2. Artificial intelligence should operate on principles of intelligibility and fairness.

Companies and organisations need to improve the intelligibility of their AI systems. Without this, regulators may need to step in and prohibit the use of opaque technology in significant and sensitive areas of life and society. To ensure that our use of AI does not inadvertently prejudice the treatment of particular groups in society, we call for the Government to incentivise the development of new approaches to the auditing of datasets used in AI, and to encourage greater diversity in the training and recruitment of AI specialists.
Principle: AI Code, Apr 16, 2018

Published by House of Lords, Select Committee on Artificial Intelligence

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).

Published by Cabinet Office, Government of Japan in Social Principles of Human-centric AI (Draft), Dec 27, 2018

· (4) Security

Positive utilization of AI means that many social systems will be automated, and the safety of the systems will be improved. On the other hand, within the scope of today's technologies, it is impossible for AI to respond appropriately to rare events or deliberate attacks. Therefore, there is a new security risk for the use of AI. Society should always be aware of the balance of benefits and risks, and should work to improve social safety and sustainability as a whole. Society must promote broad and deep research and development in AI (from immediate measures to deep understanding), such as the proper evaluation of risks in the utilization of AI and research to reduce risks. Society must also pay attention to risk management, including cybersecurity awareness. Society should always pay attention to sustainability in the use of AI. Society should not, in particular, be uniquely dependent on single AI or a few specified AI.

Published by Cabinet Office, Government of Japan in Social Principles of Human-centric AI (Draft), Dec 27, 2018

3. Principle of controllability

Developers should pay attention to the controllability of AI systems. [Comment] In order to assess the risks related to the controllability of AI systems, it is encouraged that developers make efforts to conduct verification and validation in advance. One of the conceivable methods of risk assessment is to conduct experiments in a closed space such as in a laboratory or a sandbox in which security is ensured, at a stage before the practical application in society. In addition, in order to ensure the controllability of AI systems, it is encouraged that developers pay attention to whether the supervision (such as monitoring or warnings) and countermeasures (such as system shutdown, cut off from networks, or repairs) by humans or other trustworthy AI systems are effective, to the extent possible in light of the characteristics of the technologies to be adopted. [Note] Verification and validation are methods for evaluating and controlling risks in advance. Generally, the former is used for confirming formal consistency, while the latter is used for confirming substantial validity. (See, e.g., The Future of Life Institute (FLI), Research Priorities for Robust and Beneficial Artificial Intelligence (2015)). [Note] Examples of what to see in the risk assessment are risks of reward hacking in which AI systems formally achieve the goals assigned but substantially do not meet the developer's intents, and risks that AI systems work in ways that the developers have not intended due to the changes of their outputs and programs in the process of the utilization with their learning, etc. For reward hacking, see, e.g., Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman & Dan Mané, Concrete Problems in AI Safety, arXiv: 1606.06565 [cs.AI] (2016).

Published by Ministry of Internal Affairs and Communications (MIC), the Government of Japan in AI R&D Principles, Jul 28, 2017


