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

· 2) Humanistic approach:

Humanistic approach: Artificial intelligence should empower users to make their own decisions. We are committed to providing transparent, understandable decision interpretations and interactive tools, allowing users to join, monitor or involve in the decision making process.

Principle: Chinese Young Scientists’ Declaration on the Governance and Innovation of Artificial Intelligence, Aug 29, 2019

Published by Youth Work Committee of Shanghai Computer Society

Related Principles

(Preamble)

Automated decision making algorithms are now used throughout industry and government, underpinning many processes from dynamic pricing to employment practices to criminal sentencing. Given that such algorithmically informed decisions have the potential for significant societal impact, the goal of this document is to help developers and product managers design and implement algorithmic systems in publicly accountable ways. Accountability in this context includes an obligation to report, explain, or justify algorithmic decision making as well as mitigate any negative social impacts or potential harms. We begin by outlining five equally important guiding principles that follow from this premise: Algorithms and the data that drive them are designed and created by people There is always a human ultimately responsible for decisions made or informed by an algorithm. "The algorithm did it" is not an acceptable excuse if algorithmic systems make mistakes or have undesired consequences, including from machine learning processes.

Published by Fairness, Accountability, and Transparency in Machine Learning (FAT/ML) in Principles for Accountable Algorithms, Jul 22, 2016 (unconfirmed)

8. Principle of user assistance

Developers should take it into consideration that AI systems will support users and make it possible to give them opportunities for choice in appropriate manners. [Comment] In order to support users of AI systems, it is recommended that developers pay attention to the followings: ● To make efforts to make available interfaces that provide in a timely and appropriate manner the information that can help users’ decisions and are easy to use for them. ● To make efforts to give consideration to make available functions that provide users with opportunities for choice in a timely and appropriate manner (e.g., default settings, easy to understand options, feedbacks, emergency warnings, handling of errors, etc.). And ● To make efforts to take measures to make AI systems easier to use for socially vulnerable people such as universal design. In addition, it is recommended that developers make efforts to provide users with appropriate information considering the possibility of changes in outputs or programs as a result of learning or other methods of AI systems.

Published by Ministry of Internal Affairs and Communications (MIC), the Government of Japan in AI R&D Principles, Jul 28, 2017

1. Transparent and explainable

There must be transparent use and responsible disclosure around data enhanced technology like AI, automated decisions and machine learning systems to ensure that people understand outcomes and can discuss, challenge and improve them. This includes being open about how and why these technologies are being used. When automation has been used to make or assist with decisions, a meaningful explanation should be made available. The explanation should be meaningful to the person requesting it. It should include relevant information about what the decision was, how the decision was made, and the consequences. Why it matters Transparent use is the key principle that helps enable other principles while building trust and confidence in government use of data enhanced technologies. It also encourages a dialogue between those using the technology and those who are affected by it. Meaningful explanations are important because they help people understand and potentially challenge outcomes. This helps ensure decisions are rendered fairly. It also helps identify and reverse adverse impacts on historically disadvantaged groups. For more on this, please consult the Transparency Guidelines.

Published by Government of Ontario, Canada in Principles for Ethical Use of AI [Beta], Sept 14, 2023

Transparency and explainability

United Nations system organizations should ensure transparency and explainability of AI systems that they use at all stages of their lifecycle and of decision making processes involving AI systems. Technical explainability requires that the decisions made by an AI system can be understood and traced by human beings. Individuals should be meaningfully informed when a decision which may or will impact their rights, fundamental freedoms, entitlements, services or benefits, is informed by or made based on AI algorithms and have access to the reasons for a decision and the logic involved. The information and reasons for a decision should be presented in a manner that is understandable to them.

Published by United Nations System Chief Executives Board for Coordination in Principles for the Ethical Use of Artificial Intelligence in the United Nations System, Sept 20, 2022

1 Protect autonomy

Adoption of AI can lead to situations in which decision making could be or is in fact transferred to machines. The principle of autonomy requires that any extension of machine autonomy not undermine human autonomy. In the context of health care, this means that humans should remain in full control of health care systems and medical decisions. AI systems should be designed demonstrably and systematically to conform to the principles and human rights with which they cohere; more specifically, they should be designed to assist humans, whether they be medical providers or patients, in making informed decisions. Human oversight may depend on the risks associated with an AI system but should always be meaningful and should thus include effective, transparent monitoring of human values and moral considerations. In practice, this could include deciding whether to use an AI system for a particular health care decision, to vary the level of human discretion and decision making and to develop AI technologies that can rank decisions when appropriate (as opposed to a single decision). These practicescan ensure a clinician can override decisions made by AI systems and that machine autonomy can be restricted and made “intrinsically reversible”. Respect for autonomy also entails the related duties to protect privacy and confidentiality and to ensure informed, valid consent by adopting appropriate legal frameworks for data protection. These should be fully supported and enforced by governments and respected by companies and their system designers, programmers, database creators and others. AI technologies should not be used for experimentation or manipulation of humans in a health care system without valid informed consent. The use of machine learning algorithms in diagnosis, prognosis and treatment plans should be incorporated into the process for informed and valid consent. Essential services should not be circumscribed or denied if an individual withholds consent and that additional incentives or inducements should not be offered by either a government or private parties to individuals who do provide consent. Data protection laws are one means of safeguarding individual rights and place obligations on data controllers and data processors. Such laws are necessary to protect privacy and the confidentiality of patient data and to establish patients’ control over their data. Construed broadly, data protection laws should also make it easy for people to access their own health data and to move or share those data as they like. Because machine learning requires large amounts of data – big data – these laws are increasingly important.

Published by World Health Organization (WHO) in Key ethical principles for use of artificial intelligence for health, Jun 28, 2021