens, victims should be able to seek compensation. Liability regimes will therefore play a key role in ensuring basic protection for victims using or interacting with these systems. However, the same characteristics that make AI systems inherently risky, such as lack of causality, opacity, unpredictability or their self and continuous learning capabilities, may lead to considerable difficulties when it comes to proving causation. This paper presents three case studies, as well as the methodology to reach them, that illustrate these difficulties. Specifically, we address the cases of cleaning robots, delivery drones and robots in education. The outcome of the proposed analysis suggests the need to revise liability regimes to alleviate the burden of proof on victims in cases involving AI technologies. This article appears in the AI & Society track.

The video above represents one of the first times that a pangolin, one of the world's most critically endangered species, was detected in real time using artificial intelligence. A U.K.-based nonprofit called Conservation AI made this possible with the help of NVIDIA technology. Such use of AI can help track even the rarest, most reclusive of species in real time, enabling conservationists to protect them from threats, such as poachers and fires, before it's too late to intervene. The organization was founded four years ago by researchers at Liverpool John Moores University -- Paul Fergus, Carl Chalmers, Serge Wich and Steven Longmore. In the past year and a half, Conservation AI has deployed 70 AI-powered cameras across the world.

As businesses, consumers and government agencies look for ways to take advantage of artificial intelligence tools, experts this week called on Congress to craft AI regulations addressing challenges facing the technology. AI concerns run the gamut from bias in algorithms that could affect decisions such as who is selected for housing and employment opportunities, to the use of deep fake AI that can artificially generate images and sounds that can imitate real human beings' appearances and voices. Yet AI has also led to the development of lifesaving drugs, advanced manufacturing and self-driving cars. Indeed, the increased adoption of artificial intelligence has led to the rapid growth of advanced technology in "virtually every sector," said Sen. Gary Peters (D-Mich.), chairman of the U.S. Senate Committee on Homeland Security and Governmental Affairs. Peters spoke during a committee hearing on AI risks and opportunities Wednesday.

But no matter how different we are, we all have one thing in common. We believe our differences are our strength. So we push for inclusion, challenge convention and bring in new perspectives, to inspire new ideas. Because when we connect by understanding what makes people different, we can create unforgettable experiences that enrich lives.

In recent years, hate speech has gained great relevance in social networks and other virtual media because of its intensity and its relationship with violent acts against members of protected groups. Due to the great amount of content generated by users, great effort has been made in the research and development of automatic tools to aid the analysis and moderation of this speech, at least in its most threatening forms. One of the limitations of current approaches to automatic hate speech detection is the lack of context. Most studies and resources are performed on data without context; that is, isolated messages without any type of conversational context or the topic being discussed. This restricts the available information to define if a post on a social network is hateful or not. In this work, we provide a novel corpus for contextualized hate speech detection based on user responses to news posts from media outlets on Twitter. This corpus was collected in the Rioplatense dialectal variety of Spanish and focuses on hate speech associated with the COVID-19 pandemic. Classification experiments using state-of-the-art techniques show evidence that adding contextual information improves hate speech detection performance for two proposed tasks (binary and multi-label prediction). We make our code, models, and corpus available for further research.