AI Fairness is a concept in the field of artificial intelligence sciences that concerns the creation and application of algorithms that ensure unbiased decisions. It aims to promote equality and prevent any form of discrimination (like age, race, or gender discrimination) which might be inadvertently incorporated in AI system's decision-making process. AI Fairness strives to ensure that AI technologies, irrespective of the data they are trained on, do not perpetuate harmful biases and stereotypes but rather make decisions and predictions that are equitable and fair.
AI Fairness in practice
Implementing AI Fairness involves several methods and techniques. At an initial stage, it starts with the collection of unbiased and diverse datasets for the training of the AI models. The datasets should be free from any biased data that can compromise the integrity of the model’s future decisions or predictions.
AI Fairness also requires continuous monitoring and auditing. Even if a model was unbiased at the time of deployment, changes in input data or the environment over time can still lead to it developing biases. Therefore, regular audits and updates are crucial.
Another method is the application of Fairness through Awareness approach where the algorithm is made aware of the specific attribute (like race or gender) which it can have bias against. Consequently, it is tasked to ensure that the decisions or predictions don't rely heavily on this attribute.
Moreover, the fairness of an AI system is not a one-off task but is a continuous process that requires persistent efforts and improvements.
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