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

5. Principle 5 — A IS Technology Misuse and Awareness of It

Issue: How can we extend the benefits and minimize the risks of A IS technology being misused? [Candidate Recommendations] Raise public awareness around the issues of potential A IS technology misuse in an informed and measured way by: 1. Providing ethics education and security awareness that sensitizes society to the potential risks of misuse of A IS (e.g., by providing “data privacy” warnings that some smart devices will collect their user’s personal data). 2. Delivering this education in scalable and effective ways, beginning with those having the greatest credibility and impact that also minimize generalized (e.g., non productive) fear about A IS (e.g., via credible research institutions or think tanks via social media such as Facebook or YouTube). 3. Educating government, lawmakers, and enforcement agencies surrounding these issues so citizens work collaboratively with them to avoid fear or confusion (e.g., in the same way police officers have given public safety lectures in schools for years; in the near future they could provide workshops on safe A IS).

Principle: Ethically Aligned Design (v2): General Principles, (v1) Dec 13, 2016. (v2) Dec 12, 2017

Published by The IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems

Related Principles

3. Artificial intelligence should not be used to diminish the data rights or privacy of individuals, families or communities.

Many of the hopes and the fears presently associated with AI are out of step with reality. The public and policymakers alike have a responsibility to understand the capabilities and limitations of this technology as it becomes an increasing part of our daily lives. This will require an awareness of when and where this technology is being deployed. Access to large quantities of data is one of the factors fuelling the current AI boom. The ways in which data is gathered and accessed need to be reconsidered, so that innovative companies, big and small, have fair and reasonable access to data, while citizens and consumers can also protect their privacy and personal agency in this changing world. Large companies which have control over vast quantities of data must be prevented from becoming overly powerful within this landscape. We call on the Government, with the Competition and Markets Authority, to review proactively the use and potential monopolisation of data by big technology companies operating in the UK.

Published by House of Lords of United Kingdom, Select Committee on Artificial Intelligence in AI Code, Apr 16, 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

8 PRUDENCE PRINCIPLE

Every person involved in AI development must exercise caution by anticipating, as far as possible, the adverse consequences of AIS use and by taking the appropriate measures to avoid them. 1) It is necessary to develop mechanisms that consider the potential for the double use — beneﬁcial and harmful —of AI research and AIS development (whether public or private) in order to limit harmful uses. 2) When the misuse of an AIS endangers public health or safety and has a high probability of occurrence, it is prudent to restrict open access and public dissemination to its algorithm. 3) Before being placed on the market and whether they are offered for charge or for free, AIS must meet strict reliability, security, and integrity requirements and be subjected to tests that do not put people’s lives in danger, harm their quality of life, or negatively impact their reputation or psychological integrity. These tests must be open to the relevant public authorities and stakeholders. 4) The development of AIS must preempt the risks of user data misuse and protect the integrity and conﬁdentiality of personal data. 5) The errors and ﬂaws discovered in AIS and SAAD should be publicly shared, on a global scale, by public institutions and businesses in sectors that pose a signiﬁcant danger to personal integrity and social organization.

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

· 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

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