Ideal point estimation and dimensionality reduction have long been utilized to simplify and cluster complex, high-dimensional political data (e.g., roll-call votes and surveys) for use in analysis and visualization. These methods often work by finding the directions or principal components (PCs) on which either the data varies the most or respondents make the fewest decision errors. However, these PCs, which usually reflect the left-right political spectrum, are sometimes uninformative in explaining significant differences in the distribution of the data (e.g., how to categorize a set of highly-moderate voters). To tackle this issue, we adopt an emerging analysis approach, called contrastive learning. Contrastive learning-e.g., contrastive principal component analysis (cPCA)-works by first splitting the data by predefined groups, and then deriving PCs on which the target group varies the most but the background group varies the least. As a result, cPCA can often find `hidden' patterns, such as subgroups within the target group, which PCA cannot reveal when some variables are the dominant source of variations across the groups. We contribute to the field of contrastive learning by extending it to multiple correspondence analysis (MCA) to enable an analysis of data often encountered by social scientists---namely binary, ordinal, and nominal variables. We demonstrate the utility of contrastive MCA (cMCA) by analyzing three different surveys: The 2015 Cooperative Congressional Election Study, 2012 UTokyo-Asahi Elite Survey, and 2018 European Social Survey. Our results suggest that, first, for the cases when ordinary MCA depicts differences between groups, cMCA can further identify characteristics that divide the target group; second, for the cases when MCA does not show clear differences, cMCA can successfully identify meaningful directions and subgroups, which traditional methods overlook.

We propose new tools for policy-makers to use when assessing and correcting fairness and bias in AI algorithms. The three tools are: - A new definition of fairness called "controlled fairness" with respect to choices of protected features and filters. The definition provides a simple test of fairness of an algorithm with respect to a dataset. This notion of fairness is suitable in cases where fairness is prioritized over accuracy, such as in cases where there is no "ground truth" data, only data labeled with past decisions (which may have been biased). - Algorithms for retraining a given classifier to achieve "controlled fairness" with respect to a choice of features and filters. Two algorithms are presented, implemented and tested. These algorithms require training two different models in two stages. We experiment with combinations of various types of models for the first and second stage and report on which combinations perform best in terms of fairness and accuracy. - Algorithms for adjusting model parameters to achieve a notion of fairness called "classification parity". This notion of fairness is suitable in cases where accuracy is prioritized. Two algorithms are presented, one which assumes that protected features are accessible to the model during testing, and one which assumes protected features are not accessible during testing. We evaluate our tools on three different publicly available datasets. We find that the tools are useful for understanding various dimensions of bias, and that in practice the algorithms are effective in starkly reducing a given observed bias when tested on new data.

While deep learning methods continue to improve This paper describes initial work on URSABench, an open in predictive accuracy on a wide range source suite of benchmarking tools for assessment of approximate of application domains, significant issues remain Bayesian inference methods applied to deep with other aspects of their performance including neural network classification tasks. URSABench includes their ability to quantify uncertainty and their benchmark models, data sets, tasks and evaluation metrics robustness. Recent advances in approximate focused on simultaneously assessing the uncertainty Bayesian inference hold significant promise for quantification performance, robustness, computational scalability addressing these concerns, but the computational and accuracy of learning and inference methods.

The threat status and criminal collaborations of potential terrorists are hidden but give rise to observable behaviours and communications. Terrorists, when acting in concert, need to communicate to organise their plots. The authorities utilise such observable behaviour and communication data to inform their investigations and policing. We present a dynamic latent network model that integrates real-time communications data with prior knowledge on individuals. This model estimates and predicts the latent strength of criminal collaboration between individuals to assist in the identification of potential cells and the measurement of their threat levels. We demonstrate how, by assuming certain plausible conditional independences across the measurements associated with this population, the network model can be combined with models of individual suspects to provide fast transparent algorithms to predict group attacks. The methods are illustrated using a simulated example involving the threat posed by a cell suspected of plotting an attack.

