Safe and Ethical AI (SEA) Platform Network · Linking Artificial Intelligence Principles (LAIP)

E. Governability:

AI applications will be developed and used according to their intended functions and will allow for: appropriate human machine interaction; the ability to detect and avoid unintended consequences; and the ability to take steps, such as disengagement or deactivation of systems, when such systems demonstrate unintended behaviour.

Principle: NATO Principles of Responsible Use of Artificial Intelligence in Defence, Oct 22, 2021

Published by The North Atlantic Treaty Organization (NATO)

Related Principles

5. Governable.

DoD AI systems should be designed and engineered to fulfill their intended function while possessing the ability to detect and avoid unintended harm or disruption, and for human or automated disengagement or deactivation of deployed systems that demonstrate unintended escalatory or other behavior.

Published by Defense Innovation Board (DIB), Department of Defense (DoD), United States in AI Ethics Principles for DoD, Oct 31, 2019

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

3. Safe

Data enhanced technologies like AI and ML systems must function in a safe and secure way throughout their life cycles and potential risks should be continually assessed and managed. Designers, policy makers and developers should embed appropriate safeguards throughout the life cycle of the system to ensure it is working as intended. This would include mechanisms related to system testing, piloting, scaling and human intervention as well as alternative processes in case a complete halt of system operations is required. The mechanisms must be appropriate to the context and determined before deployment but should be iterated upon throughout the system’s life cycle. Why it matters Automated algorithmic decisions can reflect and amplify undesirable patterns in the data they are trained on. As well, issues with the system can arise that only become apparent after the system is deployed. Therefore, despite our best efforts unexpected outcomes and impacts need to be considered. Accordingly, systems will require ongoing monitoring and mitigation planning to ensure that if the algorithmic system is making decisions that are not intended, a human can adapt, correct or improve the system.

Published by Government of Ontario, Canada in Principles for Ethical Use of AI [Beta], Sept 14, 2023

Fourth principle: Bias and Harm Mitigation

Those responsible for AI enabled systems must proactively mitigate the risk of unexpected or unintended biases or harms resulting from these systems, whether through their original rollout, or as they learn, change or are redeployed. AI enabled systems offer significant benefits for Defence. However, the use of AI enabled systems may also cause harms (beyond those already accepted under existing ethical and legal frameworks) to those using them or affected by their deployment. These may range from harms caused by a lack of suitable privacy for personal data, to unintended military harms due to system unpredictability. Such harms may change over time as systems learn and evolve, or as they are deployed beyond their original setting. Of particular concern is the risk of discriminatory outcomes resulting from algorithmic bias or skewed data sets. Defence must ensure that its AI enabled systems do not result in unfair bias or discrimination, in line with the MOD’s ongoing strategies for diversity and inclusion. A principle of bias and harm mitigation requires the assessment and, wherever possible, the mitigation of these biases or harms. This includes addressing bias in algorithmic decision making, carefully curating and managing datasets, setting safeguards and performance thresholds throughout the system lifecycle, managing environmental effects, and applying strict development criteria for new systems, or existing systems being applied to a new context.

Published by The Ministry of Defence (MOD), United Kingdom in Ethical Principles for AI in Defence, Jun 15, 2022

5. Governable

The department will design and engineer AI capabilities to fulfill their intended functions while possessing the ability to detect and avoid unintended consequences, and the ability to disengage or deactivate deployed systems that demonstrate unintended behavior.

Published by Department of Defense (DoD), United States in DoD's AI ethical principles, Feb 24, 2020