· Prepare Input Data:
1 Following the best practice of responsible data acquisition, handling, classification, and management must be a priority to ensure that results and outcomes align with the AI system’s set goals and objectives.
· Prepare Input Data:
1 To ensure that AI models embody a human centric build and design that requires adhering to practices of responsible and ethical data management frameworks and processes to be followed according to best practices and data regulations within KSA.
· Plan and Design:
2 When planning and designing AI systems, due consideration should be given to preventing and helping address social and environmental issues in a way that will ensure sustainable social and ecological responsibility.
Principle 7 – Accountability & Responsibility
Principle 7 – Accountability & responsibility
Principle 7 – Accountability & Responsibility
The accountability and responsibility principle holds designers, vendors, procurers, developers, owners and assessors of AI systems and the technology itself ethically responsible and liable for the decisions and actions that may result in potential risk and negative effects on individuals and communities.
Principle 7 – Accountability & Responsibility
The accountability and responsibility principle holds designers, vendors, procurers, developers, owners and assessors of AI systems and the technology itself ethically responsible and liable for the decisions and actions that may result in potential risk and negative effects on individuals and communities.
Principle 7 – Accountability & Responsibility
The designers, developers, and people who implement the AI system should be identifiable and assume responsibility and accountability for any potential damage the technology has on individuals or communities, even if the adverse impact is unintended.
Principle 7 – Accountability & Responsibility
The accountability and responsibility principle is closely related to the fairness principle.
Principle 7 – Accountability & Responsibility
The parties responsible for the AI system should ensure that the fairness of the system is maintained and sustained through control mechanisms.
Plan and Design:
1 This step is crucial to design or procure an AI System in an accountable and responsible manner.
Plan and Design:
The ethical responsibility and liability for the outcomes of the AI system should be attributable to stakeholders who are responsible for certain actions in the AI System Lifecycle.
Plan and Design:
The ethical responsibility and liability for the outcomes of the AI system should be attributable to stakeholders who are responsible for certain actions in the AI System Lifecycle.
Plan and Design:
It is essential to set a robust governance structure that defines the authorization and responsibility areas of the internal and external stakeholders without leaving any areas of uncertainty to achieve this principle.
· Prepare Input Data:
1 An important aspect of the Accountability and responsibility principle during Prepare Input Data step in the AI System Lifecycle is data quality as it affects the outcome of the AI model and decisions accordingly.
· Prepare Input Data:
The data preparation process and data quality checks should be documented and validated by responsible parties.
· Build and Validate:
To achieve this, the technical stakeholders who build and validate models should be responsible for these decisions.
· Deploy and Monitor:
1 The responsibility and associated liability in the Deploy and Monitor step should be set clearly.