V. Diversity, non discrimination and fairness

Data sets used by AI systems (both for training and operation) may suffer from the inclusion of inadvertent historic bias, incompleteness and bad governance models. The continuation of such biases could lead to (in)direct discrimination. Harm can also result from the intentional exploitation of (consumer) biases or by engaging in unfair competition. Moreover, the way in which AI systems are developed (e.g. the way in which the programming code of an algorithm is written) may also suffer from bias. Such concerns should be tackled from the beginning of the system’ development. Establishing diverse design teams and setting up mechanisms ensuring participation, in particular of citizens, in AI development can also help to address these concerns. It is advisable to consult stakeholders who may directly or indirectly be affected by the system throughout its life cycle. AI systems should consider the whole range of human abilities, skills and requirements, and ensure accessibility through a universal design approach to strive to achieve equal access for persons with disabilities.
Principle: Key requirements for trustworthy AI, Apr 8, 2019

Published by European Commission

Related Principles

· (2) Education

In a society premised on AI, we have to eliminate disparities, divisions, or socially weak people. Therefore, policy makers and managers of the enterprises involved in AI must have an accurate understanding of AI, the knowledge for proper use of AI in society and AI ethics, taking into account the complexity of AI and the possibility that AI can be misused intentionally. The AI user should understand the outline of AI and be educated to utilize it properly because AI is much more complicated than the already developed conventional tools. On the other hand, from the viewpoint of AI’s contributions to society, it is important for the developers of AI to learn about the social sciences, business models, and ethics, including normative awareness of norms and wide range of liberal arts not to mention the basis possibly generated by AI. From the above point of view, it is necessary to establish an educational environment that provides AI literacy according to the following principles, equally to every person. In order to get rid of disparity between people having a good knowledge about AI technology and those being weak in it, opportunities for education such as AI literacy are widely provided in early childhood education and primary and secondary education. The opportunities of learning about AI should be provided for the elderly people as well as workforce generation. Our society needs an education scheme by which anyone should be able to learn AI, mathematics, and data science beyond the boundaries of literature and science. Literacy education provides the following contents: 1) Data used by AI are usually contaminated by bias, 2) AI is easy to generate unwanted bias in its use, and 3) The issues of impartiality, fairness, and privacy protection which are inherent to actual use of AI. In a society in which AI is widely used, the educational environment is expected to change from the current unilateral and uniform teaching style to one that matches the interests and skill level of each individual person. Therefore, the society probably shares the view that the education system will change constantly to the above mentioned education style, regardless of the success experience in the educational system of the past. In education, it is especially important to avoid dropouts. For this, it is desirable to introduce an interactive educational environment which fully utilizes AI technologies and allows students to work together to feel a kind accomplishment. In order to develop such an educational environment, it is desirable that companies and citizens work on their own initiative, not to burden administrations and schools (teachers).





· 4. The Principle of Justice: “Be Fair”



· 8. Robustness



6. Human Centricity and Well being


Published by Personal Data Protection Commission (PDPC), Singapore in A compilation of existing AI ethical principles (Annex A), Jan 21, 2020