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Artificial Intelligence for good

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AI promises

​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. Deep-learning systems perform tasks by considering examples, generally without being programmed, and out-perform traditional machine-learning algorithms.[2]

Big Data, referring to extremely large data sets that can be analysed computationally to reveal patterns, trends and associations,[3] together with the power of AI and high-performance computing, are generating new forms of information and insight with tremendous value for tackling humanity's greatest challenges.

Below are just a few examples showing how AI can be applied for good:


While the opportunities of AI are great, there are risks involved.

Datasets and algorithms can reflect or reinforce gender, racial or ideological biases[4] . When the datasets (fed by humans) that AI rely on are incomplete or biased, they may lead to biased AI conclusions.

Humans are increasingly using deep-learning technologies to decide who gets a loan or a job. But the workings of deep-learning algorithms are opaque, and do not provide humans with insight as to why AI is arriving at certain associations or conclusions, when failures may occur, and when and how AI may be reproducing bias.[5]

AI can deepen inequalities by automating routine tasks and displacing jobs.

Software, including the software that runs cell phones, security cameras, and electrical grids, can have security flaws.[6] These can lead to thefts of money and identity, or internet and electricity failures.

New threats to international peace and security can also emerge from advances in AI technologies. For example, machine learning can be used to generate fake video and audio to influence votes, policy-making and governance.[7]

Solutions: ensuring AI is used for good

The development and adoption of relevant international standards, and the availability of open-source software, will provide a common language and tool for coordination that will facilitate the participation of many independent parties in the development of AI applications. This can help to bring the benefits of AI advances to the entire world, while mitigating its negative effects.

Indeed, it is vital that a diverse range of stakeholders guide the design, development and application of AI systems. Accurate and representative AI conclusions require datasets that are accurate and representative of all. Furthermore, safeguards need to be put in place to promote the legal, ethical, private and secure use of AI and Big Data.

Increased transparency in AI, with the aim to inform legal or medical decision-making, will allow humans to understand why AI is arriving at certain associations or conclusions. This, in turn, will encourage people to use their expertise, experience and intuition to validate conclusions or make a different decision than the one proposed by the machine. While the machine analyses and arrives at conclusions at much greater speed and accuracy than before, it is still humans who have the power to question the machine´s conclusions and make final decisions.

To balance the consequences of AI on employment and benefit from the new job opportunities that AI offers, it is essential to create environments that are conducive to acquiring digital skills, be it through formal education or training at the workplace. In particular, AI will bring employment opportunities to people who have the advanced digital skills needed to create, manage, test and analyse ICTs.

Efforts that protect the safety, privacy, identity, money, and possessions of the end-user need to be deployed to address AI-related security challenges in areas as diverse as e–Finance, e-governance, smart sustainable cities, and connected cars.​

ITU's contribution to AI for good

Facilitating conducive policy and regulation

As the United Nations´ specialized agency for information and communication technologies, ITU brings together stakeholders representing governments, industries, academic institutions and civil society groups from all over the world to gain a better understanding of the emerging field of AI for good.

Building on the success of ITU´s first AI for Good Global Summit, the 2019 Summit collaborated with over 30 UN family agencies and other global stakeholders to identify strategies to ensure that AI technologies are developed in a trusted, safe and inclusive manner, with equitable access to their benefits. The Summit spawned many pioneering 'AI for Good' project proposals on expanded and improved health care, enhanced monitoring of agriculture and biodiversity using satellite imagery, smart urban development and trust in AI.

ITU maintains an AI Repository where anyone working in the field of artificial intelligence can contribute key information about how to leverage AI for good. This is the only global repository that identifies AI-related projects, research initiatives, think tanks and organizations that aim to accelerate progress on the 17 United Nations Sustainable Development Goals (SDGs).

ITU regularly brings together heads of ICT regulatory authorities from around the world to share views and developments on AI and other pressing regulatory issues, address questions of governance and strengthen collaboration to use AI for good.

