The principle "Draft Ethics Guidelines for Trustworthy AI" has mentioned the topic "transparency" in the following places:

5. The Principle of Explicability: “Operate transparently”

The Principle of Explicability: “Operate transparently”

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.

5. The Principle of Explicability: “Operate transparently”

Both technological and business model transparency matter from an ethical standpoint.

5. The Principle of Explicability: “Operate transparently”

Technological transparency implies that AI systems be auditable, comprehensible and intelligible by human beings at varying levels of comprehension and expertise.

5. The Principle of Explicability: “Operate transparently”

Technological transparency implies that AI systems be auditable, comprehensible and intelligible by human beings at varying levels of comprehension and expertise.

5. The Principle of Explicability: “Operate transparently”

Technological transparency implies that AI systems be auditable, comprehensible and intelligible by human beings at varying levels of comprehension and expertise.

5. The Principle of Explicability: “Operate transparently”

Business model transparency means that human beings are knowingly informed of the intention of developers and technology implementers of AI systems.

2. Data Governance

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.

5. Non Discrimination

Harm may also result from exploitation of consumer biases or unfair competition, such as homogenisation of prices by means of collusion or non transparent market.

10. Transparency

transparency

10. Transparency

transparency concerns the reduction of information asymmetry.

10. Transparency

explainability – as a form of transparency – entails the capability to describe, inspect and reproduce the mechanisms through which AI systems make decisions and learn to adapt to their environments, as well as the provenance and dynamics of the data that is used and created by the system.

10. Transparency

Explainability – as a form of transparency – entails the capability to describe, inspect and reproduce the mechanisms through which AI systems make decisions and learn to adapt to their environments, as well as the provenance and dynamics of the data that is used and created by the system.