Providing a small set of promising candidates in place of a single prediction is well-suited for many open-ended classification tasks. Conformal Prediction (CP) is a technique for creating classifiers that produce a valid set of predictions that contains the true answer with arbitrarily high probability. In practice, however, standard CP can suffer from both low predictive and computational efficiency during inference---i.e., the predicted set is both unusably large, and costly to obtain. This is particularly pervasive in the considered setting, where the correct answer is not unique and the number of total possible answers is high. In this work, we develop two simple and complementary techniques for improving both types of efficiencies. First, we relax CP validity to arbitrary criterions of success---allowing our framework to make more efficient predictions while remaining "equivalently correct." Second, we amortize cost by conformalizing prediction cascades, in which we aggressively prune implausible labels early on by using progressively stronger classifiers---while still guaranteeing marginal coverage. We demonstrate the empirical effectiveness of our approach for multiple applications in natural language processing and computational chemistry for drug discovery.

These aren't the droids you are looking.

Can you imagine as a business owner being entitled to thousands of pounds and not even knowing? How frustrating would that be? Sounds too good to be true, doesn't it? In this information age, business owners and entrepreneurs often fails to get the correct information with regard to different government funding and tax incentives. Addressing this gap, here we present Business Butler, an artificial intelligence powered chatbot that can identify the business need through conversation, and can guide users to relevant information sources vetted by business experts.

The challenge Artificial intelligence (AI) holds the potential to vastly improve government operations and help meet the needs of citizens in new ways, ranging from traffic management to healthcare delivery to processing tax forms. But most public institutions have not yet adopted this powerful technology. While public sector officials are increasingly aware of the transformational impact of data and AI-powered solutions, the data needed for AI solutions to be developed and deployed is often neither accessible nor discoverable. Public sector officials may also lack the appropriate knowledge and expertise to make strategic buying decisions for AI-powered tools. Uncertainty about ethical considerations adds further layers of complexity. As a result, officials tend to delay buying decisions, or reduce perceived risk by concentrating their purchasing on a few known suppliers. The opportunity The World Economic Forum’s Centre for the Fourth Industrial Revolution has brought together a multistakeholder community to co-design the AI Procurement in a Box toolkit guide for governments to rethink their public procurement processes:  IntroductionGuidelines for AI procurement, presenting the general considerations to be taken when government is procuring AI-powered solutionsWorkbook for policy and procurement officials guiding them through the guidelines ChallengesPilot case studiesThis guidance aims to empower government officials to more confidently make responsible AI purchasing decisions. The tools also improve the experience for AI solutions providers by supporting the creation of transparent and innovative public procurement processes that meet their needs. Impact By co-designing these guidelines with governments, small and large businesses, civil society and academia, the intended impact is the responsible deployment of AI solutions for the public benefit of constituents. Leveraging the significant purchasing power of government in the market, the private-sector adoption of the guidelines can permeate the industry beyond the adoption by public sector organizations. Embedding the principles advocated for in the guidelines into administrative processes will also expand opportunities for new entrants and create a more competitive environment for the ethical development of AI. Further, as industry debates its own standards on these technologies, the government’s influence can help set a baseline for the harmonization of standards-setting. Project accomplishments  March–September 2019: Policy development – the World Economic Forum worked with fellows from the public and private sectors, and a multistakeholder group that also included academia and civil society organizations, to create action-orientated guidelines for government procurement of AI. October–March 2020: Pilot and Iteration – the project team validated guidelines through feedback sessions and a pilot project with the United Kingdom government, the Dubai Electricity and Water Authority and the Government of Bahrain. June 2020: Publication of the AI Procurement in a Box guide that will allow governments to effectively learn and adopt the best practices developed. Contact information For more information, contact Kay Firth-Butterfield, Head of AI and Machine Learning, World Economic Forum, at Kay.Firth-Butterfield@weforum.org.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The much-anticipated, high-tech B-21 bomber will "come on in two years," bringing new dimensions of stealth, software, attack possibilities and nuclear deterrence to the U.S. Air Force. It would even possibly usher in new tactical approaches to how modern operations may move forward in the years ahead. In a conversation with the Mitchell Institute for Aerospace Studies regarding the importance of modernizing the nuclear triad, Air Force Chief of Staff General Stephen Wilson confirmed that the stealthy new aircraft will "come on in two years."

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Senior Air Force commanders are employing new tactics, technologies and protocols to better safeguard drones from being shot down by enemy fire during missions. Air Force Gen. Jeffrey Harrigian, the commander of U.S. Forces Europe, recently told reporters that senior U.S. military leaders are now in an effort to increase mission survivability for combat drones operating in high-risk areas. Responding to a question about an MQ-9 Reaper being shot down over Yemen last year, Harrigian emphasized that drone operations need to become less predictable to enemies. "There is something to be said for operating in a manner that offers us an opportunity to not be as predictable as we have been.