5. Data Provenance

A description of the way in which the training data was collected should be maintained by the builders of the algorithms, accompanied by an exploration of the potential biases induced by the human or algorithmic data gathering process. Public scrutiny of the data provides maximum opportunity for corrections. However, concerns over privacy, protecting trade secrets, or revelation of analytics that might allow malicious actors to game the system can justify restricting access to qualified and authorized individuals.
Principle: Principles for Algorithmic Transparency and Accountability, Jan 12, 2017

Published by ACM US Public Policy Council (USACM)

Related Principles

· 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

· 7. Respect for Privacy

Privacy and data protection must be guaranteed at all stages of the life cycle of the AI system. This includes all data provided by the user, but also all information generated about the user over the course of his or her interactions with the AI system (e.g. outputs that the AI system generated for specific users, how users responded to particular recommendations, etc.). Digital records of human behaviour can reveal highly sensitive data, not only in terms of preferences, but also regarding sexual orientation, age, gender, religious and political views. The person in control of such information could use this to his her advantage. Organisations must be mindful of how data is used and might impact users, and ensure full compliance with the GDPR as well as other applicable regulation dealing with privacy and data protection.

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

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

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


2.1. Risk based approach. The level of attention to ethical issues in AI and the nature of the relevant actions of AI Actors should be proportional to the assessment of the level of risk posed by specific technologies and AISs and the interests of individuals and society. Risk level assessment must take into account both the known and possible risks; in this case, the level of probability of threats should be taken into account as well as their possible scale in the short and long term. In the field of AI development, making decisions that are significant to society and the state should be accompanied by scientifically verified and interdisciplinary forecasting of socio economic consequences and risks, as well as by the examination of possible changes in the value and cultural paradigm of the development of society, while taking into account national priorities. In pursuance of this Code, the development and use of an AIS risk assessment methodology is recommended. 2.2. Responsible attitude. AI Actors should have a responsible approach to the aspects of AIS that influence society and citizens at every stage of the AIS life cycle. These include privacy; the ethical, safe and responsible use of personal data; the nature, degree and amount of damage that may follow as a result of the use of the technology and AIS; and the selection and use of companion hardware and software. In this case, the responsibility of the AI Actors must correspond to the nature, degree and amount of damage that may occur as a result of the use of technologies and AIS, while taking into account the role of the AI Actor in the life cycle of AIS, as well as the degree of possible and real impact of a particular AI Actor on causing damage, as well as its size. 2.3. Precautions. When the activities of AI Actors can lead to morally unacceptable consequences for individuals and society, the occurrence of which the corresponding AI Actor can reasonably assume, measures should be taken to prevent or limit the occurrence of such consequences. To assess the moral acceptability of consequences and the possible measures to prevent them, Actors can use the provisions of this Code, including the mechanisms specified in Section 2. 2.4. No harm. AI Actors should not allow use of AI technologies for the purpose of causing harm to human life, the environment and or the health or property of citizens and legal entities. Any application of an AIS capable of purposefully causing harm to the environment, human life or health or the property of citizens and legal entities during any stage, including design, development, testing, implementation or operation, is unacceptable. 2.5. Identification of AI in communication with a human. AI Actors are encouraged to ensure that users are informed of their interactions with the AIS when it affects their rights and critical areas of their lives and to ensure that such interactions can be terminated at the request of the user. 2.6. Data security AI Actors must comply with the legislation of the Russian Federation in the field of personal data and secrets protected by law when using an AIS. Furthermore, they must ensure the protection and protection of personal data processed by an AIS or AI Actors in order to develop and improve the AIS by developing and implementing innovative methods of controlling unauthorized access by third parties to personal data and using high quality and representative datasets from reliable sources and obtained without breaking the law. 2.7. Information security. AI Actors should provide the maximum possible protection against unauthorized interference in the work of the AI by third parties by introducing adequate information security technologies, including the use of internal mechanisms for protecting the AIS from unauthorized interventions and informing users and developers about such interventions. They must also inform users about the rules regarding information security when using the AIS. 2.8. Voluntary certification and Code compliance. AI Actors can implement voluntary certification for the compliance of the developed AI technologies with the standards established by the legislation of the Russian Federation and this Code. AI Actors can create voluntary certification and AIS labeling systems that indicate that these systems have passed voluntary certification procedures and confirm quality standards. 2.9. Control of the recursive self improvement of AISs. AI Actors are encouraged to collaborate in the identification and verification of methods and forms of creating universal ("strong") AIS and the prevention of the possible threats that AIS carry. The use of "strong" AI technologies should be under the control of the state.

Published by AI Alliance Russia in Artificial Intelligence Code of Ethics, Oct 26, 2021