The Global Governance of Artificial Intelligence: Next Steps for Empirical and Normative Research

Artificial intelligence (AI) represents a technological upheaval with the potential to change human society. Because of its transformative potential, AI is increasingly becoming subject to regulatory initiatives at the global level. Yet, so far, scholarship in political science and international relations has focused more on AI applications than on the emerging architecture of global AI regulation. The purpose of this article is to outline an agenda for research into the global governance of AI. The article distinguishes between two broad perspectives: an empirical approach, aimed at mapping and explaining global AI governance; and a normative approach, aimed at developing and applying standards for appropriate global AI governance. The two approaches offer questions, concepts, and theories that are helpful in gaining an understanding of the emerging global governance of AI. Conversely, exploring AI as a regulatory issue offers a critical opportunity to refine existing general approaches to the study of global governance

Inequality and Immigration Do Not Necessarily Increase Welfare Chauvinism - A Replication of Two Influential Studies

How do inequality and immigration affect support for redistribution? We study this question in light of the Covid-19 pandemic, which has elevated the salience of redistributive policies and decreased the salience of immigration. We build upon studies of Magni (2020) and Alesina, Miano and Stantcheva (2023) that showed that priming respondents on inequality or immigration can link to preferences for redistribution (the former positively, the latter negatively) and increase welfare chauvinism. We revisit these claims drawing on survey-experimental primes in a quotarepresentative sample of around (N = 1.587) German citizens. Our findings are partly contrary to prior evidence, underlining that previous studies may be context-dependent on times of exceptionally high immigration salience

The Global Governance of Artificial Intelligence : Next Steps for Empirical and Normative Research

Artificial intelligence (AI) represents a technological upheaval with the potential to change human society. Because of its transformative potential, AI is increasingly becoming subject to regulatory initiatives at the global level. Yet, so far, scholarship in political science and international relations has focused more on AI applications than on the emerging architecture of global AI regulation. The purpose of this article is to outline an agenda for research into the global governance of AI. The article distinguishes between two broad perspectives: an empirical approach, aimed at mapping and explaining global AI governance; and a normative approach, aimed at developing and applying standards for appropriate global AI governance. The two approaches offer questions, concepts, and theories that are helpful in gaining an understanding of the emerging global governance of AI. Conversely, exploring AI as a regulatory issue offers a critical opportunity to refine existing general approaches to the study of global governance.La inteligencia artificial (IA) representa una revolución tecnológica que tiene el potencial de poder cambiar la sociedad humana. Debido a este potencial transformador, la IA está cada vez más sujeta a iniciativas reguladoras a nivel global. Sin embargo, hasta ahora, el mundo académico en el área de las ciencias políticas y las relaciones internacionales se ha centrado más en las aplicaciones de la IA que en la arquitectura emergente de la regulación global en materia de IA. El propósito de este artículo es esbozar una agenda para la investigación sobre la gobernanza global en materia de IA. El artículo distingue entre dos amplias perspectivas: por un lado, un enfoque empírico, destinado a mapear y explicar la gobernanza global en materia de IA y, por otro lado, un enfoque normativo, destinado a desarrollar y a aplicar normas para una gobernanza global adecuada de la IA. Los dos enfoques ofrecen preguntas, conceptos y teorías que resultan útiles para comprender la gobernanza global emergente en materia de IA. Por el contrario, el hecho de estudiar la IA como si fuese una cuestión reguladora nos ofrece una oportunidad de gran relevancia para poder perfeccionar los enfoques generales existentes en el estudio de la gobernanza global.L'intelligence artificielle (IA) constitue un bouleversement technologique qui pourrait bien changer la société humaine. À cause de son potentiel transformateur, l'IA fait de plus en plus l'objet d'initiatives réglementaires au niveau mondial. Pourtant, jusqu'ici, les chercheurs en sciences politiques et relations internationales se sont davantage concentrés sur les applications de l'IA que sur l’émergence de l'architecture de la réglementation mondiale de l'IA. Cet article vise à exposer les grandes lignes d'un programme de recherche sur la gouvernance mondiale de l'IA. Il fait la distinction entre deux perspectives larges : une approche empirique, qui vise à représenter et expliquer la gouvernance mondiale de l'IA; et une approche normative, qui vise à mettre au point et appliquer les normes d'une gouvernance mondiale de l'IA adéquate. Les deux approches proposent des questions, des concepts et des théories qui permettent de mieux comprendre l’émergence de la gouvernance mondiale de l'IA. À l'inverse, envisager l'IA telle une problématique réglementaire présente une opportunité critique d'affiner les approches générales existantes de l’étude de la gouvernance mondiale.

Multiscale X-ray phase contrast imaging of human cartilage for investigating osteoarthritis formation

Abstract Background The evolution of cartilage degeneration is still not fully understood, partly due to its thinness, low radio-opacity and therefore lack of adequately resolving imaging techniques. X-ray phase-contrast imaging (X-PCI) offers increased sensitivity with respect to standard radiography and CT allowing an enhanced visibility of adjoining, low density structures with an almost histological image resolution. This study examined the feasibility of X-PCI for high-resolution (sub-) micrometer analysis of different stages in tissue degeneration of human cartilage samples and compare it to histology and transmission electron microscopy. Methods Ten 10%-formalin preserved healthy and moderately degenerated osteochondral samples, post-mortem extracted from human knee joints, were examined using four different X-PCI tomographic set-ups using synchrotron radiation the European Synchrotron Radiation Facility (France) and the Swiss Light Source (Switzerland). Volumetric datasets were acquired with voxel sizes between 0.7 × 0.7 × 0.7 and 0.1 × 0.1 × 0.1 µm3. Data were reconstructed by a filtered back-projection algorithm, post-processed by ImageJ, the WEKA machine learning pixel classification tool and VGStudio max. For correlation, osteochondral samples were processed for histology and transmission electron microscopy. Results X-PCI provides a three-dimensional visualization of healthy and moderately degenerated cartilage samples down to a (sub-)cellular level with good correlation to histologic and transmission electron microscopy images. X-PCI is able to resolve the three layers and the architectural organization of cartilage including changes in chondrocyte cell morphology, chondrocyte subgroup distribution and (re-)organization as well as its subtle matrix structures. Conclusions X-PCI captures comprehensive cartilage tissue transformation in its environment and might serve as a tissue-preserving, staining-free and volumetric virtual histology tool for examining and chronicling cartilage behavior in basic research/laboratory experiments of cartilage disease evolution