AI Applications We Will Not Pursue

In addition to the above objectives, we will not design or deploy AI in the following application areas: 1. Technologies that cause or are likely to cause overall harm. Where there is a material risk of harm, we will proceed only where we believe that the benefits substantially outweigh the risks, and will incorporate appropriate safety constraints. 2. Weapons or other technologies whose principal purpose or implementation is to cause or directly facilitate injury to people. 3. Technologies that gather or use information for surveillance violating internationally accepted norms. 4. Technologies whose purpose contravenes widely accepted principles of international law and human rights. As our experience in this space deepens, this list may evolve.
Principle: Artificial Intelligence at Google: Our Principles, Jun 7, 2018

Published by Google

Related Principles

· 2. The Principle of Non maleficence: “Do no Harm”

AI systems should not harm human beings. By design, AI systems should protect the dignity, integrity, liberty, privacy, safety, and security of human beings in society and at work. AI systems should not threaten the democratic process, freedom of expression, freedoms of identify, or the possibility to refuse AI services. At the very least, AI systems should not be designed in a way that enhances existing harms or creates new harms for individuals. Harms can be physical, psychological, financial or social. AI specific harms may stem from the treatment of data on individuals (i.e. how it is collected, stored, used, etc.). To avoid harm, data collected and used for training of AI algorithms must be done in a way that avoids discrimination, manipulation, or negative profiling. Of equal importance, AI systems should be developed and implemented in a way that protects societies from ideological polarization and algorithmic determinism. Vulnerable demographics (e.g. children, minorities, disabled persons, elderly persons, or immigrants) should receive greater attention to the prevention of harm, given their unique status in society. Inclusion and diversity are key ingredients for the prevention of harm to ensure suitability of these systems across cultures, genders, ages, life choices, etc. Therefore not only should AI be designed with the impact on various vulnerable demographics in mind but the above mentioned demographics should have a place in the design process (rather through testing, validating, or other). Avoiding harm may also be viewed in terms of harm to the environment and animals, thus the development of environmentally friendly AI may be considered part of the principle of avoiding harm. The Earth’s resources can be valued in and of themselves or as a resource for humans to consume. In either case it is necessary to ensure that the research, development, and use of AI are done with an eye towards environmental awareness.

Published by The European Commission’s High-Level Expert Group on Artificial Intelligence in Draft Ethics Guidelines for Trustworthy AI, Dec 18, 2018

4. Principle of safety

Developers should take it into consideration that AI systems will not harm the life, body, or property of users or third parties through actuators or other devices. [Comment] AI systems which are supposed to be subject to this principle are such ones that might harm the life, body, or property of users or third parties through actuators or other devices. It is encouraged that developers refer to relevant international standards and pay attention to the followings, with particular consideration of the possibility that outputs or programs might change as a result of learning or other methods of AI systems: ● To make efforts to conduct verification and validation in advance in order to assess and mitigate the risks related to the safety of the AI systems. ● To make efforts to implement measures, throughout the development stage of AI systems to the extent possible in light of the characteristics of the technologies to be adopted, to contribute to the intrinsic safety (reduction of essential risk factors such as kinetic energy of actuators) and the functional safety (mitigation of risks by operation of additional control devices such as automatic braking) when AI systems work with actuators or other devices. And ● To make efforts to explain the designers’ intent of AI systems and the reasons for it to stakeholders such as users, when developing AI systems to be used for making judgments regarding the safety of life, body, or property of users and third parties (for example, such judgments that prioritizes life, body, property to be protected at the time of an accident of a robot equipped with AI).

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

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

4 Foster responsibility and accountability

Humans require clear, transparent specification of the tasks that systems can perform and the conditions under which they can achieve the desired level of performance; this helps to ensure that health care providers can use an AI technology responsibly. Although AI technologies perform specific tasks, it is the responsibility of human stakeholders to ensure that they can perform those tasks and that they are used under appropriate conditions. Responsibility can be assured by application of “human warranty”, which implies evaluation by patients and clinicians in the development and deployment of AI technologies. In human warranty, regulatory principles are applied upstream and downstream of the algorithm by establishing points of human supervision. The critical points of supervision are identified by discussions among professionals, patients and designers. The goal is to ensure that the algorithm remains on a machine learning development path that is medically effective, can be interrogated and is ethically responsible; it involves active partnership with patients and the public, such as meaningful public consultation and debate (101). Ultimately, such work should be validated by regulatory agencies or other supervisory authorities. When something does go wrong in application of an AI technology, there should be accountability. Appropriate mechanisms should be adopted to ensure questioning by and redress for individuals and groups adversely affected by algorithmically informed decisions. This should include access to prompt, effective remedies and redress from governments and companies that deploy AI technologies for health care. Redress should include compensation, rehabilitation, restitution, sanctions where necessary and a guarantee of non repetition. The use of AI technologies in medicine requires attribution of responsibility within complex systems in which responsibility is distributed among numerous agents. When medical decisions by AI technologies harm individuals, responsibility and accountability processes should clearly identify the relative roles of manufacturers and clinical users in the harm. This is an evolving challenge and remains unsettled in the laws of most countries. Institutions have not only legal liability but also a duty to assume responsibility for decisions made by the algorithms they use, even if it is not feasible to explain in detail how the algorithms produce their results. To avoid diffusion of responsibility, in which “everybody’s problem becomes nobody’s responsibility”, a faultless responsibility model (“collective responsibility”), in which all the agents involved in the development and deployment of an AI technology are held responsible, can encourage all actors to act with integrity and minimize harm. In such a model, the actual intentions of each agent (or actor) or their ability to control an outcome are not considered.

Published by World Health Organization (WHO) in Key ethical principles for use of artificial intelligence for health, Jun 28, 2021