bias and fairness in neural networks

Bias and fairness in neural networks refer to the presence of biases and the extent to which the neural network outputs exhibit fairness. Bias in neural networks is the systematic favoring or prejudice towards certain classes, groups, or attributes during the learning process. It may occur due to imbalanced training data, inappropriate feature representation, or design choices. Biases can lead to unfair predictions or contribute to discrimination and inequity. Fairness in neural networks relates to the degree to which the network's decisions and predictions are impartial and unbiased across different groups or attributes. Fairness aims to ensure that the model does not discriminate based on protected attributes like race, gender, or age, and provides equal opportunities and treatment for all individuals. Achieving fairness in neural networks requires addressing biases and eliminating discriminatory patterns from the model's behavior. It involves designing and training the network in ways that mitigate disparate treatment and ensure objective decision-making processes. Various techniques such as pre-processing, in-processing, and post-processing can be used to enhance fairness in neural networks.

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