Interest in the possibilities afforded by algorithms and big data continues to blossom as early adopters gain benefits from AI systems that automate decisions as varied as making customer recommendations, screening job applicants, detecting fraud, and optimizing logistical routes.1 But when AI applications fail, they can do so quite spectacularly.2 Consider the recent example of Australia's "robodebt" scandal.3 In 2015, the Australian government established its Income Compliance Program, with the goal of clawing back unemployment and disability benefits that had been made inappropriately to recipients. It set out to identify overpayments by analyzing discrepancies between the annual income that individuals reported and the income assessed by the Australian Tax Office.

Kyiv, Ukraine – A mysterious weapon struck a target deep in Russia's heartland. On Monday morning, a deafening roar that sounded like a landing jet plane woke up a town spreadeagled in the flat steppes of the Volga River region. According to surveillance camera footage, a lightning-like flash followed by a thunderous explosion shook Engels, named after the philosopher and home to more than 300,000 people. It hit one of Russia's largest and most important military airfields that hosts strategic Tupolev Tu-160 and Tu-95 bombers. The planes are capable of carrying nuclear warheads, and Moscow has repeatedly used them to rain non-nuclear missiles on Ukraine.

According to the Department of Health and Human Services, 76% of pediatric inpatient beds are occupied across the U.S., and pediatric intensive-care beds are above 80%. Cases of respiratory syncytial virus (RSV) in the United States started showing up in the spring and are now 60% higher than 2021's peak week. As a result, pediatric hospitals across the country are under immense strain. "As we respond to this unprecedented surge in critically ill children, the Edgility Pediatric Lens is an exceptional instrument for identifying patient conditions and appropriate levels of care" Edgility's'Pediatric Lens' helps health systems across the country respond to the surge of flu and RSV patients overwhelming their pediatric populations. EdgeAi, Edgility's native Artificial Intelligence, curates data from multiple sources to orchestrate'levers of action' in real-time so staff can respond effectively and quickly to the surge in patients.

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Type "Teddy bears working on new AI research on the moon in the 1980s" into any of the recently released text-to-image artificial intelligence image generators, and after just a few seconds the sophisticated software will produce an eerily pertinent image. Seemingly bound by only your imagination, this latest trend in synthetic media has delighted many, inspired others and struck fear in some. Google, research firm OpenAI and AI vendor Stability AI have each developed a text-to-image image generator powerful enough that some observers are questioning whether in the future people will be able to trust the photographic record. As a computer scientist who specializes in image forensics, I have been thinking a lot about this technology: what it is capable of, how each of the tools have been rolled out to the public, and what lessons can be learned as this technology continues its ballistic trajectory. Although their digital precursor dates back to 1997, the first synthetic images splashed onto the scene just five years ago.

From "intelligent" vacuum cleaners and driverless cars to advanced techniques for diagnosing diseases, artificial intelligence has burrowed its way into every arena of modern life. Its promoters reckon it is revolutionising human experience, but critics stress that the technology risks putting machines in charge of life-changing decisions. Regulators in Europe and North America are worried. The European Union is likely to pass legislation next year -- the AI Act -- aimed at reining in the age of the algorithm. The United States recently published a blueprint for an AI Bill of Rights and Canada is also mulling legislation.

