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

2. Right to Human Determination.

All individuals have the right to a final determination made by a person. [Explanatory Memorandum] The Right to a Human Determination reaffirms that individuals and not machines are responsible for automated decision making. In many instances, such as the operation of an autonomous vehicle, it would not be possible or practical to insert a human decision prior to an automated decision. But the aim remains to ensure accountability. Thus where an automated system fails, this principle should be understood as a requirement that a human assessment of the outcome be made.

Principle: Universal Guidelines for Artificial Intelligence, Oct 23, 2018

Published by The Public Voice coalition, established by Electronic Privacy Information Center (EPIC)

Related Principles

(a) Human dignity

The principle of human dignity, understood as the recognition of the inherent human state of being worthy of respect, must not be violated by ‘autonomous’ technologies. This means, for instance, that there are limits to determinations and classifications concerning persons, made on the basis of algorithms and ‘autonomous’ systems, especially when those affected by them are not informed about them. It also implies that there have to be (legal) limits to the ways in which people can be led to believe that they are dealing with human beings while in fact they are dealing with algorithms and smart machines. A relational conception of human dignity which is characterised by our social relations, requires that we are aware of whether and when we are interacting with a machine or another human being, and that we reserve the right to vest certain tasks to the human or the machine.

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

9 RESPONSIBILITY PRINCIPLE

The development and use of AIS must not contribute to lessen the responsibility of human beings when decisions must be made. 1) Only human beings can be held responsible for decisions stemming from recommendations made by AIS, and the actions that proceed therefrom. 2) In all areas where a decision that affects a person’s life, quality of life, or reputation must be made, where time and circumstance permit, the ﬁnal decision must be taken by a human being and that decision should be free and informed 3) The decision to kill must always be made by human beings, and responsibility for this decision must not be transferred to an AIS. 4) People who authorize AIS to commit a crime or an offence, or demonstrate negligence by allowing AIS to commit them, are responsible for this crime or offence. 5) When damage or harm has been inﬂicted by an AIS, and the AIS is proven to be reliable and to have been used as intended, it is not reasonable to place blame on the people involved in its development or use.

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

1. Right to Transparency.

All individuals have the right to know the basis of an AI decision that concerns them. This includes access to the factors, the logic, and techniques that produced the outcome. [Explanatory Memorandum] The elements of the Transparency Principle can be found in several modern privacy laws, including the US Privacy Act, the EU Data Protection Directive, the GDPR, and the Council of Europe Convention 108. The aim of this principle is to enable independent accountability for automated decisions, with a primary emphasis on the right of the individual to know the basis of an adverse determination. In practical terms, it may not be possible for an individual to interpret the basis of a particular decision, but this does not obviate the need to ensure that such an explanation is possible.

Published by The Public Voice coalition, established by Electronic Privacy Information Center (EPIC) in Universal Guidelines for Artificial Intelligence, Oct 23, 2018

1. Demand That AI Systems Are Transparent

A transparent artificial intelligence system is one in which it is possible to discover how, and why, the system made a decision, or in the case of a robot, acted the way it did. In particular: A. We stress that open source code is neither necessary nor sufficient for transparency – clarity cannot be obfuscated by complexity. B. For users, transparency is important because it builds trust in, and understanding of, the system, by providing a simple way for the user to understand what the system is doing and why. C. For validation and certification of an AI system, transparency is important because it exposes the system’s processes for scrutiny. D. If accidents occur, the AI will need to be transparent and accountable to an accident investigator, so the internal process that led to the accident can be understood. E. Workers must have the right to demand transparency in the decisions and outcomes of AI systems as well as the underlying algorithms (see principle 4 below). This includes the right to appeal decisions made by AI algorithms, and having it reviewed by a human being. F. Workers must be consulted on AI systems’ implementation, development and deployment. G. Following an accident, judges, juries, lawyers, and expert witnesses involved in the trial process require transparency and accountability to inform evidence and decision making. The principle of transparency is a prerequisite for ascertaining that the remaining principles are observed. See Principle 2 below for operational solution.

Published by UNI Global Union in Top 10 Principles For Ethical Artificial Intelligence, Dec 11, 2017

4 Foster responsibility and accountability

Humans require clear, transparent specification of the tasks that systems can perform and the conditions under which they can achieve the desired level of performance; this helps to ensure that health care providers can use an AI technology responsibly. Although AI technologies perform specific tasks, it is the responsibility of human stakeholders to ensure that they can perform those tasks and that they are used under appropriate conditions. Responsibility can be assured by application of “human warranty”, which implies evaluation by patients and clinicians in the development and deployment of AI technologies. In human warranty, regulatory principles are applied upstream and downstream of the algorithm by establishing points of human supervision. The critical points of supervision are identified by discussions among professionals, patients and designers. The goal is to ensure that the algorithm remains on a machine learning development path that is medically effective, can be interrogated and is ethically responsible; it involves active partnership with patients and the public, such as meaningful public consultation and debate (101). Ultimately, such work should be validated by regulatory agencies or other supervisory authorities. When something does go wrong in application of an AI technology, there should be accountability. Appropriate mechanisms should be adopted to ensure questioning by and redress for individuals and groups adversely affected by algorithmically informed decisions. This should include access to prompt, effective remedies and redress from governments and companies that deploy AI technologies for health care. Redress should include compensation, rehabilitation, restitution, sanctions where necessary and a guarantee of non repetition. The use of AI technologies in medicine requires attribution of responsibility within complex systems in which responsibility is distributed among numerous agents. When medical decisions by AI technologies harm individuals, responsibility and accountability processes should clearly identify the relative roles of manufacturers and clinical users in the harm. This is an evolving challenge and remains unsettled in the laws of most countries. Institutions have not only legal liability but also a duty to assume responsibility for decisions made by the algorithms they use, even if it is not feasible to explain in detail how the algorithms produce their results. To avoid diffusion of responsibility, in which “everybody’s problem becomes nobody’s responsibility”, a faultless responsibility model (“collective responsibility”), in which all the agents involved in the development and deployment of an AI technology are held responsible, can encourage all actors to act with integrity and minimize harm. In such a model, the actual intentions of each agent (or actor) or their ability to control an outcome are not considered.

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