In January 2017, a group of artificial intelligence researchers gathered at the Asilomar Conference Grounds in California and developed 23 principles for artificial intelligence, which was later dubbed the Asilomar AI Principles. The sixth principle states that "AI systems should be safe and secure throughout their operational lifetime, and verifiably so where applicable and feasible." Thousands of people in both academia and the private sector have since signed on to these principles, but, more than three years after the Asilomar conference, many questions remain about what it means to make AI systems safe and secure. Verifying these features in the context of a rapidly developing field and highly complicated deployments in health care, financial trading, transportation, and translation, among others, complicates this endeavor. Much of the discussion to date has centered on how beneficial machine learning algorithms may be for identifying and defending against computer-based vulnerabilities and threats by automating the detection of and response to attempted attacks.1 Conversely, concerns have been raised that using AI for offensive purposes may make cyberattacks increasingly difficult to block or defend against by enabling rapid adaptation of malware to adjust to restrictions imposed by countermeasures and security controls.2

What if artillery rounds, mini-drone swarms of explosives, rockets and even air-fired missiles are all approaching forward-positioned Army troops at the same time? Imagine that these incoming weapons are dispersed, varied and fast approaching. How do ground commanders avoid being overwhelmed and destroyed? Could AI help solve this lethal predicament? 

In October, Amazon had to discontinue an artificial intelligence–powered recruiting tool after it discovered the system was biased against female applicants. In 2016, a ProPublica investigation revealed a recidivism assessment tool that used machine learning was biased against black defendants. More recently, the US Department of Housing and Urban Development sued Facebook because its ad-serving algorithms enabled advertisers to discriminate based on characteristics like gender and race. And Google refrained from renewing its AI contract with the Department of Defense after employees raised ethical concerns. Those are just a few of the many ethical controversies surrounding artificial intelligence algorithms in the past few years.

Streaming data are increasingly present in real-world applications such as sensor measurements, satellite data feed, stock market, and financial data. The main characteristics of these applications are the online arrival of data observations at high speed and the susceptibility to changes in the data distributions due to the dynamic nature of real environments. The data stream mining community still faces some primary challenges and difficulties related to the comparison and evaluation of new proposals, mainly due to the lack of publicly available non-stationary real-world datasets. The comparison of stream algorithms proposed in the literature is not an easy task, as authors do not always follow the same recommendations, experimental evaluation procedures, datasets, and assumptions. In this paper, we mitigate problems related to the choice of datasets in the experimental evaluation of stream classifiers and drift detectors. To that end, we propose a new public data repository for benchmarking stream algorithms with real-world data. This repository contains the most popular datasets from literature and new datasets related to a highly relevant public health problem that involves the recognition of disease vector insects using optical sensors. The main advantage of these new datasets is the prior knowledge of their characteristics and patterns of changes to evaluate new adaptive algorithm proposals adequately. We also present an in-depth discussion about the characteristics, reasons, and issues that lead to different types of changes in data distribution, as well as a critical review of common problems concerning the current benchmark datasets available in the literature.

To address the unprecedented scale of HL-LHC data, the Exa.TrkX project is investigating a variety of machine learning approaches to particle track reconstruction. The most promising of these solutions, graph neural networks (GNN), process the event as a graph that connects track measurements (detector hits corresponding to nodes) with candidate line segments between the hits (corresponding to edges). Detector information can be associated with nodes and edges, enabling a GNN to propagate the embedded parameters around the graph and predict node-, edge- and graph-level observables. Previously, message-passing GNNs have shown success in predicting doublet likelihood, and we here report updates on the state-of-the-art architectures for this task. In addition, the Exa.TrkX project has investigated innovations in both graph construction, and embedded representations, in an effort to achieve fully learned end-to-end track finding. Hence, we present a suite of extensions to the original model, with encouraging results for hitgraph classification. In addition, we explore increased performance by constructing graphs from learned representations which contain non-linear metric structure, allowing for efficient clustering and neighborhood queries of data points. We demonstrate how this framework fits in with both traditional clustering pipelines, and GNN approaches. The embedded graphs feed into high-accuracy doublet and triplet classifiers, or can be used as an end-to-end track classifier by clustering in an embedded space. A set of post-processing methods improve performance with knowledge of the detector physics. Finally, we present numerical results on the TrackML particle tracking challenge dataset, where our framework shows favorable results in both seeding and track finding.

