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

· 5. The Principle of Explicability: “Operate transparently”

Transparency is key to building and maintaining citizen’s trust in the developers of AI systems and AI systems themselves. Both technological and business model transparency matter from an ethical standpoint. Technological transparency implies that AI systems be auditable, comprehensible and intelligible by human beings at varying levels of comprehension and expertise. Business model transparency means that human beings are knowingly informed of the intention of developers and technology implementers of AI systems. Explicability is a precondition for achieving informed consent from individuals interacting with AI systems and in order to ensure that the principle of explicability and non maleficence are achieved the requirement of informed consent should be sought. Explicability also requires accountability measures be put in place. Individuals and groups may request evidence of the baseline parameters and instructions given as inputs for AI decision making (the discovery or prediction sought by an AI system or the factors involved in the discovery or prediction made) by the organisations and developers of an AI system, the technology implementers, or another party in the supply chain.

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

1. Transparency and Explainability

Transparency refers to providing disclosure on when an AI system is being used and the involvement of an AI system in decision making, what kind of data it uses, and its purpose. By disclosing to individuals that AI is used in the system, individuals will become aware and can make an informed choice of whether to use the AIenabled system. Explainability is the ability to communicate the reasoning behind an AI system’s decision in a way that is understandable to a range of people, as it is not always clear how an AI system has arrived at a conclusion. This allows individuals to know the factors contributing to the AI system’s recommendation. In order to build public trust in AI, it is important to ensure that users are aware of the use of AI technology and understand how information from their interaction is used and how the AI system makes its decisions using the information provided. In line with the principle of transparency, deployers have a responsibility to clearly disclose the implementation of an AI system to stakeholders and foster general awareness of the AI system being used. With the increasing use of AI in many businesses and industries, the public is becoming more aware and interested in knowing when they are interacting with AI systems. Knowing when and how AI systems interact with users is also important in helping users discern the potential harm of interacting with an AI system that is not behaving as intended. In the past, AI algorithms have been found to discriminate against female job applicants and have failed to accurately recognise the faces of dark skinned women. It is important for users to be aware of the expected behaviour of the AI systems so they can make more informed decisions about the potential harm of interacting with AI systems. An example of transparency in an AI enabled ecommerce platform is informing users that their purchase history is used by the platform’s recommendation algorithm to identify similar products and display them on the users’ feeds. In line with the principle of explainability, developers and deployers designing, developing, and deploying AI systems should also strive to foster general understanding among users of how such systems work with simple and easy to understand explanations on how the AI system makes decisions. Understanding how AI systems work will help humans know when to trust its decisions. Explanations can have varying degrees of complexity, ranging from a simple text explanation of which factors more significantly affected the decisionmaking process to displaying a heatmap over the relevant text or on the area of an image that led to the system’s decision. For example, when an AI system is used to predict the likelihood of cardiac arrest in patients, explainability can be implemented by informing medical professionals of the most significant factors (e.g., age, blood pressure, etc.) that influenced the AI system’s decision so that they can subsequently make informed decisions on their own. Where “black box” models are deployed, rendering it difficult, if not impossible to provide explanations as to the workings of the AI system, outcome based explanations, with a focus on explaining the impact of decisionmaking or results flowing from the AI system may be relied on. Alternatively, deployers may also consider focusing on aspects relating to the quality of the AI system or preparing information that could build user confidence in the outcomes of an AI system’s processing behaviour. Some of these measures are: • Documenting the repeatability of results produced by the AI system. Some practices to demonstrate repeatability include conducting repeatability assessments to ensure deployments in live environments are repeatable and performing counterfactual fairness testing to ensure that the AI system’s decisions are the same in both the real world and in the counterfactual world. Repeatability refers to the ability of the system to consistently obtain the same results, given the same scenario. Repeatability often applies within the same environment, with the same data and the same computational conditions. • Ensuring traceability by building an audit trail to document the AI system development and decisionmaking process, implementing a black box recorder that captures all input data streams, or storing data appropriately to avoid degradation and alteration. • Facilitating auditability by keeping a comprehensive record of data provenance, procurement, preprocessing, lineage, storage, and security. Such information can also be centralised digitally in a process log to increase capacity to cater the presentation of results to different tiers of stakeholders with different interests and levels of expertise. Deployers should, however, note that auditability does not necessarily entail making certain confidential information about business models or intellectual property related to the AI system publicly available. A risk based approach can be taken towards identifying the subset of AI enabled features in the AI system for which implemented auditability is necessary to align with regulatory requirements or industry practices. • Using AI Model Cards, which are short documents accompanying trained machine learning models that disclose the context in which models are intended to be used, details of the performance evaluation procedures, and other relevant information. In cases where AI systems are procured directly from developers, deployers will have to work together with these developers to achieve transparency. More on this will be covered in later sections of the Guide.

