We review practical challenges in building and deploying ethical AI at the scale of contemporary industrial and societal uses. Apart from the purely technical concerns that are the usual focus of academic research, the operational challenges of inconsistent regulatory pressures, conflicting business goals, data quality issues, development processes, systems integration practices, and the scale of deployment all conspire to create new ethical risks. Such ethical concerns arising from these practical considerations are not adequately addressed by existing research results. We argue that a holistic consideration of ethics in the development and deployment of AI systems is necessary for building ethical AI in practice, and exhort researchers to consider the full operational contexts of AI systems when assessing ethical risks.

Regulators have signalled an interest in adopting explainable AI(XAI) techniques to handle the diverse needs for model governance, operational servicing, and compliance in the financial services industry. In this short overview, we review the recent technical literature in XAI and argue that based on our current understanding of the field, the use of XAI techniques in practice necessitate a highly contextualized approach considering the specific needs of stakeholders for particular business applications.

We introduce the needs for explainable AI that arise from Standard No. 239 from the Basel Committee on Banking Standards (BCBS 239), which outlines 11 principles for effective risk data aggregation and risk reporting for financial institutions. Of these, explainableAI is necessary for compliance in two key aspects: data quality, and appropriate reporting for multiple stakeholders. We describe the implementation challenges for one specific regulatory requirement:that of having a complete data taxonomy that is appropriate for firmwide use. The constantly evolving nature of financial ontologies necessitate a continuous updating process to ensure ongoing compliance.

The RDF2vec method for creating node embeddings on knowledge graphs is based on word2vec, which, in turn, is agnostic towards the position of context words. In this paper, we argue that this might be a shortcoming when training RDF2vec, and show that using a word2vec variant which respects order yields considerable performance gains especially on tasks where entities of different classes are involved.

The death of a 33-year-old Sri Lankan migrant, trapped in the bowels of Japan's immigration system, triggered national calls to reform the bureaucracy that allowed her to waste away in a detention center without proper medical treatment. A government report Tuesday detailed the missteps that contributed to the tragedy, including insufficient medical resources, communication failures and a lack of proper oversight. But activists and politicians said the proposed changes did not go far enough to address the fundamental failures in an immigration system they describe as opaque and capricious. The nearly 280-page document describes the series of events that led to the death in March of Ratnayake Liyanage Wishma Sandamali, who had been detained for overstaying her visa. While the report said her death was the "result of illness," it noted the possibility that her health was affected by several factors, "making it difficult to concretely determine the cause."

It's Tom here, covering Tristan as he takes a two-week sojourn through time and space. I've been struck by the recent influx of AI systems that promise to predict attacks by enemy combatants. In the last week alone, two eye-catching examples have emerged. The first was courtesy of the US military. General Glen VanHerck said the Pentagon is developing AI that could predict events "days in advance." The system was recently tested on a simulated threat to a crucial site (think: the Panama Canal).

Deep streams of data from Earth-imaging satellites arrive in databases every day, but advanced technology and expertise are required to access and analyze the data. Now a new system, developed in research based at the University of California, Berkeley, uses machine learning to drive low-cost, easy-to-use technology that one person could run on a laptop, without advanced training, to address their local problems. Berkeley-based project could support action worldwide on climate, health, and poverty. More than 700 imaging satellites are orbiting the earth, and every day they beam vast oceans of information -- including data that reflects climate change, health, and poverty -- to databases on the ground. There's just one problem: While the geospatial data could help researchers and policymakers address critical challenges, only those with considerable wealth and expertise can access it.

Open-source software quietly affects nearly every issue in AI policy, but it is largely absent from discussions around AI policy--policymakers need to more actively consider OSS's role in AI. Open-source software (OSS), software that is free to access, use, and change without restrictions, plays a central role in the development and use of artificial intelligence (AI). Across open-source programming languages such as Python, R, C, Java, Scala, Javascript, Julia, and others, there are thousands of implementations of machine learning algorithms. OSS frameworks for machine learning, including tidymodels in R and Scikit-learn in Python, have helped consolidate many diverse algorithms into a consistent machine learning process and enabled far easier use for the everyday data scientist. There are also OSS tools specific to the especially important subfield of deep learning, which is dominated by Google's Tensorflow and Facebook's PyTorch.

The U.S. Department of Energy announced $15.1 million for three collaborative research projects at five universities to advance the development of a flexible multi-tiered data and computational infrastructure to support a diverse collection of on-demand scientific data processing tasks and computationally intensive simulations. Scientists from The University of Texas–Austin, the University of Notre Dame, Louisiana State University, and Lawrence Berkeley National Laboratory will address mitigation strategies for gulf coastal flooding events due to extreme weather with artificial intelligence and machine learning techniques that combine experimental data with computer simulations. Scientists from the University of Connecticut and Lawrence Berkeley National Laboratory will couple experimental data with simulations using AI/ML techniques to design, manufacture, and test new materials with uniquely designed properties for potential applications in batteries, sensors, and energy storage. Scientists from the University of Southern California, Argonne National Laboratory, and Lawrence Berkeley National Laboratory will develop AI/ML-based methods to simulate and experimentally verify the performance of large, distributed computing infrastructures.

Job candidates for the most in-demand IT positions are hard to land in 2021, and that's likely to continue: Postings for open IT jobs are at their highest level since 2019, according to the US Bureau of Labor Statistics. "Software and application developers, IT support specialists, systems engineers and architects, IT project managers and systems analysts are among the positions in highest demand," reports the CTIA. And jobs related to emerging technologies and skills accounted for about 28% of open IT positions, the trade group reported. We reached out to recruiters, executives, and tech pros, asking them to weigh in on the best opportunities they see in the year ahead. If you're burning out on your current gig, or feel that your role may be heading toward a dead end, consider some of these roles that offer security and steady growth for the foreseeable future.