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

3.1 Explainability and verifiability

One of the basic characteristics of human consciousness is that it perceives the environment, seeks answers to questions. i.e. explanations of why and how something is or is not. That trait influenced the evolution of man and the development of science, and therefore artificial intelligence. Man's need to understand and make things clear to him found its foothold in this principle. Clarity in the context of these Guidelines means that all processes: development, testing, commissioning, system monitoring and shutdown must be transparent. The purpose and capabilities of the artificial intelligence system itself must be explainable, especially the decisions (recommendations) which it brings (to the extent that it is expedient) to all who are affected by the System (directly or indirectly). lf certain results of the System's work cannot be explained, it is necessary to mark them as a system with a "black box" model. Verifiability is a complementary element of this principle, which ensures that the System can check in all processes, ie. during the entire life cycle. Verifiability includes the actions and procedures of checking artificial intelligence systems during testing and implementation, as well as checking the short term and long term impact that such a system has on humans.

Principle: ETHICAL GUIDELINES FOR THE DEVELOPMENT, APPLICATION AND USE OF RELIABLE AND RESPONSIBLE ARTIFICIAL INTELLIGENCE, Febrary, 2023

Published by Republic of Serbia

Related Principles

1. Transparency and Explainability

Transparency refers to providing disclosure on when an AI system is being used and the involvement of an AI system in decision making, what kind of data it uses, and its purpose. By disclosing to individuals that AI is used in the system, individuals will become aware and can make an informed choice of whether to use the AIenabled system. Explainability is the ability to communicate the reasoning behind an AI system’s decision in a way that is understandable to a range of people, as it is not always clear how an AI system has arrived at a conclusion. This allows individuals to know the factors contributing to the AI system’s recommendation. In order to build public trust in AI, it is important to ensure that users are aware of the use of AI technology and understand how information from their interaction is used and how the AI system makes its decisions using the information provided. In line with the principle of transparency, deployers have a responsibility to clearly disclose the implementation of an AI system to stakeholders and foster general awareness of the AI system being used. With the increasing use of AI in many businesses and industries, the public is becoming more aware and interested in knowing when they are interacting with AI systems. Knowing when and how AI systems interact with users is also important in helping users discern the potential harm of interacting with an AI system that is not behaving as intended. In the past, AI algorithms have been found to discriminate against female job applicants and have failed to accurately recognise the faces of dark skinned women. It is important for users to be aware of the expected behaviour of the AI systems so they can make more informed decisions about the potential harm of interacting with AI systems. An example of transparency in an AI enabled ecommerce platform is informing users that their purchase history is used by the platform’s recommendation algorithm to identify similar products and display them on the users’ feeds. In line with the principle of explainability, developers and deployers designing, developing, and deploying AI systems should also strive to foster general understanding among users of how such systems work with simple and easy to understand explanations on how the AI system makes decisions. Understanding how AI systems work will help humans know when to trust its decisions. Explanations can have varying degrees of complexity, ranging from a simple text explanation of which factors more significantly affected the decisionmaking process to displaying a heatmap over the relevant text or on the area of an image that led to the system’s decision. For example, when an AI system is used to predict the likelihood of cardiac arrest in patients, explainability can be implemented by informing medical professionals of the most significant factors (e.g., age, blood pressure, etc.) that influenced the AI system’s decision so that they can subsequently make informed decisions on their own. Where “black box” models are deployed, rendering it difficult, if not impossible to provide explanations as to the workings of the AI system, outcome based explanations, with a focus on explaining the impact of decisionmaking or results flowing from the AI system may be relied on. Alternatively, deployers may also consider focusing on aspects relating to the quality of the AI system or preparing information that could build user confidence in the outcomes of an AI system’s processing behaviour. Some of these measures are: • Documenting the repeatability of results produced by the AI system. Some practices to demonstrate repeatability include conducting repeatability assessments to ensure deployments in live environments are repeatable and performing counterfactual fairness testing to ensure that the AI system’s decisions are the same in both the real world and in the counterfactual world. Repeatability refers to the ability of the system to consistently obtain the same results, given the same scenario. Repeatability often applies within the same environment, with the same data and the same computational conditions. • Ensuring traceability by building an audit trail to document the AI system development and decisionmaking process, implementing a black box recorder that captures all input data streams, or storing data appropriately to avoid degradation and alteration. • Facilitating auditability by keeping a comprehensive record of data provenance, procurement, preprocessing, lineage, storage, and security. Such information can also be centralised digitally in a process log to increase capacity to cater the presentation of results to different tiers of stakeholders with different interests and levels of expertise. Deployers should, however, note that auditability does not necessarily entail making certain confidential information about business models or intellectual property related to the AI system publicly available. A risk based approach can be taken towards identifying the subset of AI enabled features in the AI system for which implemented auditability is necessary to align with regulatory requirements or industry practices. • Using AI Model Cards, which are short documents accompanying trained machine learning models that disclose the context in which models are intended to be used, details of the performance evaluation procedures, and other relevant information. In cases where AI systems are procured directly from developers, deployers will have to work together with these developers to achieve transparency. More on this will be covered in later sections of the Guide.

