Sources of AI Bias:

AI bias can come from a variety of sources, including the data used to train the system, the algorithms used to process the data, and the people who create and use the system. In this lesson, we will explore each of these sources in more detail.

Data Bias:

One major source of AI bias is data bias. This occurs when the data used to train the AI system is not representative of the real world. For example, if an AI system is trained to recognize faces but only sees images of white people, it may not perform well when presented with images of people with darker skin tones.

Algorithm Bias:

Another source of AI bias is algorithm bias. This occurs when the algorithms used to process the data are biased. For example, if an AI system is trained to identify job candidates based on resumes, but the algorithm is biased against candidates who attended certain schools or have certain types of work experience, it may unfairly screen out qualified candidates.

People Bias:

A third source of AI bias is people bias. This occurs when the people who create and use the AI system are biased. For example, if a team of developers is predominantly male, they may unintentionally create an AI system that is biased against women.

Mitigating AI Bias:

To mitigate AI bias, it is important to address these sources of bias. This can include using diverse and representative data to train the system, carefully selecting and testing algorithms to ensure they are unbiased, and involving diverse teams of people in the development and use of the system.

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