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

(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

· 2. Data Governance

The quality of the data sets used is paramount for the performance of the trained machine learning solutions. Even if the data is handled in a privacy preserving way, there are requirements that have to be fulfilled in order to have high quality AI. The datasets gathered inevitably contain biases, and one has to be able to prune these away before engaging in training. This may also be done in the training itself by requiring a symmetric behaviour over known issues in the training set. In addition, it must be ensured that the proper division of the data which is being set into training, as well as validation and testing of those sets, is carefully conducted in order to achieve a realistic picture of the performance of the AI system. It must particularly be ensured that anonymisation of the data is done in a way that enables the division of the data into sets to make sure that a certain data – for instance, images from same persons – do not end up into both the training and test sets, as this would disqualify the latter. The integrity of the data gathering has to be ensured. Feeding malicious data into the system may change the behaviour of the AI solutions. This is especially important for self learning systems. It is therefore advisable to always keep record of the data that is fed to the AI systems. When data is gathered from human behaviour, it may contain misjudgement, errors and mistakes. In large enough data sets these will be diluted since correct actions usually overrun the errors, yet a trace of thereof remains in the data. To trust the data gathering process, it must be ensured that such data will not be used against the individuals who provided the data. Instead, the findings of bias should be used to look forward and lead to better processes and instructions – improving our decisions making and strengthening our institutions.

Published by The European Commission’s High-Level Expert Group on Artificial Intelligence in Draft Ethics Guidelines for Trustworthy AI, Dec 18, 2018

3. Principle of controllability

Developers should pay attention to the controllability of AI systems. [Comment] In order to assess the risks related to the controllability of AI systems, it is encouraged that developers make efforts to conduct verification and validation in advance. One of the conceivable methods of risk assessment is to conduct experiments in a closed space such as in a laboratory or a sandbox in which security is ensured, at a stage before the practical application in society. In addition, in order to ensure the controllability of AI systems, it is encouraged that developers pay attention to whether the supervision (such as monitoring or warnings) and countermeasures (such as system shutdown, cut off from networks, or repairs) by humans or other trustworthy AI systems are effective, to the extent possible in light of the characteristics of the technologies to be adopted. [Note] Verification and validation are methods for evaluating and controlling risks in advance. Generally, the former is used for confirming formal consistency, while the latter is used for confirming substantial validity. (See, e.g., The Future of Life Institute (FLI), Research Priorities for Robust and Beneficial Artificial Intelligence (2015)). [Note] Examples of what to see in the risk assessment are risks of reward hacking in which AI systems formally achieve the goals assigned but substantially do not meet the developer's intents, and risks that AI systems work in ways that the developers have not intended due to the changes of their outputs and programs in the process of the utilization with their learning, etc. For reward hacking, see, e.g., Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman & Dan Mané, Concrete Problems in AI Safety, arXiv: 1606.06565 [cs.AI] (2016).

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

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