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

· 4. Governance of AI Autonomy (Human oversight)

The correct approach to assuring properties such as safety, accuracy, adaptability, privacy, explicability, compliance with the rule of law and ethical conformity heavily depends on specific details of the AI system, its area of application, its level of impact on individuals, communities or society and its level of autonomy. The level of autonomy results from the use case and the degree of sophistication needed for a task. All other things being equal, the greater degree of autonomy that is given to an AI system, the more extensive testing and stricter governance is required. It must be ensured that AI systems continue to behave as intended when feedback signals become sparser. Depending on the area of application and or the level of impact on individuals, communities or society of the AI system, different levels or instances of governance (incl. human oversight) will be necessary. This is relevant for a large number of AI applications, and more particularly for the use of AI to suggest or take decisions concerning individuals or communities (algorithmic decision support). Good governance of AI autonomy in this respect includes for instance more or earlier human intervention depending on the level of societal impact of the AI system. This also includes the predicament that a user of an AI system, particularly in a work or decision making environment, is allowed to deviate from a path or decision chosen or recommended by the AI system.

Principle: Draft Ethics Guidelines for Trustworthy AI, Dec 18, 2018

Published by The European Commission’s High-Level Expert Group on Artificial Intelligence

Related Principles

Contestability

When an AI system significantly impacts a person, community, group or environment, there should be a timely process to allow people to challenge the use or output of the AI system. This principle aims to ensure the provision of efficient, accessible mechanisms that allow people to challenge the use or output of an AI system, when that AI system significantly impacts a person, community, group or environment. The definition of the threshold for ‘significant impact’ will depend on the context, impact and application of the AI system in question. Knowing that redress for harm is possible, when things go wrong, is key to ensuring public trust in AI. Particular attention should be paid to vulnerable persons or groups. There should be sufficient access to the information available to the algorithm, and inferences drawn, to make contestability effective. In the case of decisions significantly affecting rights, there should be an effective system of oversight, which makes appropriate use of human judgment.

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

· 8. Robustness

Trustworthy AI requires that algorithms are secure, reliable as well as robust enough to deal with errors or inconsistencies during the design, development, execution, deployment and use phase of the AI system, and to adequately cope with erroneous outcomes. Reliability & Reproducibility. Trustworthiness requires that the accuracy of results can be confirmed and reproduced by independent evaluation. However, the complexity, non determinism and opacity of many AI systems, together with sensitivity to training model building conditions, can make it difficult to reproduce results. Currently there is an increased awareness within the AI research community that reproducibility is a critical requirement in the field. Reproducibility is essential to guarantee that results are consistent across different situations, computational frameworks and input data. The lack of reproducibility can lead to unintended discrimination in AI decisions. Accuracy. Accuracy pertains to an AI’s confidence and ability to correctly classify information into the correct categories, or its ability to make correct predictions, recommendations, or decisions based on data or models. An explicit and well formed development and evaluation process can support, mitigate and correct unintended risks. Resilience to Attack. AI systems, like all software systems, can include vulnerabilities that can allow them to be exploited by adversaries. Hacking is an important case of intentional harm, by which the system will purposefully follow a different course of action than its original purpose. If an AI system is attacked, the data as well as system behaviour can be changed, leading the system to make different decisions, or causing the system to shut down altogether. Systems and or data can also become corrupted, by malicious intention or by exposure to unexpected situations. Poor governance, by which it becomes possible to intentionally or unintentionally tamper with the data, or grant access to the algorithms to unauthorised entities, can also result in discrimination, erroneous decisions, or even physical harm. Fall back plan. A secure AI has safeguards that enable a fall back plan in case of problems with the AI system. In some cases this can mean that the AI system switches from statistical to rule based procedure, in other cases it means that the system asks for a human operator before continuing the action.

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

· 2. NEED FOR CONSCIOUS RESPONSIBILITY WHEN CREATING AND USING AI

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

· 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