Published by ASEAN in ASEAN Guide on AI Governance and Ethics, 2021

· 2. RESPONSIBILITY MUST BE FULLY ACKNOWLEDGED WHEN CREATING AND USING AI

2.1. Risk based approach. The degree of attention paid to ethical AI issues and the nature of the relevant actions of AI Actors should be proportional to the assessment of the level of risk posed by specific AI technologies and systems for the interests of individuals and society. Risk level assessment shall take into account both known and possible risks, whereby the probability level of threats, as well as their possible scale in the short and long term shall be considered. Making decisions in the field of AI use that significantly affect society and the state should be accompanied by a scientifically verified, interdisciplinary forecast of socio economic consequences and risks and examination of possible changes in the paradigm of value and cultural development of the society. Development and use of an AI systems risk assessment methodology are encouraged in pursuance of this Code. 2.2. Responsible attitude. AI Actors should responsibly treat: • issues related to the influence of AI systems on society and citizens at every stage of the AI systems’ life cycle, i.a. on privacy, ethical, safe and responsible use of personal data; • the nature, degree and extent of damage that may result from the use of AI technologies and systems; • the selection and use of hardware and software utilized in different life cycles of AI systems. At the same time, the responsibility of AI Actors should correspond with the nature, degree and extent of damage that may occur as a result of the use of AI technologies and systems. The role in the life cycle of the AI system, as well as the degree of possible and real influence of a particular AI Actor on causing damage and its extent, should also be taken into account. 2.3. Precautions. When the activities of AI Actors can lead to morally unacceptable consequences for individuals and society, which can be reasonably predicted by the relevant AI Actor, the latter, should take measures to prohibit or limit the occurrence of such consequences. AI Actors shall use the provisions of this Code, including the mechanisms specified in Section 2, to assess the moral unacceptability of such consequences and discuss possible preventive measures. 2.4. No harm. AI Actors should not allow the use of AI technologies for the purpose of causing harm to human life and or health, the property of citizens and legal entities and the environment. Any use, including the design, development, testing, integration or operation of an AI system capable of purposefully causing harm to the environment, human life and or health, the property of citizens and legal entities, is prohibited. 2.5. Identification of AI in communication with a human. AI Actors are encouraged to ensure that users are duly informed of their interactions with AI systems when it affects human rights and critical areas of people’s lives and to ensure that such interaction can be terminated at the request of the user. 2.6. Data security. AI Actors must comply with the national legislation in the field of personal data and secrets protected by law when using AI systems; ensure the security and protection of personal data processed by AI systems or by AI Actors in order to develop and improve the AI systems; develop and integrate innovative methods to counter unauthorized access to personal data by third parties and use high quality and representative datasets obtained without breaking the law from reliable sources. 2.7. Information security. AI Actors should ensure the maximum possible protection from unauthorized interference of third parties in the operation of AI systems; integrate adequate information security technologies, i.a. use internal mechanisms designed to protect the AI system from unauthorized interventions and inform users and developers about such interventions; as well as promote the informing of users about the rules of information security during the use of AI systems. 2.8. Voluntary certification and Code compliance. AI Actors may implement voluntary certification systems to assess the compliance of developed AI technologies with the standards established by the national legislation and this Code. AI Actors may create voluntary certification and labeling systems for AI systems to indicate that these systems have passed voluntary certification procedures and confirm quality standards. 2.9. Control of the recursive self improvement of AI systems. AI Actors are encouraged to cooperate in identifying and verifying information about ways and forms of design of so called universal ("general") AI systems and prevention of possible threats they carry. The issues concerning the use of "general" AI technologies should be under the control of the state.

Published by AI Alliance Russia in AI Ethics Code (revised version), Oct 21, 2022 (unconfirmed)

Principle 6 – Transparency & Explainability

The transparency and explainability principle is crucial for building and maintaining trust in AI systems and technologies. AI systems must be built with a high level of clarity and explainability as well as features to track the stages of automated decision making, particularly those that may lead to detrimental effects on data subjects. It follows that data, algorithms, capabilities, processes, and purpose of the AI system need to be transparent and communicated as well as explainable to those who are directly and indirectly affected. The degree to which the system is traceable, auditable, transparent, and explainable is dependent on the context and purpose of the AI system and the severity of the outcomes that may result from the technology. AI systems and their designers should be able to justify how the rationale behind their design, practices, processes, algorithms, and decisions or behaviors are ethically permissible, nondiscriminatory, and nonharmful to the public.

Published by SDAIA in AI Ethics Principles, Sept 14, 2022

· Plan and Design:

1 When designing a transparent and trusted AI system, it is vital to ensure that stakeholders affected by AI systems are fully aware and informed of how outcomes are processed. They should further be given access to and an explanation of the rationale for decisions made by the AI technology in an understandable and contextual manner. Decisions should be traceable. AI system owners must define the level of transparency for different stakeholders on the technology based on data privacy, sensitivity, and authorization of the stakeholders. 2 The AI system should be designed to include an information section in the platform to give an overview of the AI model decisions as part of the overall transparency application of the technology. Information sharing as a sub principle should be adhered to with end users and stakeholders of the AI system upon request or open to the public, depending on the nature of the AI system and target market. The model should establish a process mechanism to log and address issues and complaints that arise to be able to resolve them in a transparent and explainable manner. Prepare Input Data: 1 The data sets and the processes that yield the AI system’s decision should be documented to the best possible standard to allow for traceability and an increase in transparency. 2 The data sets should be assessed in the context of their accuracy, suitability, validity, and source. This has a direct effect on the training and implementation of these systems since the criteria for the data’s organization, and structuring must be transparent and explainable in their acquisition and collection adhering to data privacy regulations and intellectual property standards and controls.

Published by SDAIA in AI Ethics Principles, Sept 14, 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