The fairness principle requires taking necessary actions to eliminate bias, discriminationor stigmatization of individuals, communities, or groups in the design, data, development, deployment and use of AI systems.

bias may occur due to data, representation or algorithms and could lead to discrimination against the historically disadvantaged groups.

When designing, selecting, and developing AI systems, it is essential to ensure just, fair, non biased, non discriminatory and objective standards that are inclusive, diverse, and representative of all or targeted segments of society.

To ensure consistent AI systems that are based on fairness and inclusiveness, AI systems should be trained on data that are cleansed from bias and is representative of affected minority groups.

Al algorithms should be built and developed in a manner that makes their composition free from bias and correlation fallacy.

The fairness principle requires taking necessary actions to eliminate bias, discrimination or stigmatization of individuals, communities, or groups in the design, data, development, deployment and use of AI systems.

bias may occur due to data, representation or algorithms and could lead to discrimination against the historically disadvantaged groups.

When designing, selecting, and developing AI systems, it is essential to ensure just, fair,non biased, non discriminatory and objective standards that are inclusive, diverse, andrepresentative of all or targeted segments of society.

To ensure consistent AI systems that are based on fairness and inclusiveness, AI systems shouldbe trained on data that are cleansed from bias and is representative of affected minority groups.Al algorithms should be built and developed in a manner that makes their composition free frombias and correlation fallacy.

During this phase, it is important to implement a fairness awaredesign that takes appropriate precautions across the AI system algorithm, processes, andmechanisms to prevent biases from having a discriminatory effect or lead to skewed andunwanted results or outcomes.

2 Sensitive personal data attributes which are defined in the plan and design phase should not be included in the model data not to feed the existing bias on them.

4 Automated decision support technologies present major risks of bias and unwanted application at the deployment phase, so it is critical to set out mechanisms to prevent harmful and discriminatory results at this phase.

Periodic UI and UX testing should be conducted to avoid the risk of confusion, confirmation of biases, or cognitive fatigue of the AI system.

Furthermore, the data should be cleansed from societal biases.