Setting standards

Moving forward, international standards—the technical specifications and requirements that AI and other technologies will need to fulfil to perform well—can help address the risks of AI by allowing machine learning to be predictable, reliable and efficient.

AI and Machine Learning are gaining a larger share of the ITU standardization work programme in fields such as network orchestration and management, multimedia coding, service quality assessment, operational aspects of service provision and telecom management, cable networks, digital health, environmental efficiency, and autonomous driving. 

Open platforms advancing various aspects of AI and Machine Learning 

The ITU Focus Group on 'Machine Learning for Future Networks including 5G'  is defining the requirements of machine learning as they relate to interfaces, protocols, algorithms, data formats and network architectures. 

The ITU Focus Group on 'Environmental Efficiency for AI and other Emerging Technologies' will benchmark best practices and describe pathways towards a standardized framework to assess environmental aspects of the adoption of emerging technologies. 

The ITU Focus Group on 'AI for Health', driven in close collaboration by ITU and WHO, is working towards the establishment of a framework and associated processes for the performance benchmarking of AI for Health solutions. 

The ITU Focus Group on 'AI for Autonomous and Assisted Driving' is working towards the establishment of international standards to monitor and assess the behavioural performance of the AI 'drivers' in control of automated vehicles. 

The Global Initiative on 'AI and Data Commons', established in January 2020, aims to support AI for Good projects in achieving global scale. The Initiative will offer assemblies of resources to launch new AI projects aligned with the SDGs, and scale them up fast. 

How are ITU standards addressing AI and Machine Learning?  

ICT companies in the networking business are introducing AI and Machine Learning as part of their innovations to optimize network operations and increase energy and cost efficiency. New ITU standards provide an architectural framework for the integration of machine learning into 5G and future networks (ITU Y.3172), a framework to evaluate intelligence levels across different parts of the network (ITU Y.3173), and a framework for data handling in support of machine learning (ITU Y.3174). The basis for these ITU standards was provided by the ITU Focus Group on 'Machine Learning for Future Networks including 5G'*.

These 'Machine Learning for 5G' standards are also guiding contributions to the ITU Global Challenge on AI and Machine Learning in 5G. Competitors are meeting new partners – and gaining access to new tools and data resources – to achieve goals set out by problem statements contributed by industry and academia around the world. The Grand Challenge Finale, online from 15 to 17 December 2020, will demo outstanding solutions and decide the overall winners of the Challenge. 

AI and Machine Learning play an important part in multimedia coding, an area of ITU standards work known for the Primetime Emmy winning video-compression standards, ITU H.264 'Advanced Video Coding', ITU H.265 'High Efficiency Video Coding', and ITU H.266 'Versatile Video Coding'. ITU has also established a new working group ('Question') on “Artificial intelligence-enabled multimedia applications" (Q5/16). 

AI and Machine Learning are widely used in developing models to assess the quality of speech, audio and video, for example in ITU standards for the quality assessment of audiovisual streaming, in particular ITU P.1203 (progressive-download and adaptive-bitrate AV) and ITU P.1204 (video streaming services up to 4K).

New ITU quality-assessment standards address intelligent network analytics and diagnostics (ITU E.475) and the creation and performance testing of Machine Learning-based models to assess the impact of the transmission network on speech quality for 4G voice services (ITU P.565).

Other notable new ITU standards relevant to AI and Machine Learning address environmental sustainability, cable networks, and operational aspects of service provision and telecom management. 

A new ITU standard specifies a datacentre infrastructure management (DCIM) system based on Big Data and AI technology (ITU L.1305), supporting DCIM systems in reducing the energy required to control datacentre temperature. 

A new ITU standard provides the framework for a premium cable network platform to support industry in offering advanced multimedia services (ITU J.1600). It is the first of a new series of ITU standards on AI-assisted cable networks.   

AI is one of the five characteristics of a new ITU framework to support smart service operation, network management and infrastructure maintenance (ITU M.3041). New ITU standards under development in this domain will address AI-enhanced telecom operation and management, energy saving for 5G Radio Access Networks with AI, and robot-based smart patrols of telecoms networks. ​


Last update: October 2020