The premise of this paper is that compliance with Trustworthy AI governance best practices and regulatory frameworks is an inherently fragmented process spanning across diverse organizational units, external stakeholders, and systems of record, resulting in process uncertainties and in compliance gaps that may expose organizations to reputational and regulatory risks. Moreover, there are complexities associated with meeting the specific dimensions of Trustworthy AI best practices such as data governance, conformance testing, quality assurance of AI model behaviors, transparency, accountability, and confidentiality requirements. These processes involve multiple steps, hand-offs, re-works, and human-in-the-loop oversight. In this paper, we demonstrate that process mining can provide a useful framework for gaining fact-based visibility to AI compliance process execution, surfacing compliance bottlenecks, and providing for an automated approach to analyze, remediate and monitor uncertainty in AI regulatory compliance processes.

Often in statistics and machine learning we are required to partition a dataset, e.g., when (i) splitting a dataset for training and testing, (ii) subsampling from Big Data for conducting tractable statistical analysis or to save storage space, (iii) generating multiple splits of a dataset for divide-and-conquer procedures to act upon, and (iv) creating k-fold cross validation sets for model tuning and validation. For this purpose, we propose a novel method named Twinning that can be used for partitioning a dataset into statistically similar sets. Twinning is motivated from the recent work on optimal data splitting for model validation, by Joseph and Vakayil (2021). For model validation, the common practice is to randomly split the dataset into training and testing sets, e.g., for an 80-20 split, 20% of the dataset is selected randomly for testing, while the remaining 80% is used for training the model. It is easy to see that such random splitting can plausibly give rise to pathological splits, wherein the training and testing sets cover roughly disjoint regions of the feature space, thereby resulting in poor testing performance of the model.

Graph neural networks (GNNs) have been shown to possess strong representation power, which can be exploited for downstream prediction tasks on graph-structured data, such as molecules and social networks. They typically learn representations by aggregating information from the K-hop neighborhood of individual vertices or from the enumerated walks in the graph. Prior studies have demonstrated the effectiveness of incorporating weighting schemes into GNNs; however, this has been primarily limited to K-hop neighborhood GNNs so far. In this paper, we aim to extensively analyze the effect of incorporating weighting schemes into walk-aggregating GNNs. Towards this objective, we propose a novel GNN model, called AWARE, that aggregates information about the walks in the graph using attention schemes in a principled way to obtain an end-to-end supervised learning method for graph-level prediction tasks. We perform theoretical, empirical, and interpretability analyses of AWARE. Our theoretical analysis provides the first provable guarantees for weighted GNNs, demonstrating how the graph information is encoded in the representation, and how the weighting schemes in AWARE affect the representation and learning performance. We empirically demonstrate the superiority of AWARE over prior baselines in the domains of molecular property prediction (61 tasks) and social networks (4 tasks). Our interpretation study illustrates that AWARE can successfully learn to capture the important substructures of the input graph.

Graph is an important data representation ubiquitously existing in the real world. However, analyzing the graph data is computationally difficult due to its non-Euclidean nature. Graph embedding is a powerful tool to solve the graph analytics problem by transforming the graph data into low-dimensional vectors. These vectors could also be shared with third parties to gain additional insights of what is behind the data. While sharing graph embedding is intriguing, the associated privacy risks are unexplored. In this paper, we systematically investigate the information leakage of the graph embedding by mounting three inference attacks. First, we can successfully infer basic graph properties, such as the number of nodes, the number of edges, and graph density, of the target graph with up to 0.89 accuracy. Second, given a subgraph of interest and the graph embedding, we can determine with high confidence that whether the subgraph is contained in the target graph. For instance, we achieve 0.98 attack AUC on the DD dataset. Third, we propose a novel graph reconstruction attack that can reconstruct a graph that has similar graph structural statistics to the target graph. We further propose an effective defense mechanism based on graph embedding perturbation to mitigate the inference attacks without noticeable performance degradation for graph classification tasks. Our code is available at https://github.com/Zhangzhk0819/GNN-Embedding-Leaks.

Japanese-American scientist Syukuro Manabe, Klaus Hasselmann of Germany and Giorgio Parisi of Italy on Tuesday won the Nobel Physics Prize for climate models and the understanding of physical systems. The Nobel committee said it was sending a message with its prize announcement just weeks before the COP26 climate summit in Glasgow, as the rate of global warming sets off alarm bells around the world. "The world leaders that haven't got the message yet, I'm not sure they will get it because we are saying it," said Thor Hans Hansson, chair of the Nobel Committee for Physics. "But … what we are saying is that the modeling of climate is solidly based in physics theory." Manabe, 90, and Hasselmann, 89, will share half of the 10 million kronor ($1.1 million) prize for their research on climate models.

