Principle 6 – transparency & Explainability

Principle 6 – Transparency & explainability

The transparency and explainability principle is crucial for building and maintaining trust in AI systems and technologies.

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.

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.

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.

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.

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.

1 When designing a transparent and trusted AI system, it is vital to ensure that stakeholders affected by AI systems are fully aware and informed of how outcomes are processed.

Decisions should be traceable.

AI system owners must define the level of transparency for different stakeholders on the technology based on data privacy, sensitivity, and authorization of the stakeholders.

2 The AI system should be designed to include an information section in the platform to give an overview of the AI model decisions as part of the overall transparency application of the technology.

The model should establish a process mechanism to log and address issues and complaints that arise to be able to resolve them in a transparent and explainable manner.

The model should establish a process mechanism to log and address issues and complaints that arise to be able to resolve them in a transparent and explainable manner.

1 The data sets and the processes that yield the AI system’s decision should be documented to the best possible standard to allow for traceability and an increase in transparency.

1 The data sets and the processes that yield the AI system’s decision should be documented to the best possible standard to allow for traceability and an increase in transparency.

This has a direct effect on the training and implementation of these systems since the criteria for the data’s organization, and structuring must be transparent and explainable in their acquisition and collection adhering to data privacy regulations and intellectual property standards and controls.

This has a direct effect on the training and implementation of these systems since the criteria for the data’s organization, and structuring must be transparent and explainable in their acquisition and collection adhering to data privacy regulations and intellectual property standards and controls.

1 transparency in AI is thought about from two perspectives, the first is the process behind it (the design and implementation practices that lead to an algorithmically supported outcome) and the second is in terms of its product (the content and justification of that outcome).

Algorithms should be developed in a transparent way to ensure that input transparency is evident and explainable to the end users of the AI system to be able to provide evidence and information on the data used to process the decisions that have been processed.

Algorithms should be developed in a transparent way to ensure that input transparency is evident and explainable to the end users of the AI system to be able to provide evidence and information on the data used to process the decisions that have been processed.

Algorithms should be developed in a transparent way to ensure that input transparency is evident and explainable to the end users of the AI system to be able to provide evidence and information on the data used to process the decisions that have been processed.

2 transparent and explainable algorithms ensure that stakeholders affected by AI systems, both individuals and communities, are fully informed when an outcome is processed by the AI system by providing the opportunity to request explanatory information from the AI system owner.

2 Transparent and explainable algorithms ensure that stakeholders affected by AI systems, both individuals and communities, are fully informed when an outcome is processed by the AI system by providing the opportunity to request explanatory information from the AI system owner.

This enables the identification of the AI decision and its respective analysis which facilitates its auditability as well as its explainability.

This enables the identification of the AI decision and its respective analysis which facilitates its auditability as well as its explainability.

3 If the AI system is built by a third party, AI system owners should make sure that an AI Ethics due diligence is carried out and all the documentation are accessible and traceable before procurement or sign off.

and execution of the AI system transparent.

2 Organizations can put in place additional instruments such as impact assessments, risk mitigation frameworks, audit and due diligence mechanisms, redress, and disaster recovery plans.

3 The documentation of the process is necessary for auditing and risk mitigation.