This study explores how AI-powered digital innovations are reshaping organisational accountability in a transnational governance context. As AI systems increasingly mediate decision-making in domains such as auditing and financial reporting, traditional mechanisms of accountability, based on control, transparency, and auditability, are being destabilised. We integrate the Technology Acceptance Model (TAM), Actor-Network Theory (ANT), and institutional theory to examine how organisations adopt AI technologies in response to regulatory, ethical, and cultural pressures that transcend national boundaries. We argue that accountability is co-constructed within global socio-technical networks, shaped not only by user perceptions but also by governance logics and normative expectations. Extending TAM, we incorporate compliance and legitimacy as key factors in perceived usefulness and usability. Drawing on ANT, we reconceptualise accountability as a relational and emergent property of networked assemblages. We propose two organisational strategies including internal governance reconfiguration and external actor-network engagement to foster responsible, legitimate, and globally accepted AI adoption in the accounting domain.

Intrinsically motivated exploration has proven useful for reinforcement learning, even without additional extrinsic rewards. When the environment is naturally represented as a graph, how to guide exploration best remains an open question. In this work, we propose a novel approach for exploring graph-structured data motivated by two theories of human curiosity: the information gap theory and the compression progress theory. The theories view curiosity as an intrinsic motivation to optimize for topological features of subgraphs induced by nodes visited in the environment. We use these proposed features as rewards for graph neural-network-based reinforcement learning. On multiple classes of synthetically generated graphs, we find that trained agents generalize to longer exploratory walks and larger environments than are seen during training. Our method computes more efficiently than the greedy evaluation of the relevant topological properties. The proposed intrinsic motivations bear particular relevance for recommender systems. We demonstrate that next-node recommendations considering curiosity are more predictive of human choices than PageRank centrality in several real-world graph environments.

This week's publication of the Pandora Papers–which the International Consortium of International Journalists based on a trove of private data leaked from offshore tax havens–showcased the alarming extent of fraud and corruption in the world. While big data tech like graph analytics and machine learning can help to a shine light on bad actors, we'll always be playing catch up, fraud hunters tell Datanami. The sheer numbers behind the Pandora Papers, which the ICIJ published on October 3, 2021, are staggering. The ICIJ was provided with 11.9 million documents, including text files, PDFs, images, emails, and spreadsheets, from 14 offshore tax havens, totaling 2.9 TB of data. The documents contained information about 27,000 shell companies created to protect the assets of 29,000 beneficial owners, including 130 billionaires and 330 politicians from 90 countries.

You are free to share this article under the Attribution 4.0 International license. Researchers have combined artificial intelligence with systems theory to develop a more efficient way to detect and accurately identify an epileptic seizure in real time. "Our technique allows us to get raw data, process it, and extract a feature that's more informative for the machine learning model to use," says Walter Bomela, a postdoctoral fellow in the lab of Jr-Shin Li, professor in the electrical and systems engineering department of the Washington University in St. Louis McKelvey School of Engineering. "The major advantage of our approach is to fuse signals from 23 electrodes to one parameter that can be efficiently processed with much less computing resources," Bomela says. In brain science, the current understanding of most seizures is that they occur when normal brain activity is interrupted by a strong, sudden hyper-synchronized firing of a cluster of neurons.

IMAGE: This gif was recorded during two seizures, one at 2950 seconds, the other at 9200. The top left animation is of EEG signals from three electrodes. The top right is... view more Researchers from Washington University in St. Louis' McKelvey School of Engineering have combined artificial intelligence with systems theory to develop a more efficient way to detect and accurately identify an epileptic seizure in real-time. Their results were published May 26 in the journal Scientific Reports. The research comes from the lab of Jr-Shin Li, professor in the Preston M. Green Department of Electrical & Systems Engineering, and was headed by Walter Bomela, a postdoctoral fellow in Li's lab.

Modern medicine is based on the concept of disease. Each disease has its own unique and specific pathophysiology – meaning that each disease has a biological fault that defines that disease and only that disease. Functional disorders (e.g., irritable bowel syndrome, chronic fatigue syndrome, fibromyalgia syndrome) are problematic in that no specific pathophysiology has been discovered, though the search goes on. There are a number of biological abnormalities associated with functional disorders, but they are often shared between the different functional disorders, they are not always found, and they do not uniquely define any particular functional disorder. Additionally, patients with functional disorders are polysymptomatic and the symptoms of one disorder tend to overlap to some degree with those of another disorder, leading the description of spectrum disorders.

It is quite difficult at first to precisely define'Complexity Science'. It is a new perspective of methodology and modeling approaches that are based more on reality than assumptions. Quite simply put, Complexity Science is a new way to grasp and manage reality. It does not study systems in isolation like gambling dice or planetary motion only. It studies the complex, holistic, inter-connected reality in which we actually live such as financial stock markets, social policies, economic policies, natural catastrophes and so on.

It is quite difficult at first to precisely define'Complexity Science'. It is a new perspective of methodology and modeling approaches that are based more on reality than assumptions. Quite simply put, Complexity Science is a new way to grasp and manage reality. It does not study systems in isolation like gambling dice or planetary motion only. It studies the complex, holistic, inter-connected reality in which we actually live such as financial stock markets, social policies, economic policies, natural catastrophes and so on.

We examine some ways in which contemporary results from neural network theory can potentially contribute to a Buddhist understanding of emptiness. We also make some general remarks about the pitfalls and benefits inherent in attempting to apply ideas from artificial intelligence to an understanding of Buddhism.

There is growing interest in automating the detection of interesting new developments in science and technology. BAE Systems is pursuing ARBITER (Abductive Reasoning Based on Indicators and Topics of EmeRgence), a multi-disciplinary study and development effort to analyze full- text and metadata for indicators of emergent technologies and scientific fields. To define these indicators, our team has applied the primary insights of actant network theory developed within the disciplines of Science and Technology Studies and the history of technology and science to create a pragmatic theory of technoscientific emergence. Specifically, this practical theory articulates emergence in terms of the robustness of actant networks. This applied actant-network theory currently guides our definition of indicators and indicator patterns for the ARBITER system, and represents a novel contribution to the discussion of emergent technologies and fields. Several elements of our theory were validated with 15 case studies and 25 example technologies.