Artificial intelligence

Introduction

Artificial Intelligence (AI) is increasingly present in our everyday lives, whether through smart assistants, crypto currencies, recommendations when we search the web, chatbots, etc. AI’s fields of application are manifold and rapidly continue to expand as the digital transformation accelerates due to the COVID-19 pandemic and technological breakthroughs.

AI technologies have the capability to support sustainable development and positively transform people’s lives, yet they may also have unanticipated effects on societies, economies and the environment.

UNECE is assessing the use of AI in its areas of work to help member States harness AI in a manner that is based on the United Nations Charter and the Universal Declaration of Human Rights.

Large Language Models for Official Statistics

Established in 2010, the UNECE High Level Group on Modernisation of Official Statistics (HLG-MOS) has been at the forefront of modernisation initiatives in the field of official statistics. These initiatives include innovative areas such as big data, synthetic data and machine learning – AI system developed based on data.

In 2023, the international collaboration efforts of HLG-MOS on the topic of AI have concentrated around the Large Language model (LLMs) – advanced AI systems that sparked a substantial public interest with the release of ChatGPT in late 2022. Based on the extensive training on vast data sets, LLMs are capable of understanding and generating texts that cannot always easily be differentiated from texts produced by a human being. This exceptional capability in natural language processing tasks offers an immense potential for statistical organizations to enhance the quality of their services to society – provision of statistics and data services that are foundational for policy-makers, business and citizens alike.

To establish a common understanding of LLM’s potential within the statistical community, a recently published white paper provides examples of implementation from various statistical organizations (e.g., code translation, report generation, natural language interface for database), which showcase the capabilities, challenges, and risks of LLM (e.g., privacy concerns, hallucination, ethical issues, governance, alignment with Fundamental Principles of Official Statistics).

Developing the regulatory framework for autonomous vehicles

The motor industry’s 125-year history is an impressive succession of innovation. Today, the industry faces the challenging task to massively electrify vehicles over the next 5 to 10 years, at least in developed markets.

At the same time, with the rise and promises associated to automated and autonomous driving, we are standing before what will be the biggest quantum leap forward in automotive technology in history.   

Autonomous vehicles have the potential to improve the life of billions of people and fundamentally change how road transportation works.  This will transform the global automotive industry, which employs some 50 million people worldwide and represents a turnover of almost $2 trillion per year. 

Yet, autonomous vehicles raise as many questions as they offer potential benefits. These include liability, insurance regimes, safety standards, software reliability and cybersecurity, to name just a few.

Providing an appropriate and balanced regulatory answer to these questions is a prerequisite to the mass introduction of these vehicles on the road. UNECE, through the World Forum for Harmonization of Vehicle Regulations (WP.29) and the UN Road Safety Instruments, is the platform where countries from all around the world gather to jointly develop the regulatory frameworks governing motor vehicles and road traffic. It has therefore embarked since 2015 in adapting existing legal instruments and developing new ones to facilitate the gradual introduction of automated and autonomous driving functionalities made possible by the use of AI and other technologies.

In an effort to focus its activities on automation, UNECE established the Working Party on Automated/Autonomous and Connected Vehicles (GRVA) in 2019.

Since its establishment, GRVA has created a global scheme to develop requirements and guidelines for automated and connected vehicles, namely the Framework on Automated/Autonomous and Connected Vehicles (FDAV), which largely guides GRVA’s work. This Framework was drafted by the European Union, China, Japan, and the United States of America and endorsed by the World Forum for Harmonization of Vehicle Regulations and the UNECE Inland Transport Committee. The document defines a safety vision, key safety elements, guidance to the Working Parties of WP.29 as well as a programme of activities. These activities, at the intergovernmental level, form a novel initiative aimed at globally harmonizing automated vehicles regulations and creating a more productive environment for innovation.

Recent developments under the 1958 Agreement include the adoption of amendments to UN Regulation No. 79 (Steering equipment) as well as new UN Regulations Nos.: 157 (Automated Lane Keeping System), 155 (Cyber Security and Cyber Security Management System) and 156 (Software Update and Software Update Management System).

