ITU is engaged in a wide range of work relating to new and emerging trends in AI, as well as helping ITU Members, Member States and stakeholders prepare for its wide-ranging policy and regulatory consequences.
AI for Good as a platform
The AI for Good platform [https://aiforgood.itu.int/programme/] focuses on uses of AI to help fulfil the essential needs of humanity, including achieving the 17 SDGs set out by the UN to be achieved by 2030, as an
all-year, always online programme. The goal of the Summit is to identify practical applications of AI to advance the sustainable development goals and scale solutions for global impact. The Summit is organized by ITU in partnership with 38 UN sister agencies and co-convened with Switzerland.
AI for Good YouTube channel hosts hundreds of videos highlighting interviews with AI leaders and innovators, innovations and demos showcasing AI solutions to accelerate the SDGs as a one-stop shop to catch up on emerging trends in AI for Good.
Subscribe to the channel and join online for new updates and exclusive content as they go live on to explore ideas, insights and active discussions around AI to achieve the SDGs. The channel features keynotes, webinars, perspectives, an Innovation Factory and social media.
Policy and Regulation
Through annual regulatory surveys and monitoring (https://www.itu.int/itu-d/sites/regulatory-market/), ITU tracks the growth of national AI strategies and policies. Machine Learning models are trained and fed by vast quantities of data, so it is vital to consider national policies in data privacy, regulation and data protection as well as approaches to the Internet of Things (IoT), sensor networks and the 5G networks making data transmission possible, when considering national approaches to AI.
According to ITU's latest Telecommunication/ICT Regulatory survey, some 18 countries had prepared specific strategies on AI by 2019, although more countries have AI sector-specific strategies, which has risen to 49 countries in 2021. However, AI encompasses diverse set of technologies, and few national strategies consider the field in total. Countries also have to consider the treatment of data flows now generated by IoT and sensor networks which feed ML models and AI technologies. Several countries, including the United States and Saudi Arabia, have prepared strategies on all three topics (5G, IoT and AI). According to the United Nations Conference on Trade and Development (UNCTAD; 2020), some two-thirds of all countries have developed policies for data protection, including AI for development.
Figure: Numbers of countries with strategies for emerging technologies, 2020
Source: ITU annual regulatory survey.
Moving forward, international standards—the technical specifications and requirements needed for AI and other technologies to perform well—can help address real and perceived risks by setting clear boundaries and making machine learning (ML) predictable, reliable and efficient.
AI and ML are gaining ground in ITU's standardization work, with research, analysis and stakeholder discussions focusing on network orchestration and management, multimedia coding, service quality assessment, and various aspects of telecom management, operation and services, as well as cable networks, digital health, environmental efficiency, and autonomous driving.
AI in Radiocommunication Standards
ITU Radiocommunication (ITU-R) study groups and forthcoming reports examine the use of AI in radiocommunications:
- ITU-R Study Group 1 covers all aspects of spectrum management, including spectrum monitoring.
Question 241/1 looks at “Methodologies for assessing or predicting spectrum availability".
- ITU-R Study Group 6, dedicated to broadcasting services, is studying AI and ML applications:
Question ITU-R 144/6, “Use of AI for broadcasting", considers the impact of AI technologies and how can they be deployed to increase efficiency in programme production, quality evaluation, programme assembly and broadcast emission.
Recommendation ITU-R BS.1387: “Method for objective measurements of perceived audio quality" about AI in the field of broadcasting.
Report ITU-R BT.2447, “AI systems for programme production and exchange", discusses current applications and near-term initiatives, revised regularly to reflect the latest progress on AI for the applications in broadcasting.
ITU-T standards addressing AI and Machine Learning
ICT companies in the networking business are introducing AI and ML to optimize network operations and increase energy and cost efficiency. New ITU standards provide: an architectural framework to integrate ML into 5G and future networks (ITU-T Y.3172); an evaluation framework for intelligence levels across different parts of the network (ITU-T Y.3173); and a framework for data handling in support of ML (ITU-T Y.3174. These standards originated in discussions by the ITU-T Focus Group on 'Machine Learning for Future Networks including 5G'.
