(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.
2. Data Governance
Published by: The European Commission’s High-Level Expert Group on Artificial Intelligence in Draft Ethics Guidelines for Trustworthy AI
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.
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?
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).
9. Principle of transparency
Published by: Ministry of Internal Affairs and Communications (MIC), the Government of Japan in Draft AI Utilization Principles
AI service providers and business users should pay attention to the verifiability of inputs outputs of AI systems or AI services and the explainability of their judgments.
Note: This principle is not intended to ask for the disclosure of algorithm, source code, or learning data. In interpreting this principle, privacy of individuals and trade secrets of enterprises are also taken into account.
[Main points to discuss]
A) Recording and preserving the inputs outputs of AI
In order to ensure the verifiability of the input and output of AI, AI service providers and business users may be expected to record and preserve the inputs and outputs.
In light of the characteristics of the technologies to be used and their usage, in what cases and to what extent are the inputs and outputs expected to be recorded and preserved? For example, in the case of using AI in fields where AI systems might harm the life, body, or property, such as the field of autonomous driving, the inputs and outputs of AI may be expected to be recorded and preserved to the extent whch is necessary for investigating the causes of accidents and preventing the recurrence of such accidents.
B) Ensuring explainability
AI service providers and business users may be expected to ensure explainability on the judgments of AI. In light of the characteristics of the technologies to be used and their usage, in what cases and to what extent is explainability expected to be ensured? Especially in the case of using AI in fields where the judgments of AI might have significant influences on individual rights and interests, such as the fields of medical care, personnel evaluation and recruitment and financing, explainability on the judgments of AI may be expected to be ensured. (For example, we have to pay attention to the current situation where deep learning has high prediction accuracy, but it is difficult to explain its judgment.)
2. Transparent and explainable AI
We will be explicit about the kind of personal and or non personal data the AI systems uses as well as about the purpose the data is used for. When people directly interact with an AI system, we will be transparent to the users that this is the case.
When AI systems take, or support, decisions we take the technical and organizational measures required to guarantee a level of understanding adequate to the application area. In any case, if the decisions significantly affect people's lives, we will ensure we understand the logic behind the conclusions. This will also apply when we use third party technology.