As Advanced Driver Assistance Systems and Automated Driving Systems rely on combinations of technologies including technologies that the general public call Artificial Intelligence, GRVA address the safety of such systems in a technology neutral manner. Given the nature of machine learning and deep learning, for example, GRVA reviewed how industry employ such AI technologies and for which use cases. It drafted relevant definitions for the purpose of its work addressing the automotive sector and it is considering the need to develop AI specific provisions in the form of recommendation or guidelines that would address the specific risks posed by the technology.

AI for Smart Cities

The UN global initiative United for Smart Sustainable Cities (U4SSC) established by UNECE and the International Telecommunication Union (ITU) in 2016 is a global platform for smart cities stakeholders, which advocates for public policies to encourage the use of ICT for sustainable urban development. The initiative is coordinated by ITU, UNECE and UN-Habitat, and supported by 14 other UN agencies and programmes.

The initiative aims to: Generate guidelines, policies and frameworks for the integration of ICTs into urban operations, based on the SDGs, international standards and urban key performance indicators (KPIs); and help streamline smart sustainable cities action plans and establish best practices with feasible targets that urban development stakeholders are encouraged to meet. The topics of the current phase of

U4SSC looks in particular at how AI applications can make cities smarter and more sustainable.

In 2017, the U4SSC stakeholders elaborated a set of KPIs for smart sustainable cities (KPI4SSC) which includes 92 indicators (core and advanced) divided in the 3 dimensions of sustainable development: economy, environment, and society and culture. The indicators are fully aligned with the Sustainable Development Goals (SDGs) and serve as a tool for evidence-based decision making, progress monitoring and achieving the SDGs at the local level. They are being implemented by 50 cities of different sizes and development worldwide.

Cities taking part in UNECE‘s KPI evaluation include: San Marino, Almaty (Kazakhstan), Bishkek (Kyrgyzstan), Nur Sultan (Montenegro), Tblisi (Georgia), Voznesensk (Ukraine), Goris (Armenia), 17 cities in Norway and many other countries

UNECE Housing and Land Management Unit implements a UNDA 12th tranche project on innovative financing for sustainable smart cities and as part of the project, develops smart sustainable cities profiles using the KPIs for SSC for the cities of Grodno (Belarus), Nur-Sultan (Kazakhstan), Bishkek (Kyrgyzstan), Tblisi (Georgia) and Podgorica (Montenegro). Based on the cities profiles recommendations, including for innovative urban infrastructure using AI, UNECE supports with developing bankable projects and connections to the IFIs. More information is at https://unece.org/housing/innovativefinancing-sustainablesmartcities. Also see the publication on “people-smart” cities at https://unece.org/media/Housing-and-Land-Management/press/355189

AI for energy

Artificial Intelligence and other technologies are inspiring energy suppliers, transmission and distribution companies, and demand sectors (buildings, industry, transport, and other) to establish new business models to generate, deliver and consume energy in a more sustainable way.

UNECE established a Task Force on Digitalization in Energy to offer a platform for cross-industry experts from the energy sector and digital innovation to develop a unified voice on digitalization in energy. Serving as an umbrella for the subsidiary bodies of the Committee on Sustainable Energy to conduct relevant research and assess sectoral opportunities and challenges, the Task Force on Digitalization in Energy:

Monitors new and emerging trends in the digitalization in energy domain, that enable advances in connectivity, data, analytics, optimization of the overall energy infrastructure, and can greatly increase overall efficiency of the energy system;
Conducts in-depth research into the potential of integrating digital solutions throughout the entire energy system, based on thorough evaluations of challenges and policy obstacles, including notably the socio-economic context, to provide a clear, concise and balanced view to policymakers and other stakeholders;

The aim of the Task Force on Digitalization in Energy is to bridge the gap between academic research, industrial innovations, and policy needs, and to develop a consensus about the approach that should be considered for shaping the future of energy systems. 