ITU-T AI/ML in 5G Challenge, introduced in 2020, rallied like-minded students and professionals from around the globe to study the practical application of AI and ML in emerging and future digital communication networks. The first edition attracted over 1,300 students and professionals from 62 countries, competing for global recognition and a prize fund of USD 36,000. By mapping emerging AI and ML solutions, the Challenge fosters a community to support the evolution of ITU standards. See the
Challenge GitHub (Clickable link:
The ITU-T Focus Group on 'Environmental Efficiency for AI and other Emerging Technologies' aims to benchmark best practices and describe pathways towards a standardized environmental framework.
The ITU-T Focus Group on 'AI for Health', convened jointly with the World Health Organization (WHO), is working towards a framework and processes for performance benchmarking of AI for health solutions, including in response to COVID-19. It represents an open platform open to all stakeholders from different fields. The Focus Group works at the interface of multiple fields (e.g., ML/AI, medicine, regulation, public health, statistics) and includes decision-makers who value a standardized benchmarking framework.
The ITU-T Focus Group on 'AI for Autonomous and Assisted Driving' is working to establish international standards to monitor and assess the behaviour of the AI 'drivers' in control of automated vehicles.
The Global Initiative on 'AI and Data Commons', established in January 2020, assembles key resources for AI projects aligned with SDGs, supports rapid implementation and aims to help bring AI for Good projects to global scale.
AI and ML are widely used to construct models for the qualities of speech and other audio-visual (AV) data. An ITU-T working group on 'AI-enabled multimedia applications' (ITU-T
Q5/16) is discussing standard requires for the quality assessments in AV streaming, in progressive-download and adaptive-bitrate AV (ITU-T P.1203) and video streaming (ITU-T P.1204).
New ITU-T standards address intelligent network analytics and diagnostics (ITU-T E.475) and the creation and performance testing for ML models to assess the impact of the transmission network on speech quality for 4G voice services (ITU-T P.565). Others address environmental sustainability, cable networks, and operational aspects of service provision and telecom management.
Other new ITU standards describe a datacentre infrastructure management (DCIM) system based on Big Data and AI technology (ITU-T L.1305), aiming to reduce the energy needs of datacentres, and provide the framework for a premium cable network platform to support industry in offering advanced multimedia services (ITU-T J.1600) for AI-assisted cable networks.
AI and ICT for development issues
ITU is gathering and disseminating information about effective and sustainable AI solutions to equip relevant stakeholders with evidence and knowledge to adopt and leverage relevant AI applications. FAO and ITU publish the e-Agriculture in Action Report: AI for Agriculture, which identifies informative case studies of AI uses in agriculture, with valuable insights about implementation, success factors, and lessons learned.
In addition, ITU engages directly in deployment and testing of some promising AI applications to support the provision of SDG-related services and tools. In Senegal, ITU, in close collaboration with WHO and the Ministry of Health and Social Action, is leading the work on pilot testing an AI application for automatic detection of diabetic retinopathy to improve the coverage and accessibility of screening. This solution could support ophthalmologists in analyzing digital images of retinas.
ITU has released the
AI and big data for development 4.0 report which highlights opportunities and outlines good policy and regulatory practices for implementation, with suggestions in managing and overcoming barriers. The report describes the building-blocks of a national AI and data system for development, including governance, regulation, ethical considerations, digital and data skills, the technological innovation landscape and opportunities for international collaboration.
UN System activities
In 2019 the UN Chief Executive Board endorsed the ITU-coordinated UN system-wide strategic approach and road map for supporting capacity development on AI, under the aegis of the High-Level Committee on Programmes (HLCP). HLCP has also worked on the ethics of AI, and taking into consideration the Secretary-General's Roadmap for Digital Cooperation, the 40th HLCP session decided in October 2020 to establish an HLCP interagency working group on AI (IAWG-AI), co-led by UNESCO and ITU to focus on policy and programmatic coherence of AI activities within the UN. The group leverages the stocktaking and gap analysis exercise by ITU regarding internal capacities within the UN and other stakeholders in relation to the UN system-wide strategy.
ITU has also issued the “Compendium of UN Activities on AI" as an overview of activities being carried out by the UN system. A joint effort between ITU and 37 UN agencies and bodies, all partners of the 2020 AI for Good Global Summit, resulted in an updated version of the compendium at the sixth AI for Good UN Partners Meeting, held virtually on 21 September. The 2020 Compendium covers around 260 cases and projects run by 36 UN agencies and bodies, in areas ranging from smart agriculture and food systems to transportation, financial services, healthcare and AI solutions to combat COVID-19.
Last update: June 2021