Bridging the Global Divide in AI Regulation: A Proposal for a Contextual, Coherent, and Commensurable Framework

This paper examines the current landscape of AI regulations, highlighting the divergent approaches being taken, and proposes an alternative contextual, coherent, and commensurable (3C) framework. The EU, Canada, South Korea, and Brazil follow a horizontal or lateral approach that postulates the homogeneity of AI systems, seeks to identify common causes of harm, and demands uniform human interventions. In contrast, the U.K., Israel, Switzerland, Japan, and China have pursued a context-specific or modular approach, tailoring regulations to the specific use cases of AI systems. The U.S. is reevaluating its strategy, with growing support for controlling existential risks associated with AI. Addressing such fragmentation of AI regulations is crucial to ensure the interoperability of AI. The present degree of proportionality, granularity, and foreseeability of the EU AI Act is not sufficient to garner consensus. The context-specific approach holds greater promises but requires further development in terms of details, coherency, and commensurability. To strike a balance, this paper proposes a hybrid 3C framework. To ensure contextuality, the framework categorizes AI into distinct types based on their usage and interaction with humans: autonomous, allocative, punitive, cognitive, and generative AI. To ensure coherency, each category is assigned specific regulatory objectives: safety for autonomous AI; fairness and explainability for allocative AI; accuracy and explainability for punitive AI; accuracy, robustness, and privacy for cognitive AI; and the mitigation of infringement and misuse for generative AI. To ensure commensurability, the framework promotes the adoption of international industry standards that convert principles into quantifiable metrics. In doing so, the framework is expected to foster international collaboration and standardization without imposing excessive compliance costs

Exploring users’ desire for transparency and control in news recommender systems: A five-nation study

As news organizations increasingly adopt AI-driven technologies like news recommender systems (NRS), notions of responsible artificial intelligence (AI) design have attracted significant scholarly and public attention. Particularly, transparency and user control are crucial for the fulfilment of democratic freedoms and human rights. However, users’ desire for such features in NRS has not yet been sufficiently examined. Therefore, this study set out to comparatively examine how individual-level characteristics contribute to the desirability of transparency and user control in NRS across five nations (Netherlands, Poland, Switzerland, United Kingdom, United States; N = 5079). We show that the desire for features of responsible NRS is shaped by individual-level characteristics like NRS-related concerns and algorithmic awareness but does not always manifest equally across different national settings. By considering the audience’s view on features of responsible NRS, our study can be a stepping stone towards responsible journalistic AI

Fairness and Diversity in Information Access Systems

Among the seven key requirements to achieve trustworthy AI proposed by the High-Level Expert Group on Artificial Intelligence (AI-HLEG) established by the European Commission (EC), the fifth requirement ("Diversity, non-discrimination and fairness") declares: "In order to achieve Trustworthy AI, we must enable inclusion and diversity throughout the entire AI system's life cycle. [...] This requirement is closely linked with the principle of fairness". In this paper, we try to shed light on how closely these two distinct concepts, diversity and fairness, may be treated by focusing on information access systems and ranking literature. These concepts should not be used interchangeably because they do represent two different values, but what we argue is that they also cannot be considered totally unrelated or divergent. Having diversity does not imply fairness, but fostering diversity can effectively lead to fair outcomes, an intuition behind several methods proposed to mitigate the disparate impact of information access systems, i.e. recommender systems and search engines.Comment: Presented at the European Workshop on Algorithmic Fairness (EWAF'23) Winterthur, Switzerland, June 7-9, 202

Robust FOREX Trading with Deep Q Network (DQN)

Financial trading is one of the most attractive areas in finance. Trading systems development is not an easy task because it requires extensive knowledge in several areas such as quantitative analysis, financial skills, and computer programming. A trading systems expert, as a human, also brings in their own bias when developing the system. There should be another, more effective way to develop the system using artificial intelligence. The aim of this study was to compare the performance of AI agents to the performance of the buy-and-hold strategy and the expert trader. The tested market consisted of 15 years of the Forex data market, from two currency pairs (EURUSD, USDJPY) obtained from Dukascopy Bank SA Switzerland. Both hypotheses were tested with a paired t-Test at the 0.05 significance level. The findings showed that AI can beat the buy & hold strategy with significant superiority, in FOREX for both currency pairs (EURUSD, USDJPY), and that AI can also significantly outperform CTA (experienced trader) for trading in EURUSD. However, the AI could not significantly outperform CTA for USDJPY trading. Limitations, contributions, and further research were recommended

An agent-based industrial cyber-physical system deployed in an automobile multi-stage production system