The Task Force on Digitalization in Energy found that AI and digitalization have the potential to reduce residential and commercial buildings’ energy use by as much as 10% globally by 2040 if applied throughout buildings value chain and life cycle. In particular, applications of AI may help optimize buildings’ orientation for solar heat gains and predict individual power and heat needs, thus increasing overall energy security and maximizing the integration of renewable energy sources. 

The Digitalization in Energy also found that AI and digitalization could help achieve energy savings of at least 10-20% in the industrial sector (which consumes around 38% of global final energy and produces 24% of GHG).

Other findings of the Task Force on Digitalization in Energy on Big Data, advanced analytics, machine learning, AI, and related matters, also hold considerable significance for energy sector industries and end uses, and inform the subject-matter discussions at UNECE level and beyond.

Energy systems transformation involves finding a balance between energy security, affordability, and environmental sustainability, which will define their resilience (as defined in UNECE’s publication “Building Resilient Energy Systems”): energy security, affordability and environmental sustainability.

To facilitate decision-making in this complex endeavour, UNECE partners with University of Zürich to develop an AI-powered tool that will use selected and vetted information sources. European Investment Bank, International Atomic Energy Agency, International Energy Agency, International Telecommunication Union, Organization for Security and Co-operation in Europe, World Meteorological Organization, World Bank, and other organizations contribute their knowledge base to support and shape this tool.

AI in trade facilitation

Artificial intelligence (AI) is an enabling technology impacting the global economy and international trade. Combined with business-process-oriented automation and more efficient data flow exchanges, AI further promises to lift barriers to international trade, stimulate growth in global electronic commerce and allow for better predictions and associations to inform policy decisions. AI is expected to underpin productivity growth, overall economic growth and create new opportunities in facilitating trade.

In order to harness this potential, UN/CEFACT is reviewing its mandates and developing whitepapers analyzing how AI can be used to facilitate trade processes in collecting, processing and analyzing data (https://unece.org/trade/documents/2023/09/reports/report-edata-domain-artificial-intelligence-trade-facilitation).

This includes examining: 

How AI technology could be used to facilitate trade and related processes in international supply chain including study of areas such as data privacy, AI based trade policies, use of AI in e-Commerce and payments.
How existing UN/CEFACT deliverables could be used in AI applications.
Possible changes to existing UN/CEFACT deliverables, or new deliverables, that could be considered in order to support AI trade facilitation applications.

Initial contributions include existing descriptions and technical specifications for UN/CEFACT:

Advisory Group on Advanced Technologies document “Artificial Intelligence Demystified"
Core Components Library (CCL);
Business Requirement Specifications (BRSs)
Requirement Specification Mappings (RSMs)
Reference Data Models (RDMs)

lready published material on AI technology and implementations (e-Negotiation project)
Blockchain work undertaken by UN/CEFACT under the traceability project to harness the potential of blockchain technology for due diligence and sustainability in cotton value chains.

One other tool complimenting AI for Trade Facilitation is the newly released UN/CEFACT JSON-LD Web Vocabulary.

A recent completed project, the UN/CEFACT JSON-LD Web Vocabulary (https://vocabulary.uncefact.org) complements and enhances the capabilities of AI systems for trade related exchanges. The UN/CEFACT JSON-LD Web Vocabulary is an initiative to support the interoperability of trade by allowing supply chain actors to more easily integrate a common vocabulary in their business tools (e.g. software applications, AI algorithms) to ensure that data shared between different entities (e.g., suppliers, manufacturers, distributors, transporters, financiers, and regulators) is consistent and easily interpretable, reducing errors and misunderstandings. The vocabulary is based on the UN/CEFACT Buy-Ship-Pay model, which is a globally recognized schematic for defining terms in international trade.

AI systems leverage JSON-LD to structure and represent information in a standardized way. UN/CEFACT JSON-LD web vocabulary will allow AI systems to read and process data encoded in JSON-LD format. This capability, in turn, facilitates the application of machine learning and deep learning algorithms to graphically represent, cross-reference, and comprehend crucial trade contexts.