10. Prohibition on Secret Profiling.

No institution shall establish or maintain a secret profiling system. [Explanatory Memorandum] The Prohibition on Secret Profiling follows from the earlier Identification Obligation. The aim is to avoid the information asymmetry that arises increasingly with AI systems and to ensure the possibility of independent accountability.
Principle: Universal Guidelines for Artificial Intelligence, Oct 23, 2018

Published by The Public Voice coalition, established by Electronic Privacy Information Center (EPIC)

Related Principles

Transparency and explainability

There should be transparency and responsible disclosure to ensure people know when they are being significantly impacted by an AI system, and can find out when an AI system is engaging with them. This principle aims to ensure responsible disclosure when an AI system is significantly impacting on a person’s life. The definition of the threshold for ‘significant impact’ will depend on the context, impact and application of the AI system in question. Achieving transparency in AI systems through responsible disclosure is important to each stakeholder group for the following reasons for users, what the system is doing and why for creators, including those undertaking the validation and certification of AI, the systems’ processes and input data for those deploying and operating the system, to understand processes and input data for an accident investigator, if accidents occur for regulators in the context of investigations for those in the legal process, to inform evidence and decision‐making for the public, to build confidence in the technology Responsible disclosures should be provided in a timely manner, and provide reasonable justifications for AI systems outcomes. This includes information that helps people understand outcomes, like key factors used in decision making. This principle also aims to ensure people have the ability to find out when an AI system is engaging with them (regardless of the level of impact), and are able to obtain a reasonable disclosure regarding the AI system.

Published by Department of Industry, Innovation and Science, Australian Government in AI Ethics Principles, Nov 7, 2019

· 5. The Principle of Explicability: “Operate transparently”

Transparency is key to building and maintaining citizen’s trust in the developers of AI systems and AI systems themselves. Both technological and business model transparency matter from an ethical standpoint. Technological transparency implies that AI systems be auditable, comprehensible and intelligible by human beings at varying levels of comprehension and expertise. Business model transparency means that human beings are knowingly informed of the intention of developers and technology implementers of AI systems. Explicability is a precondition for achieving informed consent from individuals interacting with AI systems and in order to ensure that the principle of explicability and non maleficence are achieved the requirement of informed consent should be sought. Explicability also requires accountability measures be put in place. Individuals and groups may request evidence of the baseline parameters and instructions given as inputs for AI decision making (the discovery or prediction sought by an AI system or the factors involved in the discovery or prediction made) by the organisations and developers of an AI system, the technology implementers, or another party in the supply chain.

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

5. Principle of security

Developers should pay attention to the security of AI systems. [Comment] In addition to respecting international guidelines on security such as “OECD Guidelines for the Security of Information Systems and Networks,” it is encouraged that developers pay attention to the followings, with consideration of the possibility that AI systems might change their outputs or programs as a result of learning or other methods: ● To pay attention, as necessary, to the reliability (that is, whether the operations are performed as intended and not steered by unauthorized third parties) and robustness (that is, tolerance to physical attacks and accidents) of AI systems, in addition to: (a) confidentiality; (b) integrity; and (c) availability of information that are usually required for ensuring the information security of AI systems. ● To make efforts to conduct verification and validation in advance in order to assess and control the risks related to the security of AI systems. ● To make efforts to take measures to maintain the security to the extent possible in light of the characteristics of the technologies to be adopted throughout the process of the development of AI systems (“security by design”).

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

1. Right to Transparency.

All individuals have the right to know the basis of an AI decision that concerns them. This includes access to the factors, the logic, and techniques that produced the outcome. [Explanatory Memorandum] The elements of the Transparency Principle can be found in several modern privacy laws, including the US Privacy Act, the EU Data Protection Directive, the GDPR, and the Council of Europe Convention 108. The aim of this principle is to enable independent accountability for automated decisions, with a primary emphasis on the right of the individual to know the basis of an adverse determination. In practical terms, it may not be possible for an individual to interpret the basis of a particular decision, but this does not obviate the need to ensure that such an explanation is possible.

Published by The Public Voice coalition, established by Electronic Privacy Information Center (EPIC) in Universal Guidelines for Artificial Intelligence, Oct 23, 2018

3. Identification Obligation.

The institution responsible for an AI system must be made known to the public. [Explanatory Memorandum] Identification Obligation. This principle seeks to address the identification asymmetry that arises in the interaction between individuals and AI systems. An AI system typically knows a great deal about an individual; the individual may not even know the operator of the AI system. The Identification Obligation establishes the foundation of AI accountability which is to make clear the identity of an AI system and the institution responsible.

Published by The Public Voice coalition, established by Electronic Privacy Information Center (EPIC) in Universal Guidelines for Artificial Intelligence, Oct 23, 2018