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

1. Artificial intelligence and machine learning technologies should be designed, developed and used in respect of fundamental human rights and in accordance with the fairness principle, in particular by:

a. Considering individuals’ reasonable expectations by ensuring that the use of artificial intelligence systems remains consistent with their original purposes, and that the data are used in a way that is not incompatible with the original purpose of their collection, b. taking into consideration not only the impact that the use of artificial intelligence may have on the individual, but also the collective impact on groups and on society at large, c. ensuring that artificial intelligence systems are developed in a way that facilitates human development and does not obstruct or endanger it, thus recognizing the need for delineation and boundaries on certain uses,

Principle: Declaration On Ethics And Data Protection In Artifical Intelligence, Oct 23, 2018

Published by 40th International Conference of Data Protection and Privacy Commissioners (ICDPPC)

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, 2024

(h) Data protection and privacy

In an age of ubiquitous and massive collection of data through digital communication technologies, the right to protection of personal information and the right to respect for privacy are crucially challenged. Both physical AI robots as part of the Internet of Things, as well as AI softbots that operate via the World Wide Web must comply with data protection regulations and not collect and spread data or be run on sets of data for whose use and dissemination no informed consent has been given. ‘Autonomous’ systems must not interfere with the right to private life which comprises the right to be free from technologies that influence personal development and opinions, the right to establish and develop relationships with other human beings, and the right to be free from surveillance. Also in this regard, exact criteria should be defined and mechanisms established that ensure ethical development and ethically correct application of ‘autonomous’ systems. In light of concerns with regard to the implications of ‘autonomous’ systems on private life and privacy, consideration may be given to the ongoing debate about the introduction of two new rights: the right to meaningful human contact and the right to not be profiled, measured, analysed, coached or nudged.

Published by European Group on Ethics in Science and New Technologies, European Commission in Ethical principles and democratic prerequisites, Mar 9, 2018

PREAMBLE

For the first time in human history, it is possible to create autonomous systems capable of performing complex tasks of which natural intelligence alone was thought capable: processing large quantities of information, calculating and predicting, learning and adapting responses to changing situations, and recognizing and classifying objects. Given the immaterial nature of these tasks, and by analogy with human intelligence, we designate these wide ranging systems under the general name of artificial intelligence. Artificial intelligence constitutes a major form of scientific and technological progress, which can generate considerable social benefits by improving living conditions and health, facilitating justice, creating wealth, bolstering public safety, and mitigating the impact of human activities on the environment and the climate. Intelligent machines are not limited to performing better calculations than human beings; they can also interact with sentient beings, keep them company and take care of them. However, the development of artificial intelligence does pose major ethical challenges and social risks. Indeed, intelligent machines can restrict the choices of individuals and groups, lower living standards, disrupt the organization of labor and the job market, influence politics, clash with fundamental rights, exacerbate social and economic inequalities, and affect ecosystems, the climate and the environment. Although scientific progress, and living in a society, always carry a risk, it is up to the citizens to determine the moral and political ends that give meaning to the risks encountered in an uncertain world. The lower the risks of its deployment, the greater the benefits of artificial intelligence will be. The first danger of artificial intelligence development consists in giving the illusion that we can master the future through calculations. Reducing society to a series of numbers and ruling it through algorithmic procedures is an old pipe dream that still drives human ambitions. But when it comes to human affairs, tomorrow rarely resembles today, and numbers cannot determine what has moral value, nor what is socially desirable. The principles of the current declaration are like points on a moral compass that will help guide the development of artificial intelligence towards morally and socially desirable ends. They also offer an ethical framework that promotes internationally recognized human rights in the fields affected by the rollout of artificial intelligence. Taken as a whole, the principles articulated lay the foundation for cultivating social trust towards artificially intelligent systems. The principles of the current declaration rest on the common belief that human beings seek to grow as social beings endowed with sensations, thoughts and feelings, and strive to fulfill their potential by freely exercising their emotional, moral and intellectual capacities. It is incumbent on the various public and private stakeholders and policymakers at the local, national and international level to ensure that the development and deployment of artificial intelligence are compatible with the protection of fundamental human capacities and goals, and contribute toward their fuller realization. With this goal in mind, one must interpret the proposed principles in a coherent manner, while taking into account the specific social, cultural, political and legal contexts of their application.

Published by University of Montreal in The Montreal Declaration for a Responsible Development of Artificial Intelligence, Dec 4, 2018

6. Human Centricity and Well being

a. To aim for an equitable distribution of the benefits of data practices and avoid data practices that disproportionately disadvantage vulnerable groups. b. To aim to create the greatest possible benefit from the use of data and advanced modelling techniques. c. Engage in data practices that encourage the practice of virtues that contribute to human flourishing, human dignity and human autonomy. d. To give weight to the considered judgements of people or communities affected by data practices and to be aligned with the values and ethical principles of the people or communities affected. e. To make decisions that should cause no foreseeable harm to the individual, or should at least minimise such harm (in necessary circumstances, when weighed against the greater good). f. To allow users to maintain control over the data being used, the context such data is being used in and the ability to modify that use and context. g. To ensure that the overall well being of the user should be central to the AI system’s functionality.

Published by Personal Data Protection Commission (PDPC), Singapore in A compilation of existing AI ethical principles (Annex A), Jan 21, 2020

· 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