AI Bias and Fairness

In AI, bias can be defined as the presence of systematic errors in a machine learning model. These errors can be due to numerous factors, including the dataset used to train the model, the features selected, and the algorithms used to build the model.

Fairness, on the other hand, is the absence of any systematic bias in the model. A model is considered fair if it does not make decisions that disproportionately harm certain groups of people.

For example, an AI-powered recruitment tool that is trained on resumes of only male applicants will likely be biased against female applicants. Similarly, a facial recognition system that is trained on images of only light-skinned individuals is likely to be biased against people with darker skin tones.

Addressing Bias and Ensuring Fairness

To address bias and ensure fairness in AI, researchers and developers must take a proactive approach. This includes:

• Using diverse datasets
• Carefully selecting features
• Using algorithms that are designed to minimize bias

In addition, oversight and accountability mechanisms must be put in place to ensure that the AI systems are unbiased and fair in their decision-making processes.

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