Industrial Cyber-Physical Systems (CPS) are promoting the development of smart machines and products, leading to the next generation of intelligent production systems. In this context, Artificial Intelligence (AI) is posed as a key enabler for the realization of CPS requirements, supporting the data analysis and the system dynamic adaptation. However, the centralized Cloud-based AI approaches are not suitable to handle many industrial scenarios, constrained by responsiveness and data sensitivity. Edge Computing can address the new challenges, enabling the decentralization of data analysis along the cyber-physical components. In this context, distributed AI approaches such as those based on Multi-agent Systems (MAS) are essential to handle the distribution and interaction of the components. Based on that, this work uses a MAS approach to design cyber-physical agents that can embed different data analysis capabilities, supporting the decentralization of intelligence. These concepts were applied to an industrial automobile multi-stage production system, where different kinds of data analysis were performed in autonomous and cooperative agents disposed along Edge, Fog and Cloud computing layers. © 2020, Springer Nature Switzerland AG.info:eu-repo/semantics/publishedVersio

Artificial Intelligence in the Construction Industry: A Systematic Review of the Entire Construction Value Chain Lifecycle

© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/In recent years, there has been a surge in the global digitization of corporate processes and concepts such as digital technology development which is growing at such a quick pace that the construction industry is struggling to catch up with latest developments. A formidable digital technology, artificial intelligence (AI), is recognized as an essential element within the paradigm of digital transformation, having been widely adopted across different industries. Also, AI is anticipated to open a slew of new possibilities for how construction projects are designed and built. To obtain a better knowledge of the trend and trajectory of research concerning AI technology application in the construction industry, this research presents an exhaustive systematic review of seventy articles toward AI applicability to the entire lifecycle of the construction value chain identified via the guidelines outlined by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The review’s findings show foremostly that AI technologies are mostly used in facility management, creating a huge opportunity for the industry to profit by allowing facility managers to take proactive action. Secondly, it shows the potential for design expansion as a key benefit according to most of the selected literature. Finally, it found data augmentation as one of the quickest prospects for technical improvement. This knowledge will assist construction companies across the world in recognizing the efficiency and productivity advantages that AI technologies can provide while helping them make smarter technology investment decisions.Peer reviewe

AI Suffrage: A four-country survey on the acceptance of an automated voting system

Governments have begun to employ technological systems that use massive amounts of data and artificial intelligence (AI) in the domains of law enforcement, public health, or social welfare. In some areas, shifts in public opinion increasingly favor technology-aided public decision-making. This development presents an opportunity to explore novel approaches to how technology could be used to reinvigorate democratic governance and how the public perceives such changes. The study therefore posits a hypothetical AI voting system that mediates political decision-making between citizens and the state. We conducted a four-country online survey (N=6043) in Greece, Singapore, Switzerland, and the US to find out what factors affect the public’s acceptance of such a system. The data show that Singaporeans are most likely and Greeks least likely to accept the system. Considerations of the technology’s utility have a large effect on acceptance rates across cultures whereas attitudes towards political norms and political performance have partial effects

Today’s architecture and new toponymy

Sono massicci gli interventi sul territorio e sulle città che operano architetti e pianificatori attuali. In tema si recano esempi spagnoli, italiani (Expo 2015), tedeschi (ricostruzione dell’Alexanderplatz), svizzeri (NEAT, Alptransit). In dialogo con i colleghi linguisti si insiste per una rinnovata attenzione ai nuovi luoghi e ai nomi nuovi che giorno dopo giorno vengono ad attorniarci. Lurati passa in rassegna nozioni come smantellamento dei territori identitari, Neue Einfachkeit (= concetto dell’architetto Hans Kollhoff), paesaggio culturale, Edge Cities, quartiere cintato (< gated community), coscienza ambientale.There are massive interventions on the countryside and in the cities which the architects and planners actually process. The essay shows examples from Spain (Parc de l’Alba), Italy (Expo 2015), Germany (reconstruction of the Alexanderplatz) and Switzerland (NEAT, Alptransit). In discussion with linguistic colleagues we insist on a renewed attention of new places and new names which day after day come to surround us. Lurati examines notions like “dismantling the territories of identità”; “Neue Einfachheit” (= concept of the architect Hans Kollhoff), “cultural countryside”; “Edge cities, quartiere cintato” (< gated community), “environmental consciousness”

Robot computing for music visualization

© Springer Nature Switzerland AG 2019. This paper presents an algorithm design of Music Visualization on Robot (MVR) which could automatically link the flashlight, color, and emotion through music. We call this algorithm as MVR algorithm that composed by two analyses. First, we focus on Music Signal Analysis. Second, we focus on Music Sentiment Analysis. We integrate two analysis results and implement the MVR algorithm on a robot called Zenbo which is released from ASUS Company. We perform the Zenbo Robot in luminous environments. The MVR system not only could be used in Zenbo robot but also could extend to other fields of Artificial Intelligent (AI) equipment in the future