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

Second principle: Responsibility

Human responsibility for AI enabled systems must be clearly established, ensuring accountability for their outcomes, with clearly defined means by which human control is exercised throughout their lifecycles. The increased speed, complexity and automation of AI enabled systems may complicate our understanding of pre existing concepts of human control, responsibility and accountability. This may occur through the sorting and filtering of information presented to decision makers, the automation of previously human led processes, or processes by which AI enabled systems learn and evolve after their initial deployment. Nevertheless, as unique moral agents, humans must always be responsible for the ethical use of AI in Defence. Human responsibility for the use of AI enabled systems in Defence must be underpinned by a clear and consistent articulation of the means by which human control is exercised, and the nature and limitations of that control. While the level of human control will vary according to the context and capabilities of each AI enabled system, the ability to exercise human judgement over their outcomes is essential. Irrespective of the use case, Responsibility for each element of an AI enabled system, and an articulation of risk ownership, must be clearly defined from development, through deployment – including redeployment in new contexts – to decommissioning. This includes cases where systems are complex amalgamations of AI and non AI components, from multiple different suppliers. In this way, certain aspects of responsibility may reach beyond the team deploying a particular system, to other functions within the MOD, or beyond, to the third parties which build or integrate AI enabled systems for Defence. Collectively, these articulations of human control, responsibility and risk ownership must enable clear accountability for the outcomes of any AI enabled system in Defence. There must be no deployment or use without clear lines of responsibility and accountability, which should not be accepted by the designated duty holder unless they are satisfied that they can exercise control commensurate with the various risks.

Principle: Ethical Principles for AI in Defence, Jun 15, 2022

Published by The Ministry of Defence (MOD), United Kingdom

Related Principles

Accountability

Those responsible for the different phases of the AI system lifecycle should be identifiable and accountable for the outcomes of the AI systems, and human oversight of AI systems should be enabled. This principle aims to acknowledge the relevant organisations' and individuals’ responsibility for the outcomes of the AI systems that they design, develop, deploy and operate. The application of legal principles regarding accountability for AI systems is still developing. Mechanisms should be put in place to ensure responsibility and accountability for AI systems and their outcomes. This includes both before and after their design, development, deployment and operation. The organisation and individual accountable for the decision should be identifiable as necessary. They must consider the appropriate level of human control or oversight for the particular AI system or use case. AI systems that have a significant impact on an individual's rights should be accountable to external review, this includes providing timely, accurate, and complete information for the purposes of independent oversight bodies.

Published by Department of Industry, Innovation and Science, Australian Government in AI Ethics Principles, Nov 7, 2019

I. Human agency and oversight

AI systems should support individuals in making better, more informed choices in accordance with their goals. They should act as enablers to a flourishing and equitable society by supporting human agency and fundamental rights, and not decrease, limit or misguide human autonomy. The overall wellbeing of the user should be central to the system's functionality. Human oversight helps ensuring that an AI system does not undermine human autonomy or causes other adverse effects. Depending on the specific AI based system and its application area, the appropriate degrees of control measures, including the adaptability, accuracy and explainability of AI based systems, should be ensured. Oversight may be achieved through governance mechanisms such as ensuring a human in the loop, human on the loop, or human in command approach. It must be ensured that public authorities have the ability to exercise their oversight powers in line with their mandates. All other things being equal, the less oversight a human can exercise over an AI system, the more extensive testing and stricter governance is required.

Published by European Commission in Key requirements for trustworthy AI, Apr 8, 2019

Third principle: Understanding

AI enabled systems, and their outputs, must be appropriately understood by relevant individuals, with mechanisms to enable this understanding made an explicit part of system design. Effective and ethical decision making in Defence, from the frontline of combat to back office operations, is always underpinned by appropriate understanding of context by those making decisions. Defence personnel must have an appropriate, context specific understanding of the AI enabled systems they operate and work alongside. This level of understanding will naturally differ depending on the knowledge required to act ethically in a given role and with a given system. It may include an understanding of the general characteristics, benefits and limitations of AI systems. It may require knowledge of a system’s purposes and correct environment for use, including scenarios where a system should not be deployed or used. It may also demand an understanding of system performance and potential fail states. Our people must be suitably trained and competent to operate or understand these tools. To enable this understanding, we must be able to verify that our AI enabled systems work as intended. While the ‘black box’ nature of some machine learning systems means that they are difficult to fully explain, we must be able to audit either the systems or their outputs to a level that satisfies those who are duly and formally responsible and accountable. Mechanisms to interpret and understand our systems must be a crucial and explicit part of system design across the entire lifecycle. This requirement for context specific understanding based on technically understandable systems must also reach beyond the MOD, to commercial suppliers, allied forces and civilians. Whilst absolute transparency as to the workings of each AI enabled system is neither desirable nor practicable, public consent and collaboration depend on context specific shared understanding. What our systems do, how we intend to use them, and our processes for ensuring beneficial outcomes result from their use should be as transparent as possible, within the necessary constraints of the national security context.

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

· Transparency and explainability

37. 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. 38. 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. 39. 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. 40. 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. 41. 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 The Recommendation on the Ethics of Artificial Intelligence, Nov 24, 2021

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