Blame attribution is one of the key aspects of accountable decision making, as it provides means to quantify the responsibility of an agent for a decision making outcome. In this paper, we study blame attribution in the context of cooperative multi-agent sequential decision making. As a particular setting of interest, we focus on cooperative decision making formalized by Multi-Agent Markov Decision Processes (MMDP), and we analyze different blame attribution methods derived from or inspired by existing concepts in cooperative game theory. We formalize desirable properties of blame attribution in the setting of interest, and we analyze the relationship between these properties and the studied blame attribution methods. Interestingly, we show that some of the well known blame attribution methods, such as Shapley value, are not performance-incentivizing, while others, such as Banzhaf index, may over-blame agents. To mitigate these value misalignment and fairness issues, we introduce a novel blame attribution method, unique in the set of properties it satisfies, which trade-offs explanatory power (by under-blaming agents) for the aforementioned properties. We further show how to account for uncertainty about agents' decision making policies, and we experimentally: a) validate the qualitative properties of the studied blame attribution methods, and b) analyze their robustness to uncertainty.

Recently, the use of sound measures and metrics in Artificial Intelligence has become the subject of interest of academia, government, and industry. Efforts towards measuring different phenomena have gained traction in the AI community, as illustrated by the publication of several influential field reports and policy documents. These metrics are designed to help decision takers to inform themselves about the fast-moving and impacting influences of key advances in Artificial Intelligence in general and Machine Learning in particular. In this paper we propose to use such newfound capabilities of AI technologies to augment our AI measuring capabilities. We do so by training a model to classify publications related to ethical issues and concerns. In our methodology we use an expert, manually curated dataset as the training set and then evaluate a large set of research papers. Finally, we highlight the implications of AI metrics, in particular their contribution towards developing trustful and fair AI-based tools and technologies. Keywords: AI Ethics; AI Fairness; AI Measurement. Ethics in Computer Science.

Statistical signal processing applications usually require the estimation of some parameters of interest given a set of observed data. These estimates are typically obtained either by solving a multi-variate optimization problem, as in the maximum likelihood (ML) or maximum a posteriori (MAP) estimators, or by performing a multi-dimensional integration, as in the minimum mean squared error (MMSE) estimators. Unfortunately, analytical expressions for these estimators cannot be found in most real-world applications, and the Monte Carlo (MC) methodology is one feasible approach. MC methods proceed by drawing random samples, either from the desired distribution or from a simpler one, and using them to compute consistent estimators. The most important families of MC algorithms are Markov chain MC (MCMC) and importance sampling (IS). On the one hand, MCMC methods draw samples from a proposal density, building then an ergodic Markov chain whose stationary distribution is the desired distribution by accepting or rejecting those candidate samples as the new state of the chain. On the other hand, IS techniques draw samples from a simple proposal density, and then assign them suitable weights that measure their quality in some appropriate way. In this paper, we perform a thorough review of MC methods for the estimation of static parameters in signal processing applications. A historical note on the development of MC schemes is also provided, followed by the basic MC method and a brief description of the rejection sampling (RS) algorithm, as well as three sections describing many of the most relevant MCMC and IS algorithms, and their combined use.

As drought- and wind-driven wildfires have become more dangerous across the American West in recent years, firefighters have tried to become smarter in how they prepare. They're using new technology and better positioning of resources in a bid to keep small blazes from erupting into mega-fires like the ones that torched a record 4% of California last year, or the nation's biggest wildfire this year that has charred a section of Oregon half the size of Rhode Island. There have been 730 more wildfires in California so far this year than last, an increase of about 16%. But nearly triple the area has burned -- 470 square miles. Catching fires more quickly gives firefighters a better chance of keeping them small.

