We reaffirm that the use of AI must take place within the context of the existing DoD ethical framework. Building on this foundation, we propose the following principles, which are more specific to AI, and note that they apply to both combat and non combat systems. AI is a rapidly developing field, and no organization that currently develops or fields AI systems or espouses AI ethics principles can claim to have solved all the challenges embedded in the following principles. However, the Department should set the goal that its use of AI systems is:
Principle: AI Ethics Principles for DoD, Oct 31, 2019

Published by Defense Innovation Board (DIB), Department of Defense (DoD), United States

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, 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


2.1. Risk based approach. The level of attention to ethical issues in AI and the nature of the relevant actions of AI Actors should be proportional to the assessment of the level of risk posed by specific technologies and AISs and the interests of individuals and society. Risk level assessment must take into account both the known and possible risks; in this case, the level of probability of threats should be taken into account as well as their possible scale in the short and long term. In the field of AI development, making decisions that are significant to society and the state should be accompanied by scientifically verified and interdisciplinary forecasting of socio economic consequences and risks, as well as by the examination of possible changes in the value and cultural paradigm of the development of society, while taking into account national priorities. In pursuance of this Code, the development and use of an AIS risk assessment methodology is recommended. 2.2. Responsible attitude. AI Actors should have a responsible approach to the aspects of AIS that influence society and citizens at every stage of the AIS life cycle. These include privacy; the ethical, safe and responsible use of personal data; the nature, degree and amount of damage that may follow as a result of the use of the technology and AIS; and the selection and use of companion hardware and software. In this case, the responsibility of the AI Actors must correspond to the nature, degree and amount of damage that may occur as a result of the use of technologies and AIS, while taking into account the role of the AI Actor in the life cycle of AIS, as well as the degree of possible and real impact of a particular AI Actor on causing damage, as well as its size. 2.3. Precautions. When the activities of AI Actors can lead to morally unacceptable consequences for individuals and society, the occurrence of which the corresponding AI Actor can reasonably assume, measures should be taken to prevent or limit the occurrence of such consequences. To assess the moral acceptability of consequences and the possible measures to prevent them, Actors can use the provisions of this Code, including the mechanisms specified in Section 2. 2.4. No harm. AI Actors should not allow use of AI technologies for the purpose of causing harm to human life, the environment and or the health or property of citizens and legal entities. Any application of an AIS capable of purposefully causing harm to the environment, human life or health or the property of citizens and legal entities during any stage, including design, development, testing, implementation or operation, is unacceptable. 2.5. Identification of AI in communication with a human. AI Actors are encouraged to ensure that users are informed of their interactions with the AIS when it affects their rights and critical areas of their lives and to ensure that such interactions can be terminated at the request of the user. 2.6. Data security AI Actors must comply with the legislation of the Russian Federation in the field of personal data and secrets protected by law when using an AIS. Furthermore, they must ensure the protection and protection of personal data processed by an AIS or AI Actors in order to develop and improve the AIS by developing and implementing innovative methods of controlling unauthorized access by third parties to personal data and using high quality and representative datasets from reliable sources and obtained without breaking the law. 2.7. Information security. AI Actors should provide the maximum possible protection against unauthorized interference in the work of the AI by third parties by introducing adequate information security technologies, including the use of internal mechanisms for protecting the AIS from unauthorized interventions and informing users and developers about such interventions. They must also inform users about the rules regarding information security when using the AIS. 2.8. Voluntary certification and Code compliance. AI Actors can implement voluntary certification for the compliance of the developed AI technologies with the standards established by the legislation of the Russian Federation and this Code. AI Actors can create voluntary certification and AIS labeling systems that indicate that these systems have passed voluntary certification procedures and confirm quality standards. 2.9. Control of the recursive self improvement of AISs. AI Actors are encouraged to collaborate in the identification and verification of methods and forms of creating universal ("strong") AIS and the prevention of the possible threats that AIS carry. The use of "strong" AI technologies should be under the control of the state.

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

2. Transparent and explainable AI

We will be explicit about the kind of personal and or non personal data the AI systems uses as well as about the purpose the data is used for. When people directly interact with an AI system, we will be transparent to the users that this is the case. When AI systems take, or support, decisions we take the technical and organizational measures required to guarantee a level of understanding adequate to the application area. In any case, if the decisions significantly affect people's lives, we will ensure we understand the logic behind the conclusions. This will also apply when we use third party technology.

Published by Telefónica in AI Principles of Telefónica, Oct 30, 2018


The Principles of Artificial Intelligence (AI) Ethics for the Intelligence Community (IC) are intended to guide personnel on whether and how to develop and use AI, to include machine learning, in furtherance of the IC’s mission. These Principles supplement the Principles of Professional Ethics for the IC and do not modify or supersede applicable laws, executive orders, or policies. Instead, they articulate the general norms that IC elements should follow in applying those authorities and requirements. To assist with the implementation of these Principles, the IC has also created an AI Ethics Framework to guide personnel who are determining whether and how to procure, design, build, use, protect, consume, and manage AI and other advanced analytics. The Intelligence Community commits to the design, development, and use of AI with the following principles:

Published by Intelligence Community (IC), United States in Principles of Artificial Intelligence Ethics for the Intelligence Community, Jul 23, 2020