Jean-Jacques Pluchart
The encyclical Magnifique Humanité published in May 2026 (see clubturgot.com 114) has rekindled the debate on the ethical principles of AI. Various movements, made up of institutions, companies, scientific laboratories and think tanks, are striving to define ethical frameworks – in the form of codes and charters – based on philosophical, sociological and psychological considerations, but this framework is sparking debates between regulationists and libertarians.
The foundations of AI ethics
Ethical deliberations relating to AI practices follow three main approaches: universal, normative and applied. The first – of an axiological nature and inspired by Kant and Rousseau – is based on the principles and values that underpin life in society: respect for people, truth, justice, nature, etc. The second – known as legalistic or prescriptive – covers moral judgements and social values, such as true or false, good or bad, fair or unfair, etc. The third – of a praxeological nature – measures the consequences, externalities or impacts of a system, behaviour or object on the economy, nature, society or the individual. It is most often applied to new technologies, in particular AI, and to management, in particular sustainable management (Pistilli, 2024).
Ethical frameworks for AI
In order to limit their negative impacts on collective and individual thoughts, decisions or behaviours, guides or ethical charters published by companies and normative codes, frameworks and/or regulations issued by regulators (international organisations, nation-states, associations), aim to provide a framework for the design of software and the use of its data and results by companies (Constantinides & al, 2024). The codes generally do not have a deontological or moral dimension, unlike charters, guides or, in France, the “raisons d’être” of companies.
International institutions such as the OECD, the UN, UNESCO and the G7 have been working since 2019 to establish a “normative ethics” and “global governance of AI”. The Vatican, notably inspired by the work of Bonanti (2018), who inspired several codes, advocates the advent of an “algo-ethics” (or ethics of algorithms) based on the principles “of transparency, social inclusion, responsibility,
impartiality and reliability”. Overall, according to Menecoeur (2020), the 126 documents on AI ethics identified worldwide are divided between public codes (national and international) and private guides (from companies, universities and associations). But the leaders of the United States, the People’s Republic of China and the European Union, as well as the leaders of their digital companies, apply in practice rules, codes and ethical guides that often reflect different approaches.
The ethical relativism of AI
European texts – and in particular the AI Act – are the subject of intense lobbying, particularly by GAFAM, in order to avoid open-source AI and the decommissioning of certain generative AI software. Most European think tanks recommend strengthening the regulation of AI, such as the Institut Montaigne, which launched the Objectif IA operation in favour of digital training, the Observatoire de la RSE, which is striving to put AI at the service of the application of ESG standards, and the Institute Louis Bachelier, which has initiated the Good in Tech program to measure the impact of AI on society.
But a collective of 30 global AI leaders has denounced the European approach, stating that “Europe has
become less competitive and less innovative compared to other regions, and
it now risks falling further behind in the AI era due to
inconsistent regulatory decisions“. These reactions show that AI codes and guidelines are subject to a form of “ethical relativism”, because they depend on both technological and economic factors, but also – and increasingly – on geopolitical and cultural considerations. They are interpreted differently depending on the disciplines, professions and ideologies of the AI stakeholders and ideology.
In the United States, under the influence of the Federal Guidelines for Sentencing Organisations (1991), practical guides (guidelines) are more common among American companies than among European or Chinese firms. The GAFAM companies have initiated a Partnership on AI which recommends the application of general principles and a collective commitment: “We are committed to conducting open research and dialogue on the ethical, social and economic implications of AI” (Hern, 2016). But the interpretation of these principles differs from one company to another. Google focuses on social criteria (in particular non-discrimination). Apple has a charter based on honesty and respect for stakeholders. Meta only states that it applies the professional standards in force. Amazon adopts the fundamental principles, but paradoxically states that “AI guides humans”; Microsoft and Open AI display “Codes of trust reflecting their cultures and values”; they recognise “the potential for bias in algorithms and strive to mitigate its impact”.
In Europe, reflections on the ethics of AI were launched in 2015 and led to a regulation aimed at the protection of personal data (Data Governance Act) published in 2018 (but applied in 2023), then a white paper on AI (2020), a directive on microprocessors (Chips Act, 2023) and a directive on artificial intelligence (AI Act) passed in 2024 (but applicable in 2026).
These texts seek to manage the risks induced by AI and to promote a “trustworthy AI” based on compliance with the law (Lawful AI), ethical values (ethical AI) and technical skills (robust AI). The principles – borrowed from bioethics – relate to human autonomy (respect for citizens’ rights), the prevention of any harm (protection of people and property), fairness (between users) and explainability (of software). These principles were then broken down into “requirements”: the systems (data, software, results) must “remain under human control”; they must be robust, reliable and transparent “. Compliance with the requirements is monitored by so-called “technical” methods: auditing of “trustworthy architectures” (Trustworthy AI), monitoring of the application of design standards (X-by-design), methods of explanation, testing, validation and implementation. Non-technical methods complete this system: regulation by codes, charters and guides, certification of systems, guidance on AI training and AI research.
Among these methods, the AI Act prioritizes so-called “foundational” AI systems according to three levels of risk to public and private life: models classified as “unacceptable”, leading to intrusive and discriminatory uses of AI, are prohibited; systems classified as “high-risk”, which may harm the health, safety or fundamental rights of individuals or the environment, are subject to a strict regime of software bias monitoring and data governance (in particular, false images, illicit content, and images or texts subject to copyright must be reported and corrected); “moderate-risk systems” must be subject to declarations of conformity. The European regulation aims in particular to limit the misuse of codes of ethics for the sole benefit of commercial interests (Ethic or Bluewashing). European leaders aim to transform “the European Union into a global leader in AI innovation, while ensuring that AI technologies benefit all European citizens”.
(excerpt from J-J. Pluchart’s lecture on “The impacts of AI on
ESG practices” as part of the IPM-FNEGE symposium on 25 June 2026).