Transparency and explainability (Principle 1.3) icon Transparency and explainability (Principle 1.3)

This principle is about transparency and responsible disclosure around AI systems to ensure that people understand when they are engaging with them and can challenge outcomes.

AI Actors should commit to transparency and responsible disclosure regarding AI systems. To this end, they should provide meaningful information, appropriate to the context, and consistent with the state of art:

  • to foster a general understanding of AI systems,
  • to make stakeholders aware of their interactions with AI systems, including in the workplace,
  • to enable those affected by an AI system to understand the outcome, and,
  • to enable those adversely affected by an AI system to challenge its outcome based on plain and easy-to-understand information on the factors, and the logic that served as the basis for the prediction, recommendation or decision.

From the AI Wonk

Rationale for this principle

The term transparency carries multiple meanings. In the context of this Principle, the focus is first on disclosing when AI is being used (in a prediction, recommendation or decision, or that the user is interacting directly with an AI-powered agent, such as a chatbot). Disclosure should be made with proportion to the importance of the interaction. The growing ubiquity of AI applications may influence the desirability, effectiveness or feasibility of disclosure in some cases.

Transparency further means enabling people to understand how an AI system is developed, trained, operates, and deployed in the relevant application domain, so that consumers, for example, can make more informed choices. Transparency also refers to the ability to provide meaningful information and clarity about what information is provided and why. Thus transparency does not in general extend to the disclosure of the source or other proprietary code or sharing of proprietary datasets, all of which may be too technically complex to be feasible or useful to understanding an outcome. Source code and datasets may also be subject to intellectual property, including trade secrets.

An additional aspect of transparency concerns facilitating public, multi-stakeholder discourse and the establishment of dedicated entities, as necessary, to foster general awareness and understanding of AI systems and increase acceptance and trust.

Explainability means enabling people affected by the outcome of an AI system to understand how it was arrived at. This entails providing easy-to-understand information to people affected by an AI system’s outcome that can enable those adversely affected to challenge the outcome, notably – to the extent practicable – the factors and logic that led to an outcome. Notwithstanding, explainability can be achieved in different ways depending on the context (such as, the significance of the outcomes). For example, for some types of AI systems, requiring explainability may negatively affect the accuracy and performance of the system (as it may require reducing the solution variables to a set small enough that humans can understand, which could be suboptimal in complex, high-dimensional problems), or privacy and security. It may also increase complexity and costs, potentially putting AI actors that are SMEs at a disproportionate disadvantage.

Therefore, when AI actors provide an explanation of an outcome, they may consider providing – in clear and simple terms, and as appropriate to the context – the main factors in a decision, the determinant factors, the data, logic or algorithm behind the specific outcome, or explaining why similar-looking circumstances generated a different outcome. This should be done in a way that allows individuals to understand and challenge the outcome while respecting personal data protection obligations, if relevant.

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