Motion prediction systems aim to capture the future behavior of traffic scenarios enabling autonomous vehicles to perform safe and efficient planning. The evolution of these scenarios is highly uncertain and depends on the interactions of agents with static and dynamic objects in the scene. GNN-based approaches have recently gained attention as they are well suited to naturally model these interactions. However, one of the main challenges that remains unexplored is how to address the complexity and opacity of these models in order to deal with the transparency requirements for autonomous driving systems, which includes aspects such as interpretability and explainability. In this work, we aim to improve the explainability of motion prediction systems by using different approaches. First, we propose a new Explainable Heterogeneous Graph-based Policy (XHGP) model based on an heterograph representation of the traffic scene and lane-graph traversals, which learns interaction behaviors using object-level and type-level attention. This learned attention provides information about the most important agents and interactions in the scene. Second, we explore this same idea with the explanations provided by GNNExplainer. Third, we apply counterfactual reasoning to provide explanations of selected individual scenarios by exploring the sensitivity of the trained model to changes made to the input data, i.e., masking some elements of the scene, modifying trajectories, and adding or removing dynamic agents. The explainability analysis provided in this paper is a first step towards more transparent and reliable motion prediction systems, important from the perspective of the user, developers and regulatory agencies. UTONOMOUS vehicles (AVs) have to perform trajectory planning based on the global route and the local context. Trajectory planning can be applied in a safer and more efficient way if the system is able to anticipate future motions of surrounding agents [1], as humans inherently do. Motion prediction has recently gained significant attention within the research community since it is one of the key unsolved challenges in reaching full self-driving autonomy [2]. The main goal of motion prediction is to determine a set of coordinates at a future point in time for an agent in the scene. Among the different approaches, graphs are gaining attention since traffic scenarios can be naturally represented as a graph.

Adversarial machine learning has been both a major concern and a hot topic recently, especially with the ubiquitous use of deep neural networks in the current landscape. Adversarial attacks and defenses are usually likened to a cat-and-mouse game in which defenders and attackers evolve over the time. On one hand, the goal is to develop strong and robust deep networks that are resistant to malicious actors. On the other hand, in order to achieve that, we need to devise even stronger adversarial attacks to challenge these defense models. Most of existing attacks employs a single $\ell_p$ distance (commonly, $p\in\{1,2,\infty\}$) to define the concept of closeness and performs steepest gradient ascent w.r.t. this $p$-norm to update all pixels in an adversarial example in the same way. These $\ell_p$ attacks each has its own pros and cons; and there is no single attack that can successfully break through defense models that are robust against multiple $\ell_p$ norms simultaneously. Motivated by these observations, we come up with a natural approach: combining various $\ell_p$ gradient projections on a pixel level to achieve a joint adversarial perturbation. Specifically, we learn how to perturb each pixel to maximize the attack performance, while maintaining the overall visual imperceptibility of adversarial examples. Finally, through various experiments with standardized benchmarks, we show that our method outperforms most current strong attacks across state-of-the-art defense mechanisms, while retaining its ability to remain clean visually.

This paper investigates how hate speech varies in systematic ways according to the identities it targets. Across multiple hate speech datasets annotated for targeted identities, we find that classifiers trained on hate speech targeting specific identity groups struggle to generalize to other targeted identities. This provides empirical evidence for differences in hate speech by target identity; we then investigate which patterns structure this variation. We find that the targeted demographic category (e.g. gender/sexuality or race/ethnicity) appears to have a greater effect on the language of hate speech than does the relative social power of the targeted identity group. We also find that words associated with hate speech targeting specific identities often relate to stereotypes, histories of oppression, current social movements, and other social contexts specific to identities. These experiments suggest the importance of considering targeted identity, as well as the social contexts associated with these identities, in automated hate speech classification.

As tools for content editing mature, and artificial intelligence (AI) based algorithms for synthesizing media grow, the presence of manipulated content across online media is increasing. This phenomenon causes the spread of misinformation, creating a greater need to distinguish between ``real'' and ``manipulated'' content. To this end, we present VideoSham, a dataset consisting of 826 videos (413 real and 413 manipulated). Many of the existing deepfake datasets focus exclusively on two types of facial manipulations -- swapping with a different subject's face or altering the existing face. VideoSham, on the other hand, contains more diverse, context-rich, and human-centric, high-resolution videos manipulated using a combination of 6 different spatial and temporal attacks. Our analysis shows that state-of-the-art manipulation detection algorithms only work for a few specific attacks and do not scale well on VideoSham. We performed a user study on Amazon Mechanical Turk with 1200 participants to understand if they can differentiate between the real and manipulated videos in VideoSham. Finally, we dig deeper into the strengths and weaknesses of performances by humans and SOTA-algorithms to identify gaps that need to be filled with better AI algorithms. We present the dataset at https://github.com/adobe-research/VideoSham-dataset.