5. Knowledge

[QUESTIONS] Does the development of AI put critical thinking at risk? How do we minimize the dissemination of fake news or misleading information? Should research results on AI, whether positive or negative, be made available and accessible? Is it acceptable not to be informed that medical or legal advice has been given by a chatbot? How transparent should the internal decision making processes of algorithms be? [PRINCIPLES] ​The development of AI should promote critical thinking and protect us from propaganda and manipulation.
Principle: The Montreal Declaration for a Responsible Development of Artificial Intelligence, Nov 3, 2017

Published by University of Montreal, Forum on the Socially Responsible Development of AI

Related Principles

· Transparency

As AI increasingly changes the nature of work, workers, customers and vendors need to have information about how AI systems operate so that they can understand how decisions are made. Their involvement will help to identify potential bias, errors and unintended outcomes. Transparency is not necessarily nor only a question of open source code. While in some circumstances open source code will be helpful, what is more important are clear, complete and testable explanations of what the system is doing and why. Intellectual property, and sometimes even cyber security, is rewarded by a lack of transparency. Innovation generally, including in algorithms, is a value that should be encouraged. How, then, are these competing values to be balanced? One possibility is to require algorithmic verifiability rather than full algorithmic disclosure. Algorithmic verifiability would require companies to disclose not the actual code driving the algorithm but information allowing the effect of their algorithms to be independently assessed. In the absence of transparency regarding their algorithms’ purpose and actual effect, it is impossible to ensure that competition, labour, workplace safety, privacy and liability laws are being upheld. When accidents occur, the AI and related data will need to be transparent and accountable to an accident investigator, so that the process that led to the accident can be understood.

Published by Centre for International Governance Innovation (CIGI), Canada in Toward a G20 Framework for Artificial Intelligence in the Workplace, Jul 19, 2018

Public Empowerment

Principle: The public’s ability to understand AI enabled services, and how they work, is key to ensuring trust in the technology. Recommendations: “Algorithmic Literacy” must be a basic skill: Whether it is the curating of information in social media platforms or self driving cars, users need to be aware and have a basic understanding of the role of algorithms and autonomous decision making. Such skills will also be important in shaping societal norms around the use of the technology. For example, identifying decisions that may not be suitable to delegate to an AI. Provide the public with information: While full transparency around a service’s machine learning techniques and training data is generally not advisable due to the security risk, the public should be provided with enough information to make it possible for people to question its outcomes.

Published by Internet Society, "Artificial Intelligence and Machine Learning: Policy Paper" in Guiding Principles and Recommendations, Apr 18, 2017

5 DEMOCRATIC PARTICIPATION PRINCIPLE

AIS must meet intelligibility, justifiability, and accessibility criteria, and must be subjected to democratic scrutiny, debate, and control. 1) AIS processes that make decisions affecting a person’s life, quality of life, or reputation must be intelligible to their creators. 2) The decisions made by AIS affecting a person’s life, quality of life, or reputation should always be justifiable in a language that is understood by the people who use them or who are subjected to the consequences of their use. Justification consists in making transparent the most important factors and parameters shaping the decision, and should take the same form as the justification we would demand of a human making the same kind of decision. 3) The code for algorithms, whether public or private, must always be accessible to the relevant public authorities and stakeholders for verification and control purposes. 4) The discovery of AIS operating errors, unexpected or undesirable effects, security breaches, and data leaks must imperatively be reported to the relevant public authorities, stakeholders, and those affected by the situation. 5) In accordance with the transparency requirement for public decisions, the code for decision making algorithms used by public authorities must be accessible to all, with the exception of algorithms that present a high risk of serious danger if misused. 6) For public AIS that have a significant impact on the life of citizens, citizens should have the opportunity and skills to deliberate on the social parameters of these AIS, their objectives, and the limits of their use. 7) We must at all times be able to verify that AIS are doing what they were programmed for and what they are used for. 8) Any person using a service should know if a decision concerning them or affecting them was made by an AIS. 9) Any user of a service employing chatbots should be able to easily identify whether they are interacting with an AIS or a real person. 10) Artificial intelligence research should remain open and accessible to all.

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

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

3 Ensure transparency, explainability and intelligibility

AI should be intelligible or understandable to developers, users and regulators. Two broad approaches to ensuring intelligibility are improving the transparency and explainability of AI technology. Transparency requires that sufficient information (described below) be published or documented before the design and deployment of an AI technology. Such information should facilitate meaningful public consultation and debate on how the AI technology is designed and how it should be used. Such information should continue to be published and documented regularly and in a timely manner after an AI technology is approved for use. Transparency will improve system quality and protect patient and public health safety. For instance, system evaluators require transparency in order to identify errors, and government regulators rely on transparency to conduct proper, effective oversight. It must be possible to audit an AI technology, including if something goes wrong. Transparency should include accurate information about the assumptions and limitations of the technology, operating protocols, the properties of the data (including methods of data collection, processing and labelling) and development of the algorithmic model. AI technologies should be explainable to the extent possible and according to the capacity of those to whom the explanation is directed. Data protection laws already create specific obligations of explainability for automated decision making. Those who might request or require an explanation should be well informed, and the educational information must be tailored to each population, including, for example, marginalized populations. Many AI technologies are complex, and the complexity might frustrate both the explainer and the person receiving the explanation. There is a possible trade off between full explainability of an algorithm (at the cost of accuracy) and improved accuracy (at the cost of explainability). All algorithms should be tested rigorously in the settings in which the technology will be used in order to ensure that it meets standards of safety and efficacy. The examination and validation should include the assumptions, operational protocols, data properties and output decisions of the AI technology. Tests and evaluations should be regular, transparent and of sufficient breadth to cover differences in the performance of the algorithm according to race, ethnicity, gender, age and other relevant human characteristics. There should be robust, independent oversight of such tests and evaluation to ensure that they are conducted safely and effectively. Health care institutions, health systems and public health agencies should regularly publish information about how decisions have been made for adoption of an AI technology and how the technology will be evaluated periodically, its uses, its known limitations and the role of decision making, which can facilitate external auditing and oversight.

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