Hasil untuk "Law"

Leonie Kohl

P. Maurer

Drucilla Cornell, M. Rosenfeld, D. G. Carlson

D. French, L. Kotzé

J. Bekenstein

I. Brownlie, J. Crawford

Serving as a single-volume introduction to the field as a whole, Brownlie’s Principles of Public International Law seeks to present international law as a system that is based on, and helps structure, relations among states and other entities at the international level. It aims to identify the constituent elements of that system in a clear way. This ninth edition has been completely updated to take account of the many developments in international law that have occurred since the 8th edition (2012).

T. Theis, H. Wong

S. S. Stevens

X. Gabaix

R. Herrnstein

R. Shepard

A. Demirguc-Kunt, V. Maksimovic

J. Rice, G. F. Rosengren

R. Dworkin

Kenneth W. Abbott, D. Snidal

J. Gibbon

N. Coulson

Lawyers, according to Edmund Burke, are bad historians. He was referring to an unwillingness, rather than an inaptitude, on the part of early nineteenth-century English lawyers to concern themselves with the past: for contemporary jurisprudence was a pure and isolated science wherein law appeared as a body of rules, based upon objective criteria, whose nature and very existence were independent of considerations of time and place. Despite the influence of the historical school of Western jurisprudence, Burke's observation is generally valid for Middle East studies. Muslim jurisprudence in its traditional form provides an extreme example of a legal science divorced from historical considerations. Law, in classical Islamic theory, is the revealed will of God, a divinely ordained system preceding, and not preceded by, the Muslim state controlling, but not controlled by, Muslim society. There can thus be no relativistic notion of the law itself evolving as an historical phenomenon closely tied with the progress of society. The increasing number of nations that are largely Muslim or have a Muslim head of state, emphasizes the growing political importance of the Islamic world, and, as a result, the desirability of extending and expanding the understanding and appreciation of their culture and belief systems. Since history counts for much among Muslims and what happened in 632 or 656 is still a live issue, a journalistic familiarity with present conditions is not enough; there must also be some awareness of how the past has molded the present. This book is designed to give the reader a clear picture. But where there are gaps, obscurities, and differences of opinion, these are also indicated.

J. Shalf

Moore’s Law is a techno-economic model that has enabled the information technology industry to double the performance and functionality of digital electronics roughly every 2 years within a fixed cost, power and area. Advances in silicon lithography have enabled this exponential miniaturization of electronics, but, as transistors reach atomic scale and fabrication costs continue to rise, the classical technological driver that has underpinned Moore’s Law for 50 years is failing and is anticipated to flatten by 2025. This article provides an updated view of what a post-exascale system will look like and the challenges ahead, based on our most recent understanding of technology roadmaps. It also discusses the tapering of historical improvements, and how it affects options available to continue scaling of successors to the first exascale machine. Lastly, this article covers the many different opportunities and strategies available to continue computing performance improvements in the absence of historical technology drivers. This article is part of a discussion meeting issue ‘Numerical algorithms for high-performance computational science’.

A. R. Radcliffe-Brown, I. Schapera

Sandra Wachter, B. Mittelstadt, Chris Russell

In recent years a substantial literature has emerged concerning bias, discrimination, and fairness in AI and machine learning. Connecting this work to existing legal non-discrimination frameworks is essential to create tools and methods that are practically useful across divergent legal regimes. While much work has been undertaken from an American legal perspective, comparatively little has mapped the effects and requirements of EU law. This Article addresses this critical gap between legal, technical, and organisational notions of algorithmic fairness. Through analysis of EU non-discrimination law and jurisprudence of the European Court of Justice (ECJ) and national courts, we identify a critical incompatibility between European notions of discrimination and existing work on algorithmic and automat-ed fairness. A clear gap exists between statistical measures of fairness as embedded in myriad fairness toolkits and governance mechanisms and the context-sensitive, often intuitive and ambiguous discrimination metrics and evidential requirements used by the ECJ; we refer to this approach as “contextual equality.”This Article makes three contributions. First, we review the evidential requirements to bring a claim under EU non-discrimination law. Due to the disparate nature of algorithmic and human discrimination, the EU’s current requirements are too contextual, reliant on intuition, and open to judicial interpretation to be automated. Many of the concepts fundamental to bringing a claim, such as the composition of the disadvantaged and advantaged group, the severity and type of harm suffered, and requirements for the relevance and admissibility of evidence, require normative or political choices to be made by the judiciary on a case-by-case basis. We show that automating fairness or non-discrimination in Europe may be impossible because the law, by design, does not provide a static or homogenous framework suited to testing for discrimination in AI systems.Second, we show how the legal protection offered by non-discrimination law is challenged when AI, not humans, discriminate. Humans discriminate due to negative attitudes (e.g. stereotypes, prejudice) and unintentional biases (e.g. organisational practices or internalised stereotypes) which can act as a signal to victims that discrimination has occurred. Equivalent signalling mechanisms and agency do not exist in algorithmic systems. Compared to traditional forms of discrimination, automated discrimination is more abstract and unintuitive, subtle, intangible, and difficult to detect. The increasing use of algorithms disrupts traditional legal remedies and procedures for detection, investigation, prevention, and correction of discrimination which have predominantly relied upon intuition. Consistent assessment procedures that define a common standard for statistical evidence to detect and assess prima facie automated discrimination are urgently needed to support judges, regulators, system controllers and developers, and claimants.Finally, we examine how existing work on fairness in machine learning lines up with procedures for assessing cases under EU non-discrimination law. A ‘gold standard’ for assessment of prima facie discrimination has been advanced by the European Court of Justice but not yet translated into standard assessment procedures for automated discrimination. We propose ‘conditional demographic disparity’ (CDD) as a standard baseline statistical measurement that aligns with the Court’s ‘gold standard’. Establishing a standard set of statistical evidence for automated discrimination cases can help ensure consistent procedures for assessment, but not judicial interpretation, of cases involving AI and automated systems. Through this proposal for procedural regularity in the identification and assessment of auto-mated discrimination, we clarify how to build considerations of fairness into automated systems as far as possible while still respecting and enabling the contextual approach to judicial interpretation practiced under EU non-discrimination law.