The right to contest a decision with consequences on individuals or the society is a well-established democratic right. Despite this right also being explicitly included in GDPR in reference to automated decision-making, its study seems to have received much less attention in the AI literature compared, for example, to the right for explanation. This paper investigates the type of assurances that are needed in the contesting process when algorithmic black boxes are involved, opening new questions about the interplay of contestability and explainability. We argue that specialised complementary methodologies to evaluate automated decision-making in the case of a particular decision being contested need to be developed. Further, we propose a combination of well-established software engineering and rule-based approaches as a possible socio-technical solution to the issue of contestability, one of the new democratic challenges posed by the automation of decision making.

ML-based predictive systems are increasingly used to support decisions with a critical impact on individuals' lives such as college admission, job hiring, child custody, criminal risk assessment, etc. As a result, fairness emerged as an important requirement to guarantee that predictive systems do not discriminate against specific individuals or entire sub-populations, in particular, minorities. Given the inherent subjectivity of viewing the concept of fairness, several notions of fairness have been introduced in the literature. This paper is a survey of fairness notions that, unlike other surveys in the literature, addresses the question of "which notion of fairness is most suited to a given real-world scenario and why?". Our attempt to answer this question consists in (1) identifying the set of fairness-related characteristics of the real-world scenario at hand, (2) analyzing the behavior of each fairness notion, and then (3) fitting these two elements to recommend the most suitable fairness notion in every specific setup. The results are summarized in a decision diagram that can be used by practitioners and policy makers to navigate the relatively large catalogue of fairness notions.

The Github repo for this post contains a notebook and the data needed to generate some of the charts in this post, as well as a sample of the Plotly chart and CSV table of the results. The code can be easily tweaked if you wish to generate results for multiple speeches in one go. The data comprises six official speech transcripts taken from the websites of the Singapore Government as well as the Prime Minister's Office. These speeches focused on the Government's plans to deal with the challenges from Covid-19, and are set to frame the broader debate for Singapore's upcoming election. Some excessively long chunks of text were broken up into smaller paragraphs for a fairer assessment of the sentiment, but the vast majority of the speeches were analysed in their original form.

Iran has issued an arrest warrant and asked Interpol for help in detaining President Donald Trump and dozens of others it believes carried out the U.S. drone strike that killed a top Iranian general in Baghdad, a local prosecutor reportedly said Monday. Interpol later said it wouldn't consider Iran's request, meaning Trump faces no danger of arrest. However, the charges underscore the heightened tensions between Iran and the United States since Trump unilaterally withdrew America from Tehran's nuclear deal with world powers. Tehran prosecutor Ali Alqasimehr said Trump and 35 others whom Iran accuses of involvement in the Jan. 3 strike that killed Gen. Qassem Soleimani in Baghdad face "murder and terrorism charges," the state-run IRNA news agency reported. Alqasimehr did not identify anyone else sought other than Trump, but stressed that Iran would continue to pursue his prosecution even after his presidency ends.

In 1906, in response to shocking reports about the disgusting conditions in U.S. meat-packing facilities, Congress created the Food and Drug Administration (FDA) to ensure safe and sanitary food production. In 1934, in the wake of the worst stock market crash in U.S. history, Congress created the Securities and Exchange Commission (SEC) to regulate capital markets. In 1970, as the nation became increasingly alarmed about the deterioration of the natural environment, Congress created the Environmental Protection Agency (EPA) to ensure cleaner skies and waters. When an entire field begins to create a broad set of challenges for the public, demanding thoughtful regulation, a proven governmental approach is to create a federal agency focused specifically on engaging with and managing that field. The time has come to create a federal agency for artificial intelligence.