Europe adopted its Green Deal, striving to become the first climate-neutral continent with the following rationales. "Climate change and environmental degradation are an existential threat to Europe and the world. To overcome these challenges, the European Green Deal will transform the EU into a modern, resource-efficient and competitive economy, ensuring:
- no net emissions of greenhouse gases by 2050
- economic growth decoupled from resource use
- no person and no place left behind
The European Green Deal is also our lifeline out of the COVID-19 pandemic. One third of the 1.8 trillion euro investments from the Next Generation EU Recovery Plan, and the EU’s seven-year budget will finance the European Green Deal". https://ec.europa.eu/info/strategy/priorities-2019-2024/european-green-…
It is plain and clear without effective and efficient or smart governments, the Deal has small chances for success.
The big question is How to Create Intelligent Governments, which I intend to answer in the article.
How to Create Intelligent Governments
All should start from National AI and Data Governance Strategies, focusing on the Public/Government sectors together with Industry sectors,
- Primary sector of the economy (the raw materials industry, from energy to agriculture)
- Secondary sector of the economy (manufacturing and construction)
- Tertiary sector of the economy (the service industry)
- Quaternary sector of the economy (information services)
- Quinary sector of the economy (human services)
The idea is to automate and optimize key services of bureaucracy, deliver better public services for the benefit of citizens and enhance efficiency through automating routine government processes, coordination in the public administration.
The goals are to apply intelligent digital technologies, 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:
police, police protection,
military, national defense,
public roads, public transit,
state enterprise management,
state’s government, legislature, executive, judiciary, smart government,
public administration, government policies and programs as well as the behavior of officials, public departments and agencies, at all levels of government,
rural and urban planning, green communities and smart sustainable cities,
nation, country, intelligent nation.
What is Narrow AI?
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.
- Machine learning approaches, including supervised, unsupervised and reinforcement learning, using a wide variety of methods, including deep learning;
- Logic- and knowledge-based approaches, including knowledge representation, inductive (logic) programming, knowledge bases, inference and deductive engines, (symbolic) reasoning and expert systems;
- Statistical approaches, Bayesian estimation, search and optimization methods. [the EC Artificial Intelligence Act]
Types of Narrow AI Applications could be used in government
Natural Language Processing (NLP) The field of NLP is also called computational linguistics and presents solutions in understanding human languages through computational models and processes
Speech Recognition. 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. 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 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. Rule-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 Machine learning 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 .
Types of NAI Applications
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.
- An international, commonly agreed definition of AI does not exist, only as a machine reproducing the cognitive capacities of a human being, with different types of automated learning.
- Maturity of AI implementation in public structures, organizations and programs, AI pilots/PoCs, a few use case in production on a limited scale, scaling-up use cases in production.
- Algorithmic biases in relation to gender equality, discrimination and racism and unethical public and personal data governance.
- Explainability bringing two notions with itself: interpretability and transparency.
- Losing jobs for people. According to a Eurobarometer survey published by the European Commission, 72% of respondents believe robots steal the jobs of people.
Why are AI Governments?
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, created by …non-intelligent governments, all failing to follow “the good governance principle”, at local, national, transnational or global levels.
Participation, Representation, Fair Conduct of Elections
Efficiency and Effectiveness
Openness and Transparency
Rule of Law
Competence and Capacity
Innovation and Openness to Change
Sustainability and Long-term Orientation
Sound Financial Management
Human rights, Cultural Diversity and Social Cohesion
Most of the GGPs are just declared, like Responsiveness: “Objectives, rules, structures, and procedures are adapted to the legitimate expectations and needs of citizens. Public services are delivered, and requests and complaints are responded to within a reasonable timeframe”.
Or, Competence and Capacity: “The professional skills of those who deliver governance are continuously maintained and strengthened in order to improve their output and impact. Public officials are motivated to continuously improve their performance. Practical methods and procedures are created and used in order to transform skills into capacity and to produce better results”.
So, the government SHOULD and MUST be run by AI…for its best performance and highest efficiency.
How AI Governments' Intelligent Systems Reflect The Needs Of Citizens
Here are some examples of how AI/ML/D/ contributes to public policy objectives.
- filtering state officials and government workers as to expertise, skills, competency,
- knowledge and integrity, or meritocratic principles;
- receiving employment benefits at job loss, retirement, bereavement, child birth, immediately, in an automated way;
- classifying emergency calls based on their urgency;
- detecting and preventing the spread of diseases;
- assisting public servants in making welfare payments and immigration decisions;
- adjudicating bail hearings;
- triaging health care cases;
- monitoring social media for public feedback on policies;
- monitoring social media to identify emergency situations;
- identifying fraudulent benefits claims;
- predicting a crime and recommending optimal police presence;
- predicting traffic congestion and car accidents;
- anticipating road maintenance requirements;
- identifying breaches of health regulations;
- providing personalised education to students; marking exam papers;
- monitoring/counting utility (electricity/water/waste/communications) bills;
- plan new infrastructure projects;
- assisting with defence and national security.
The Paradigmatic Case of the EU, or How we fail to implement the smart intelligent and green EU project for 10 years
In 2009, the consortium I-Europe proposed to the EU presidents the I-Europe concept as a transnational development strategy.
A couple of EU presidents took our key points while following some influential German political industrial groups. So I-Europe re-emerged as:
E U R O P E 2 0 2 0; A European strategy for smart, sustainable and inclusive growth
Our following proposals to the EC presidents to test replacing its key staff by AI algorithms, optimizing its governance structure, Smart Europe, had been suspended for 5 years now. i-Europe; Project EU XXI: Future Europe: Social Europe™, Digital Europe™, Green…
Instead, they are plagiarizing your concept, deforming it to the worst, monetizing to their own benefits: Banks give €1 billion to build “Smart Europe”/Investors and the banking community have agreed to pour €1 billion into the modernisation and digitalisation of Europe, Committee of the Regions President Markku Markkula said today. Banks give €1 billion to build "Smart Europe"
We all might be very glad that the EIB is investing in the best ideas. But it is wiser to trust the public finances to the originators of the best ‘Big Ideas’, instead of the Jeremy Rifkin-like people. It is a sort of mental crime to downgrade a smart/intelligent, green/ecological, social/inclusive, or all-sustainable Europe, just as focusing on “new communication technologies, new sources of energy and new modes of mobility to move economic activity”.
The role of Artificial Intelligence in the European Green Deal
Proposal for a REGULATION OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL LAYING DOWN HARMONISED RULES ON ARTIFICIAL INTELLIGENCE (ARTIFICIAL INTELLIGENCE ACT) AND AMENDING CERTAIN UNION LEGISLATIVE ACTS COM/2021/206 final