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Artificial Intelligence (AI) is opening up more opportunities for governments and citizens by combining human creativity with technology to drive progress in our modern society.
The use of technology will change every sector:
Artificial intelligence can reduce bureaucracy, deliver better public services for the benefit of citizens and enhance efficiency through automating routine government processes, coordination in the public administration.
Smart governments are applying intelligent digital technologies, such as AI and ML algorithms, and smart and sustainable solutions to improve bureaucratic efficiency and government’s decision making, foster positive relationships with citizens and business, or solve specific problems in critical fields such as health care, police, military, national defense, security services, taxation, waste management, water management, electrical grids, telecommunication networks and environmental protection.
Image Source: Deloitte
Artificial intelligence can provide better automated services and deliver more security to citizens. Other technologies such as the Internet of Things (IoT), cloud computing, analytics, and blockchain, have triggered a shift towards a more intelligent society.
While AI creates new possibilities, it also invokes ‘angst’ and mistrust: the fear of job losses, or a world dominated by machine-powered intelligence instead of humans. The introduction of AI needs to go hand-in-hand with good governance models and ethics.
Together with businesses, governments are forming AI strategies that steer the use of intelligent technologies related to fairness, safety, data privacy, transparency, citizen engagement, and the future of work.
Image Source: Chris Noessel
Narrow AI is a term used to describe artificial intelligence systems that are specified to handle a singular or limited task. The antithesis to Narrow AI, sometimes referred to as weak AI, is called strong AI. Strong AI, unlike Narrow AI, is capable of handling a wide range of tasks rather than one particular task or problem. This variation of artificial intelligence can be roughly conceptualized as a foundation for neural networks emulating sentience or consciousness.
The OECD defines an Artificial Intelligence (AI) System as a machine-based system that can, for a given set of human defined objectives, make predictions, recommendations, or decisions influencing real or virtual environments.
‒ AI system: An AI system is a machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations, or decisions influencing real or virtual environments. AI systems are designed to operate with varying levels of autonomy.
‒ AI system lifecycle: AI system lifecycle phases involve: i) ‘design, data and models’; which is a context-dependent sequence encompassing planning and design, data collection and processing, as well as model building; ii) ‘verification and validation’; iii) ‘deployment’; and iv) ‘operation and monitoring’. These phases often take place in an iterative manner and are not necessarily sequential. The decision to retire an AI system from operation may occur at any point during the operation and monitoring phase.
‒ AI knowledge: AI knowledge refers to the skills and resources, such as data, code, algorithms, models, research, know-how, training programmes, governance, processes and best practices, required to understand and participate in the AI system lifecycle.
‒ AI actors: AI actors are those who play an active role in the AI system lifecycle, including organisations and individuals that deploy or operate AI.
‒ Stakeholders: Stakeholders encompass all organisations and individuals involved in, or affected by, AI systems, directly or indirectly. AI actors are a subset of stakeholders.
The European Commission followed with a modified definition:
“[A]rtificial intelligence system” (AI system) means software that is developed with one or more of the techniques and approaches listed bellow and can, for a given set of human-defined objectives, generate outputs such as content, predictions, recommendations, or decisions influencing the environments they interact with.
Natural Language Processing (NLP) – is also called computational linguistics and presents solutions in understanding human languages through computational models and processes
Speech Recognition – enables a computer to identify the words that a person speaks into a microphone or telephone and convert them into written text
Computer Vision – AI applications from this category use some form of image, video or facial recognition to gain information on the external environment and/or the identity of specific persons or objects
Machine Translation – is a sub-field of computational linguistics that focuses on the use of software to translate text or speech from one language to another
Robotics – is an interdisciplinary field integrating mechanical engineering, electrical engineering, information engineering, mechatronics, electronics, bioengineering, computer engineering, control engineering, software engineering, and that includes the designing, construction, operation, and use of robots
Rules-based systems – (also known as production systems or expert systems) are the simplest form of artificial intelligence. A rule-based system is a way of encoding a human expert's knowledge in a fairly narrow area into an automated system
Machine Learning – It is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the systems which can learn from data, identify patterns and make decisions with minimal human intervention9 .
Spam-filters in email programs: – detect and block unwanted emails.
AI in cybersecurity solutions – protect networks, programs, and data from attack, damage, or unauthorized access.
Chatbots – converse with people via voice interfaces or text messages.
Fraud detection – detects, prevents and manages fraudulent patterns in the data.
AI in policing or social services – support and/or drive decisions in fields such as law enforcement, crime prevention, public safety, children welfare, social programs.
AI in HR – takes on key HR tasks including hiring, retaining talent, training, benefits and employee satisfaction.
Some big concerns brought by NAI to the public sectors are:
Image Source: National Journal
More and more people are asking today: Should the government be run by artificial intelligence?
The question is motivated with an increasing understanding: most problems we have on our Mother-Earth, from local poverty to global risks, are created by …non-intelligent governments, all failing to follow “the good governance principle”, at local, national, transnational or global levels.
Artificial intelligence can help governments in the following aspects:
Governments should be run by artificial intelligence in the near future for more efficiency.
Image Source: Accenture
Here are some examples of how AI/ML/D/ contributes to public policy objectives:
Artificial intelligence helps governments to provide a new level of convenience. Applications such as smart waste management systems have significantly reduced noise level, street congestion, and air quality for residents. Artificial intelligence can also improve mobility, advance education, protect food and water safety, decrease emissions, prevent crime & fraud detection, increase cross-border security and even save lives. Our world has the possibility to grow into a more intelligent and participatory society in the future with artificial intelligence, if paired with good ethics and governance models. The key is to achieve a symbiosis between human creativity and technology that creates the best experience for all.
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