Because radio spectrum is a finite resource, its usage and sharing is regulated by government agencies. These agencies define policies to manage spectrum allocation and assignment across multiple organizations, systems, and devices. With more portions of the radio spectrum being licensed for commercial use, the importance of providing an increased level of automation when evaluating such policies becomes crucial for the efficiency and efficacy of spectrum management. We introduce our Dynamic Spectrum Access Policy Framework for supporting the United States government's mission to enable both federal and non-federal entities to compatibly utilize available spectrum. The DSA Policy Framework acts as a machine-readable policy repository providing policy management features and spectrum access request evaluation. The framework utilizes a novel policy representation using OWL and PROV-O along with a domain-specific reasoning implementation that mixes GeoSPARQL, OWL reasoning, and knowledge graph traversal to evaluate incoming spectrum access requests and explain how applicable policies were used. The framework is currently being used to support live, over-the-air field exercises involving a diverse set of federal and commercial radios, as a component of a prototype spectrum management system.

The pervasive application of algorithmic decision-making is raising concerns on the risk of unintended bias in AI systems deployed in critical settings such as healthcare. The detection and mitigation of biased models is a very delicate task which should be tackled with care and involving domain experts in the loop. In this paper we introduce FairLens, a methodology for discovering and explaining biases. We show how our tool can be used to audit a fictional commercial black-box model acting as a clinical decision support system. In this scenario, the healthcare facility experts can use FairLens on their own historical data to discover the model's biases before incorporating it into the clinical decision flow. FairLens first stratifies the available patient data according to attributes such as age, ethnicity, gender and insurance; it then assesses the model performance on such subgroups of patients identifying those in need of expert evaluation. Finally, building on recent state-of-the-art XAI (eXplainable Artificial Intelligence) techniques, FairLens explains which elements in patients' clinical history drive the model error in the selected subgroup. Therefore, FairLens allows experts to investigate whether to trust the model and to spotlight group-specific biases that might constitute potential fairness issues.

Analytic software tools and workflows are increasing in capability, complexity, number, and scale, and the integrity of our workflows is as important as ever. Specifically, we must be able to inspect the process of analytic workflows to assess (1) confidence of the conclusions, (2) risks and biases of the operations involved, (3) sensitivity of the conclusions to sources and agents, (4) impact and pertinence of various sources and agents, and (5) diversity of the sources that support the conclusions. We present an approach that tracks agents' provenance with PROV-O in conjunction with agents' appraisals and evidence links (expressed in our novel DIVE ontology). Together, PROV-O and DIVE enable dynamic propagation of confidence and counter-factual refutation to improve human-machine trust and analytic integrity. We demonstrate representative software developed for user interaction with that provenance, and discuss key needs for organizations adopting such approaches. We demonstrate all of these assessments in a multi-agent analysis scenario, using an interactive web-based information validation UI.

Human use language not just to convey information but also to express their inner feelings and mental states. In this work, we adapt the state-of-the-art language generation models to generate affective (emotional) text. We posit a model capable of generating affect-driven and topic focused sentences without losing grammatical correctness as the affect intensity increases. We propose to incorporate emotion as prior for the probabilistic state-of-the-art text generation model such as GPT-2. The model gives a user the flexibility to control the category and intensity of emotion as well as the topic of the generated text. Previous attempts at modelling fine-grained emotions fall out on grammatical correctness at extreme intensities, but our model is resilient to this and delivers robust results at all intensities. We conduct automated evaluations and human studies to test the performance of our model, and provide a detailed comparison of the results with other models. In all evaluations, our model outperforms existing affective text generation models.

There has recently been increasing interest in learning representations of temporal knowledge graphs (KGs), which record the dynamic relationships between entities over time. Temporal KGs often exhibit multiple simultaneous non-Euclidean structures, such as hierarchical and cyclic structures. However, existing embedding approaches for temporal KGs typically learn entity representations and their dynamic evolution in the Euclidean space, which might not capture such intrinsic structures very well. To this end, we propose Dy- ERNIE, a non-Euclidean embedding approach that learns evolving entity representations in a product of Riemannian manifolds, where the composed spaces are estimated from the sectional curvatures of underlying data. Product manifolds enable our approach to better reflect a wide variety of geometric structures on temporal KGs. Besides, to capture the evolutionary dynamics of temporal KGs, we let the entity representations evolve according to a velocity vector defined in the tangent space at each timestamp. We analyze in detail the contribution of geometric spaces to representation learning of temporal KGs and evaluate our model on temporal knowledge graph completion tasks. Extensive experiments on three real-world datasets demonstrate significantly improved performance, indicating that the dynamics of multi-relational graph data can be more properly modeled by the evolution of embeddings on Riemannian manifolds.

Various countries across the Middle East have placed an emphasis on AI. Clear examples of that have been nation-wide strategies around AI and part of wider government digital transformations. In the Gulf Cooperation Council (GCC) region (Saudi Arabia, Qatar, Oman, Bahrain, Kuwait and the United Arab Emirates (UAE), economic development diversifications such as Saudi Vision 2030 has prioritised wider future and innovative economies. Sectors such as fintech and AI-both fintech and non-fintech related- play a strong role in that. It is not just nation-wide economic development diversification strategies such as Vision 2030 but also, complimenting and in parallel, strategies purely around AI.

In the last two years, we have seen a consistent expansion in our percent of readership in India. Since the mid 90s, the IT and ITeS services segment in India has been critical to its economy in the end developing to account 7.7% of India's GDP in 2016. While trying to profit by this foundation, the current Indian administration reported in February 2018 that the administration think-tank, National Institution for Transforming India (NITI) Aayog (Hindi for Policy Commission), will lead a national program on AI concentrating on research. This improvement comes on the heels of the launch of a Task Force on Artificial Intelligence for India's Economic Transformation by the Commerce and Industry Department of the Government of India in 2017. The business pioneers concur that artificial intelligence has positively grabbed the eye of the Indian government and the tech community lately.

In the United States, there was nothing else this week except for the presidential election. More people voted in this election than in any other previous U.S. presidential election -- a total of 143,518,226 votes and counting. As we close out a long, stressful week, it appears all but a formality that Joe Biden and Kamala Harris will be the country's next President and Vice President. Meanwhile, Donald Trump rages on in a toothless effort to hang onto power. The results of the election were not the resounding referendum against white supremacy, misogyny, xenophobia, and bigotry that many had hoped for. But at least the fears about how technology could tip the scales of this election didn't apparently come to pass -- many were concerned about numerous threats from (or enabled by) technology, from deepfakes to bots to hacking.

One of the challenges with defining artificial intelligence is that if you put 10 people in a room, you will get 11 different definitions. From our perspective at the National Institute of Standards and Technology, an AI system exhibits reasoning and performs some automated decision-making without the aid of a human. It's generally accepted that AI promises to grow the economy and improve our lives. But with these benefits, it also brings new risks. How can we be sure this technology is not just innovative and helpful, but also trustworthy, unbiased and resilient in the face of attack?

Artificial Intelligence, once a buzzword in the digital world, has become a part of our everyday life. From Google Assistant, Siri, Alexa to Uber and Ola, several AI-enabled services are available today that make our lives easier. The ongoing pandemic has undoubtedly impacted business models but it didn't wane the impact AI has on our lives and businesses. On the contrary, it has become evident that Artificial Intelligence, with its self-teaching and learning algorithms, will play an essential role in transforming businesses in 2021. Companies have swiftly started leveraging the potential of AI.