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The letter A also stands for accountability, i.e. clarity in the ascription of responsibility related to the consequences of the use of AI methods. Lastly, A also stands for intelligence augmentation, rather than its replacement: AI systems should be used to support and complement human decision-making, not to replace humans.

T for trust and transparency

Trust is a fundamental pillar in our relationships and comes about when three conditions are met: competence, which is the capacity to successfully execute the task undertaken; reliability, understood as competence maintained over time; and honesty and transparency. Therefore, the T in FATEN is also for transparency. A data-based decision-making system is transparent when non-experts can easily observe and understand it. Transparent, interpretable and explainable AI models are needed especially in those cases of application with social impact.

E for bEneficence and equity

The principle of bEneficence is about optimising the positive impact of AI with sustainability, diversity and truthfulness. We cannot ignore the environmental cost of technological development, particularly when it comes to deep learning algorithms, given their need for large amounts of computation and data to learn from.

Diversity is also of paramount importance. Firstly, in the composition of teams developing AI systems to optimise the likelihood of finding innovative solutions to the immense challenges we face and to develop[14] inclusive solutions that are relevant to the communities where they will be implemented. Secondly, we need to incorporate diversity criteria into algorithms to minimise the prevalence of filter bubbles and echo chamber effects, which contribute, at least in part, to the polarisation of public opinion.

We must also ensure the “veracity” of data. Today, we can algorithmically generate fake texts, audios, photos and videos through deep neural network generative models (known as deep fakes) that for humans are indistinguishable from real content. This fake content, shared by millions of people, can impact the formation of public opinion on issues of great social relevance, such as vaccines or electoral processes.

E also stands for equity. The principles of universal access to knowledge and the democratisation of technology are in jeopardy, due to the extreme dominance of technology giants in the United States (Apple, Amazon, Microsoft, Facebook and Alphabet/Google) and China (Tencent, Alibaba and Baidu). This market dominance leads to data dominance. In fact, most of these tech giants are data companies that earn billions of dollars by analysing and monetising the information they collect about their users. A significant share of valuable human behavioural data that could be used for social good is generated and captured by these tech companies.

Moreover, the 21^st century is witnessing a polarisation in the distribution of wealth. According to Credit Suisse’s Global Wealth Report, the 100 richest people in the world accumulate more wealth than the poorest 4 billion people. This accumulation in the hands of the very few has been attributed, at least somewhat, to technology and the Fourth Industrial Revolution. One could argue that data, and especially the capacity to harness and make sense of data, is the asset that generates the most wealth (data economy). Therefore, if we seek to maximise the positive impact of this abundance of data, we must develop and promote new models of data ownership, management, exploitation and regulation.

N for non-maleficence

That is, minimising the negative impact that could result from the use of AI methods. Included within this principle are “prudence” in the development of AI-based systems; guarantees of reliability, robustness and reproducibility; data security and preservation of “privacy”. Safety verification and thorough evaluation of AI models before deployment in the real world, particularly of deep neural networks, are imperative.

In light of the complex nature of the challenges faced, a pooling of experts from different disciplines is necessary to ensure the projects’ success. This multidisciplinary nature adds complexity, but is necessary and particularly beneficial, as teams would include ethicists.

External monitoring bodies are also necessary to ensure compliance with ethical principles. In recent years, the creation of data stewards, responsible for data quality and governance in each organisation; external oversight ethics committees; and/or the appointment of an advisor with oversight responsibilities to ensure that projects with social impact are aligned with FATEN principles have been proposed.

An alternative is to require the use of open processes, codes and systems, by deploying regulations that require compliance with ethical principles and/or by encouraging knowledge sharing. Furthermore, it is essential to understand the cultural and social characteristics of the societies in which projects are rolled out. Therefore, working with local institutions and civil society in the countries and regions where projects will be implemented is absolutely necessary.

It is time to take action as a society, collectively defining the ethical and political horizons of AI, because we are talking about science and technology, but also about rights, economy, democracy, equality, inclusion, citizenship, peace and power.

It is time to educate, to learn, to rise above apocalyptic sensationalism, to own our fate, to regulate AI wisely, and to focus on ending the abusive practices and social harm caused by the companies behind the advances in artificial intelligence, which in the last decade have amassed unprecedented power and have contributed to social inequality.

It is time to invest in artificial intelligence that contributes to progress, leaving no one behind and not destroying the planet in the process. To quote Theodore Roosevelt, “a revolution is sometimes necessary”. Since there is no planet B, I invite you to join the AI revolution for and by the people and the planet.

References

Oliver, N. “Artificial Intelligence for Social Good: The Way Forward”, capítol 11 del SRIP Report 2022. SRIP Report of the European Commission, 664-707. 2022.

Oliver, N. “Governance in the Era of Data-driven Decision-making Algorithms”. Women Shaping Global Economic Governance. Center for Economic Policy Research Press (CEPR), 2019.

Geschke, D., Lorenz, J., i Holtz, P. “The triple-filter bubble: Using agent-based modelling to test a meta-theoretical framework for the emergence of filter bubbles and echo chambers”. British Journal of Social Psychology, 58, 129-149. 2019.

Shorrocks, A. i Hechler-Fayd’herbe, N. Global Wealth Report 2019: Global wealth rises by 2.6% driven by US & China, despite trade tensions. Credit Suisse, Zúric, 2019.

Verhulst, S. G. “The Three Goals and Five Functions of Data Stewards. Data Stewards: a new Role and Responsibility for an AI and Data Age”. Medium i The Data Stewards Network, Nova York, 2018.