Published by ASEAN in ASEAN Guide on AI Governance and Ethics, 2024

IV. Transparency

The traceability of AI systems should be ensured; it is important to log and document both the decisions made by the systems, as well as the entire process (including a description of data gathering and labelling, and a description of the algorithm used) that yielded the decisions. Linked to this, explainability of the algorithmic decision making process, adapted to the persons involved, should be provided to the extent possible. Ongoing research to develop explainability mechanisms should be pursued. In addition, explanations of the degree to which an AI system influences and shapes the organisational decision making process, design choices of the system, as well as the rationale for deploying it, should be available (hence ensuring not just data and system transparency, but also business model transparency). Finally, it is important to adequately communicate the AI system’s capabilities and limitations to the different stakeholders involved in a manner appropriate to the use case at hand. Moreover, AI systems should be identifiable as such, ensuring that users know they are interacting with an AI system and which persons are responsible for it.

Published by European Commission in Key requirements for trustworthy AI, Apr 8, 2019

Principle 5 – Reliability & Safety

The reliability and safety principle ensures that the AI system adheres to the set specifications and that the AI system behaves exactly as its designers intended and anticipated. Reliability is a measure of consistency and provides confidence in how robust a system is. It is a measure of dependability with which it operationally conforms to its intended functionality and the outcomes it produces. On the other hand, safety is a measure of how the AI system does not pose a risk of harm or danger to society and individuals. As an illustration, AI systems such as autonomous vehicles can pose a risk to people’s lives if living organisms are not properly recognized, certain scenarios are not trained for or if the system malfunctions. A reliable working system should be safe by not posing a danger to society and should have built in mechanisms to prevent harm. The risk mitigation framework is closely related to this principle. Potential risks and unintended harms should be minimized in this aspect. The predictive model should be monitored and controlled in a periodic and continuous manner to check if its operations and functionality are aligned with the designed structure and frameworks in place. The AI system should be technically sound, robust, and developed to prevent malicious usage to exploit its data and outcomes to harm entities, individuals or communities. A continuous implementation continuous development approach is essential to ensure reliability.

Published by SDAIA in AI Ethics Principles, Sept 14, 2022

Principle 6 – Transparency & Explainability

The transparency and explainability principle is crucial for building and maintaining trust in AI systems and technologies. AI systems must be built with a high level of clarity and explainability as well as features to track the stages of automated decision making, particularly those that may lead to detrimental effects on data subjects. It follows that data, algorithms, capabilities, processes, and purpose of the AI system need to be transparent and communicated as well as explainable to those who are directly and indirectly affected. The degree to which the system is traceable, auditable, transparent, and explainable is dependent on the context and purpose of the AI system and the severity of the outcomes that may result from the technology. AI systems and their designers should be able to justify how the rationale behind their design, practices, processes, algorithms, and decisions or behaviors are ethically permissible, nondiscriminatory, and nonharmful to the public.

Published by SDAIA in AI Ethics Principles, Sept 14, 2022

1. Demand That AI Systems Are Transparent

A transparent artificial intelligence system is one in which it is possible to discover how, and why, the system made a decision, or in the case of a robot, acted the way it did. In particular: A. We stress that open source code is neither necessary nor sufficient for transparency – clarity cannot be obfuscated by complexity. B. For users, transparency is important because it builds trust in, and understanding of, the system, by providing a simple way for the user to understand what the system is doing and why. C. For validation and certification of an AI system, transparency is important because it exposes the system’s processes for scrutiny. D. If accidents occur, the AI will need to be transparent and accountable to an accident investigator, so the internal process that led to the accident can be understood. E. Workers must have the right to demand transparency in the decisions and outcomes of AI systems as well as the underlying algorithms (see principle 4 below). This includes the right to appeal decisions made by AI algorithms, and having it reviewed by a human being. F. Workers must be consulted on AI systems’ implementation, development and deployment. G. Following an accident, judges, juries, lawyers, and expert witnesses involved in the trial process require transparency and accountability to inform evidence and decision making. The principle of transparency is a prerequisite for ascertaining that the remaining principles are observed. See Principle 2 below for operational solution.

Published by UNI Global Union in Top 10 Principles For Ethical Artificial Intelligence, Dec 11, 2017

3.1 Explainability and verifiability

Principle: ETHICAL GUIDELINES FOR THE DEVELOPMENT, APPLICATION AND USE OF RELIABLE AND RESPONSIBLE ARTIFICIAL INTELLIGENCE, Febrary, 2023

Related Principles

1. Transparency and Explainability

IV. Transparency

Principle 5 – Reliability & Safety

Principle 6 – Transparency & Explainability

1. Demand That AI Systems Are Transparent

Contact