Regulations and standards in the field of artificial intelligence (AI) are necessary to minimise risks and maximise benefits, yet some argue that they stifle innovation. This paper critically examines the idea that regulation stifles innovation in the field of AI. Current trends in AI regulation, particularly the proposed European AI Act and the standards supporting its implementation, are discussed. Arguments in support of the idea that regulation stifles innovation are analysed and criticised, and an alternative point of view is offered, showing how regulation and standards can foster innovation in the field of AI.

At the World Economic Forum Annual Meeting held in Davos in May this year (2022), Minister for Communications and Information Josephine Teo (Minister Teo) announced the launch by Singapore of A.I. Verify, which is the world's first AI Governance Testing Framework and Toolkit, providing a means for companies to measure and demonstrate how safe and reliable their artificial intelligence (AI) products and services are. Singapore's launch of A.I. Verify follows its launch of the Model AI Governance Framework in 2020 and the National AI Strategy in 2019. A.I. Verify seeks to promote transparency on the use of AI between companies and their stakeholders through self-conducted technical tests and process checks. Developed by the Infocomm Media Development Authority and the Personal Data Protection Commission, A.I. Verify puts Singapore at the forefront of international discourse concerning the ethical use of AI. A.I. Verify has been launched as a Minimum Viable Product (the MVP) which will undergo further product development.

The Alan Turing Institute, supported by the British Standards Institution (BSI) and the National Physical Laboratory (NPL), will pilot a new UK government initiative aiming to shape global technical standards for Artificial Intelligence and expand the country's contribution to the field. The hub is backed by the Department for Digital, Culture, Media and Sport (DCMS) and the Office for AI (OAI). The new AI Standard Hub will create practical tools for businesses, bring the UK's AI community together through a new online platform, and develop educational materials to help organisations contribute, develop and meet global standards. This will help put the UK at the forefront of this rapidly developing area. The Hub is part of the National AI Strategy and will aim to increase UK contribution to development of global AI technical standards.

The first international standards for the design and reporting of clinical trials involving artificial intelligence have been announced in a move experts hope will tackle the issue of overhyped studies and prevent harm to patients. While the possibility that AI could revolutionise healthcare has fuelled excitement, in particular around screening and diagnosis, researchers have previously warned that the field is strewn with poor-quality research. Now an international team of experts has launched a set of guidelines under which clinical trials involving AI will be expected to meet a stringent checklist of criteria before being published in top journals. The new standards are being simultaneously published in the BMJ, Nature Medicine and Lancet Digital Health, expanding on existing standards for clinical trials – put in place more than a decade ago for drugs, diagnostic tests, and other interventions – to make them more suitable for AI-based systems. Prof Alastair Denniston of the University of Birmingham, an expert in the use of AI in healthcare and member of the team, said the guidelines were crucial to making sure AI systems were safe and effective for use in healthcare settings.

The first international standards for the design and reporting of clinical trials involving artificial intelligence have been announced in a move experts hope will tackle the issue of overhyped studies and prevent harm to patients. While the possibility that AI could revolutionise healthcare has fuelled excitement, in particular around screening and diagnosis, researchers have previously warned that the field is strewn with poor-quality research. Now an international team of experts has launched a set of guidelines under which clinical trials involving AI will be expected to meet a stringent checklist of criteria before being published in top journals. The new standards are being simultaneously published in the BMJ, Nature Medicine and Lancet Digital Health, expanding on existing standards for clinical trials – put in place more than a decade ago for drugs, diagnostic tests, and other interventions – to make them more suitable for AI-based systems. Prof Alastair Denniston of the University of Birmingham, an expert in the use of AI in healthcare and member of the team, said the guidelines were crucial to making sure AI systems were safe and effective for use in healthcare settings.

The coding of medical diagnosis and treatment has always been a challenging issue. Translating a patient's complex symptoms, and a clinician's efforts to address them, into a clear and unambiguous classification code was difficult even in simpler times. Now, however, hospitals and health insurance companies want very detailed information on what was wrong with a patient and the steps taken to treat them-- for clinical record-keeping, for hospital operations review and planning, and perhaps most importantly, for financial reimbursement purposes. The current international standard for medical coding is ICD-10 (the tenth version of International Classification of Disease codes), from the World Health Organization (WHO). ICD‑10 has over 14,000 codes for diagnoses.

According to the headlines, the Age of Artificial Intelligence is dark and dystopian -- with self-aware, killer robots coming for us all. Such storylines make for blockbuster movies, but they lack a true understanding of AI, how quickly it's developing and what technological barriers exist. The Age of AI is coming, and fast, and there is plenty to be concerned about. Most of the world, including the United States, is unprepared to reap many of the economic and societal benefits offered by AI or mitigate the inevitable risks. Getting there will take decades.

On 22 May, the Organization for Economic Co-operation and Development (OECD), an international team working on creating stronger policies in order to improve lives, adopted and approved new Artificial Intelligence (AI) principles. RELATED: WHAT IS EXPLAINABLE ARTIFICIAL INTELLIGENCE AND IS IT NEEDED? OECD principles on AI focus on AI that is original and trustworthy. Respect for human rights and democratic values are also strong focal points of these principles. This is a first of such principles to be agreed upon and put forward by governments.

Ahead of the ITU Plenipotentiary Conference 2018 (PP-18) – the top policy-making body of the International Telecommunication Union, taking place from 29 October to 16 November in Dubai – ITU News is highlighting some important and emerging areas of ITU's work. The following is an ITU Plenipotentiary Backgrounder, the original can be found on the PP-18 website here. Software has become significantly smarter in recent years. The current expansion of AI is the result of advances in a field known as machine learning. Machine learning involves using algorithms that allow computers to learn on their own by looking through data and performing tasks based on examples, rather than by relying on explicit programming by a human.[1] A machine-learning technique called deep learning, inspired by biological neural networks, finds and remembers patterns in large volumes of data.

Problems faced by international standardization bodies become more and more crucial as the number and the size of the standards they produce increase. Sometimes, also, the lack of coordination among the committees in charge of the development of standards may lead to overlaps, mistakes or incompatibilities in the documents. The aim of this study is to present a methodology enabling an automatic extraction of the technical concepts (terms) found in normative documents, through the use of semantic tools coming from the field of language processing. The first part of the paper provides a description of the standardization world, its structure, its way of working and the problems faced; we then introduce the concepts of semantic annotation, information extraction and the software tools available in this domain. The next section explains the concept of ontology and its potential use in the field of standardization. We propose here a methodology enabling the extraction of technical information from a given normative corpus, based on a semantic annotation process done according to reference ontologies. The application to the ISO 15531 MANDATE corpus provides a first use case of the methodology described in this paper. The paper ends with the description of the first experimental results produced by this approach, and with some issues and perspectives, notably its application to other standards and, or Technical Committees and the possibility offered to create pre-defined technical dictionaries of terms.