Fox News congressional correspondent Jacqui Heinrich has the latest from Capitol Hill on'America Reports' The FBI was tipped off to a Texas man arrested Friday for allegedly assaulting police officers during the Capitol riot after messaging with a woman he met on the dating app Bumble in January, the Justice Department announced. Andrew Quentin Taake, 32, was charged with assaulting an officer, obstructing an official proceeding, and other offenses for his actions during the riot, which allegedly included pepper-spraying several officers and assaulting others with a whip-like weapon. The FBI received a tip from a woman he met on the online dating app, Bumble, on Jan. 9. Screenshots of their messages show that Taake sent the woman a selfie that was taken "about 30 minutes after being sprayed," allegedly telling the potential suitor that he was at the riot "from the very beginning." A woman who Andrew Quentin Taake matched with on Bumble tipped off the FBI about his alleged Capitol riot involvement. Taake allegedly flew to Washington, D.C., from Houston the day before the riot and returned home a few days later.

Titled "Investing in trustworthy AI," the 82-page report from Deloitte and the Chamber Technology Engagement Center sought to identify the concerns that technology experts have when it comes to the adoption of AI, as well as highlight the impact that government investment in AI can have on the emerging technology. For instance, the survey found that 66% of respondents indicated that "the government could mitigate unwanted biases" and found 69% suggested that "the government could encourage accountability for AI decisions." Two-thirds of survey-takers want the government to reduce the impact of job loss due to AI, while 72% said the government could "mitigate acceleration of social divides between workers with and without AI skills." "Broadly, respondents overwhelmingly supported the notion that government intervention could enhance the benefits of AI and thus contribute to increased AI trustworthiness," the report states. One-quarter of patents granted by the United States Patent and Trademark Office use AI technologies in some shape or form, reports Deloitte, which claims that the economic impact of AI will be somewhere between $447 billion and $1.43 trillion over the next five years.

The Army Engineer Research and Development Center (ERDC) is working with Microsoft to improve climate modeling and natural disaster resilience planning through the use of predictive analytics-powered, cloud-based tools and artificial intelligence services. Under a new agreement, ERDC will test the scalability of its coastal storm modeling system, CSTORM-MS -- which was previously run on high-performance computers -- inside Microsoft's Azure Government cloud. The CSTORM-MS models provide can give coastal communities a robust, standardized approach for determining the risk of future storm events and for evaluating flood risk reduction measures caused by tropical and extra-tropical storms, as well as wind, wave and water levels. Currently, CSTORM-MS models are run at ERDC's Department of Defense Supercomputing Resource Center. In 2020, ERDC worked with DOD's High Performance Modernization Program's (HPCMP) on a feasibility study testing whether CSTORM-MS could be run in a commercial cloud.

Using satellites, drones and artificial intelligence, emerging technology is changing the way firefighting agencies and governments battle the ever-increasing threat of wildfires as hundreds of thousands of acres burn across the western United States. New programs are being developed by startups and research institutions to predict fire behavior, monitor drought and even detect fires when they first start. As climate change continues to increase the intensity and frequency of wildfires, these breakthroughs offer at least one tool in the growing arsenal of prevention and suppression strategies. "This is not to replace firefighting on the ground," said Ilkay Altintas, a computer scientist with the University of California, San Diego, who developed a fire map for the region. "The more science and data we can give firefighters and the public, the quicker we'll have solutions to combat and mitigate wildfires."

A group of researchers is using artificial intelligence techniques to calibrate some of NASA's images of the Sun, helping improve the data that scientists use for solar research. The new technique was published in the journal Astronomy & Astrophysics on April 13, 2021. A solar telescope has a tough job. Staring at the Sun takes a harsh toll, with a constant bombardment by a never-ending stream of solar particles and intense sunlight. Over time, the sensitive lenses and sensors of solar telescopes begin to degrade.

Clinicians and surgeons are increasingly using medical devices based on artificial intelligence. These AI devices, which rely on data-driven algorithms to inform health care decisions, presently aid in diagnosing cancers, heart conditions and diseases of the eye, with many more applications on the way. In a new study, Stanford faculty discuss sex, gender and race bias in medical technologies. Pulse oximeters, for example, are more likely to incorrectly report blood gas levels in dark-skinned individuals and in women. Given this surge in AI, two Stanford University faculty members are calling for efforts to ensure that this technology does not exacerbate existing heath care disparities.