1.1. Human centered and humanistic approach. In the development of AI technologies, the rights and freedoms of the individual should be given the greatest value. AI technologies developed by AI Actors should promote or not hinder the realization of humans’ capabilities to achieve harmony in social, economic and spiritual spheres, as well as in the highest self fulfillment of human beings. They should take into account key values such as the preservation and development of human cognitive abilities and creative potential; the preservation of moral, spiritual and cultural values; the promotion of cultural and linguistic diversity and identity; and the preservation of traditions and the foundations of nations, peoples and ethnic and social groups. A human centered and humanistic approach is the basic ethical principle and central criterion for assessing the ethical behavior of AI Actors, which are listed in the section 2 of this Code. 1.2. Respect for human autonomy and freedom of will. AI Actors should take all necessary measures to preserve the autonomy and free will of a human‘s decision making ability, the right to choose, and, in general, the intellectual abilities of a human as an intrinsic value and a system forming factor of modern civilization. AI Actors should, during AIS creation, assess the possible negative consequences for the development of human cognitive abilities and prevent the development of AIS that purposefully cause such consequences. 1.3. Compliance with the law. AI Actors must know and comply with the provisions of the legislation of the Russian Federation in all areas of their activities and at all stages of the creation, development and use of AI technologies, including in matters of the legal responsibility of AI Actors. 1.4. Non discrimination. To ensure fairness and non discrimination, AI Actors should take measures to verify that the algorithms, datasets and processing methods for machine learning that are used to group and or classify data concerning individuals or groups do not intentionally discriminate. AI Actors are encouraged to create and apply methods and software solutions that identify and prevent discrimination based on race, nationality, gender, political views, religious beliefs, age, social and economic status, or information about private life. (At the same time, cannot be considered as discrimination rules, which are explicitly declared by an AI Actor for functioning or the application of AIS for the different groups of users, with such factors taken into account for segmentation) 1.5. Assessment of risks and humanitarian impact. AI Actors are encouraged to assess the potential risks of using an AIS, including the social consequences for individuals, society and the state, as well as the humanitarian impact of the AIS on human rights and freedoms at different stages, including during the formation and use of datasets. AI Actors should also carry out long term monitoring of the manifestations of such risks and take into account the complexity of the behavior of AIS during risk assessment, including the relationship and the interdependence of processes in the AIS’s life cycle. For critical applications of the AIS, in special cases, it is encouraged that a risk assessment be conducted through the involvement of a neutral third party or authorized official body when to do so would not harm the performance and information security of the AIS and would ensure the protection of the intellectual property and trade secrets of the developer.

Published by AI Alliance Russia in Artificial Intelligence Code of Ethics, Oct 26, 2021

· Transparency and explainability

The transparency and explainability of AI systems are often essential preconditions to ensure the respect, protection and promotion of human rights, fundamental freedoms and ethical principles. Transparency is necessary for relevant national and international liability regimes to work effectively. A lack of transparency could also undermine the possibility of effectively challenging decisions based on outcomes produced by AI systems and may thereby infringe the right to a fair trial and effective remedy, and limits the areas in which these systems can be legally used. While efforts need to be made to increase transparency and explainability of AI systems, including those with extra territorial impact, throughout their life cycle to support democratic governance, the level of transparency and explainability should always be appropriate to the context and impact, as there may be a need to balance between transparency and explainability and other principles such as privacy, safety and security. People should be fully informed when a decision is informed by or is made on the basis of AI algorithms, including when it affects their safety or human rights, and in those circumstances should have the opportunity to request explanatory information from the relevant AI actor or public sector institutions. In addition, individuals should be able to access the reasons for a decision affecting their rights and freedoms, and have the option of making submissions to a designated staff member of the private sector company or public sector institution able to review and correct the decision. AI actors should inform users when a product or service is provided directly or with the assistance of AI systems in a proper and timely manner. From a socio technical lens, greater transparency contributes to more peaceful, just, democratic and inclusive societies. It allows for public scrutiny that can decrease corruption and discrimination, and can also help detect and prevent negative impacts on human rights. Transparency aims at providing appropriate information to the respective addressees to enable their understanding and foster trust. Specific to the AI system, transparency can enable people to understand how each stage of an AI system is put in place, appropriate to the context and sensitivity of the AI system. It may also include insight into factors that affect a specific prediction or decision, and whether or not appropriate assurances (such as safety or fairness measures) are in place. In cases of serious threats of adverse human rights impacts, transparency may also require the sharing of code or datasets. Explainability refers to making intelligible and providing insight into the outcome of AI systems. The explainability of AI systems also refers to the understandability of the input, output and the functioning of each algorithmic building block and how it contributes to the outcome of the systems. Thus, explainability is closely related to transparency, as outcomes and ub processes leading to outcomes should aim to be understandable and traceable, appropriate to the context. AI actors should commit to ensuring that the algorithms developed are explainable. In the case of AI applications that impact the end user in a way that is not temporary, easily reversible or otherwise low risk, it should be ensured that the meaningful explanation is provided with any decision that resulted in the action taken in order for the outcome to be considered transparent. Transparency and explainability relate closely to adequate responsibility and accountability measures, as well as to the trustworthiness of AI systems.

Published by The United Nations Educational, Scientific and Cultural Organization (UNESCO) in Draft Text of The Recommendation on the Ethics of Artificial Intelligence, Nov 24, 2021