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

3. TRANSPARENCY

The operation and impacts of an AI system should be transparent, traceable, auditable and generally explainable to a degree appropriate to its use and potential risk profile so outcomes can be understood and challenged, particularly where they relate to people.

Principle: Trustworthy AI in Aotearoa: The AI Principles, Mar 4, 2020

Published by the Law, Society and Ethics Working Group of the AI Forum，New Zealand

Related Principles

· Transparency

As AI increasingly changes the nature of work, workers, customers and vendors need to have information about how AI systems operate so that they can understand how decisions are made. Their involvement will help to identify potential bias, errors and unintended outcomes. Transparency is not necessarily nor only a question of open source code. While in some circumstances open source code will be helpful, what is more important are clear, complete and testable explanations of what the system is doing and why. Intellectual property, and sometimes even cyber security, is rewarded by a lack of transparency. Innovation generally, including in algorithms, is a value that should be encouraged. How, then, are these competing values to be balanced? One possibility is to require algorithmic verifiability rather than full algorithmic disclosure. Algorithmic verifiability would require companies to disclose not the actual code driving the algorithm but information allowing the effect of their algorithms to be independently assessed. In the absence of transparency regarding their algorithms’ purpose and actual effect, it is impossible to ensure that competition, labour, workplace safety, privacy and liability laws are being upheld. When accidents occur, the AI and related data will need to be transparent and accountable to an accident investigator, so that the process that led to the accident can be understood.

Published by Centre for International Governance Innovation (CIGI), Canada in Toward a G20 Framework for Artificial Intelligence in the Workplace, Jul 19, 2018

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. Risk Assessment and Management

Regulatory and non regulatory approaches to AI should be based on a consistent application of risk assessment and risk management across various agencies and various technologies. It is not necessary to mitigate every foreseeable risk; in fact, a foundational principle of regulatory policy is that all activities involve tradeoffs. Instead, a risk based approach should be used to determine which risks are acceptable and which risks present the possibility of unacceptable harm, or harm that has expected costs greater than expected benefits. Agencies should be transparent about their evaluations of risk and re evaluate their assumptions and conclusions at appropriate intervals so as to foster accountability. Correspondingly, the magnitude and nature of the consequences should an AI tool fail, or for that matter succeed, can help inform the level and type of regulatory effort that is appropriate to identify and mitigate risks. Specifically, agencies should follow the direction in Executive Order 12866, “Regulatory Planning and Review,”to consider the degree and nature of the risks posed by various activities within their jurisdiction. Such an approach will, where appropriate, avoid hazard based and unnecessarily precautionary approaches to regulation that could unjustifiably inhibit innovation.

Published by The White House Office of Science and Technology Policy (OSTP), United States in Principles for the Stewardship of AI Applications, Nov 17, 2020

4. Risk Assessment and Management

Regulatory and non regulatory approaches to AI should be based on a consistent application of risk assessment and risk management across various agencies and various technologies. It is not necessary to mitigate every foreseeable risk; in fact, a foundational principle of regulatory policy is that all activities involve tradeoffs. Instead, a risk based approach should be used to determine which risks are acceptable and which risks present the possibility of unacceptable harm, or harm that has expected costs greater than expected benefits. Agencies should be transparent about their evaluations of risk and re evaluate their assumptions and conclusions at appropriate intervals so as to foster accountability. Correspondingly, the magnitude and nature of the consequences should an AI tool fail, or for that matter succeed, can help inform the level and type of regulatory effort that is appropriate to identify and mitigate risks. Specifically, agencies should follow the direction in Executive Order 12866, “Regulatory Planning and Review,”to consider the degree and nature of the risks posed by various activities within their jurisdiction. Such an approach will, where appropriate, avoid hazard based and unnecessarily precautionary approaches to regulation that could unjustifiably inhibit innovation.

Published by The White House Office of Science and Technology Policy (OSTP), United States in Principles for the Stewardship of AI Applications, Nov 17, 2020