In this episode of the McKinsey Global Institute's Forward Thinking podcast, host Michael Chui speaks with Jeffrey Ding, researcher and founder of the ChinAI Newsletter, about information asymmetry in artificial intelligence between China and the West. They cover why data may not be like oil, the Chinese industry adage on products, platforms, and standards, "unsexy AI," and more. An edited transcript of this episode follows. Subscribe to the series on Apple Podcasts, Google Podcasts, Spotify, Stitcher, or wherever you get your podcasts. Anna Bernasek, co-host: Michael, there's a lot of talk right now about artificial intelligence, or AI, and what it means for global competition. I'm really glad we've got a guest today that can talk to us about what's really going on, particularly when it comes to the US and China. It definitely is a fascinating topic--at least, I find it personally. I'm a former AI practitioner and more recently, at the McKinsey Global Institute, have been able to study the impact of AI on business and more broadly. And one of the reasons I'm so excited about today's conversation is because it's with somebody you probably don't know yet but probably should. He's famous in certain corners of the internet but his work, it turns out, is relevant everywhere.

It was a cold, miserable winter day on the coast of North Carolina when two brothers achieved something remarkable. On December 17, 1903, Wilbur and Orville Wright flew their bizarre-looking flying machine for 12 seconds, covering 120 feet. Less than eight years later, Cal Rodgers began the first transcontinental flight across the United States. Fifteen years after that, Charles Lindbergh completed the first solo, non-stop transatlantic flight in history, flying the Spirit of St. Louis from New York to Paris. Seven U.S. presidents later, humans walked on the moon.

The next generation of wireless technology could affect a wide range of industries, from healthcare to financial services to retail. The technology enables faster data transfer speeds -- up to 10x faster than the speeds achievable with older standards -- lower latency, and greater network capacity. As a result, 5G creates a tremendous opportunity for numerous industries, but also sets the stage for large-scale disruption. Download the free report to understand what 5G is, the industries it's disrupting, and the drivers paving the way for its implementation. As of June 2021, commercial 5G services have already been deployed across more than 1,500 cities in 60 countries worldwide, according to Viavi Solutions. The number of IoT devices -- which will rely on 5G to transmit vast amounts of data in real time -- is projected to grow from 12B in 2020 to 30B in 2025, per IoT Analytics, more than 4 devices for every person on Earth. Executives across industries are already jostling to take advantage of 5G tech -- and avoid being disrupted by it. Earnings call mentions of 5G have soared in recent years. From enabling remote robotic surgery and autonomous cars to improving crop management, 5G is poised to transform many of the world's biggest industries. The impact of 5G on manufacturing could be huge. It's estimated that improved connectivity through 5G will create $13T in global economic value across industries by 2035, according to IHS Markit. A third of that total is projected to come from the manufacturing sector alone. This would enable manufacturers to build "smart factories" that rely on automation, augmented reality, and IoT. And with 5G powering large amounts of IoT devices and sensors around the factory, artificial intelligence can be integrated more deeply with operations. On fast-paced assembly lines, even microseconds of latency can cause costly disruptions for the manufacturer.

Preemptive recognition of the ethical implications of study design and algorithm choices in artificial intelligence (AI) research is an important but challenging process. AI applications have begun to transition from a promising future to clinical reality in neurology. As the clinical management of neurology is often concerned with discrete, often unpredictable, and highly consequential events linked to multimodal data streams over long timescales, forthcoming advances in AI have great potential to transform care for patients. However, critical ethical questions have been raised with implementation of the first AI applications in clinical practice. Clearly, AI will have far-reaching potential to promote, but also to endanger, ethical clinical practice. This article employs an anticipatory ethics approach to scrutinize how researchers in neurology can methodically identify ethical ramifications of design choices early in the research and development process, with a goal of preempting unintended consequences that may violate principles of ethical clinical care. First, we discuss the use of a systematic framework for researchers to identify ethical ramifications of various study design and algorithm choices. Second, using epilepsy as a paradigmatic example, anticipatory clinical scenarios that illustrate unintended ethical consequences are discussed, and failure points in each scenario evaluated. Third, we provide practical recommendations for understanding and addressing ethical ramifications early in methods development stages. Awareness of the ethical implications of study design and algorithm choices that may unintentionally enter AI is crucial to ensuring that incorporation of AI into neurology care leads to patient benefit rather than harm. AI= : artificial intelligence; ASM= : antiseizure medication; FDA= : Food and Drug Administration; RNS= : responsive neurostimulation

I am regularly asked to summarize my many posts. I thought it would be a good idea to publish on this blog, every Monday, some of the most relevant articles that I have already shared with you on my social networks. Today I will share some of the most relevant articles about Artificial Intelligence and in what form you can find it in today's life. I will also comment on the articles. People tend to desire changes, and that includes changes to their surrounding home spaces.