8 papers analyzed
These studies suggest that algorithmic bias in machine learning can arise from data processing patterns, user input, and regularization measures, potentially leading to socioeconomic disparities, social inequality, and unfair discrimination.
Algorithmic bias in machine learning refers to the systematic and repeatable errors in a computer system that create unfair outcomes, such as privileging one arbitrary group of users over others. This bias can stem from various sources, including the data used to train the algorithms, the design of the algorithms themselves, and the interaction between humans and algorithms.
Bias from Training Data:
Types of Bias:
Impact of Bias:
Sources of Bias:
Mitigation Strategies:
Algorithmic bias in machine learning is a multifaceted issue that arises from biases in training data, algorithmic design, and human-algorithm interactions. These biases can lead to unfair outcomes and perpetuate social inequalities. Addressing algorithmic bias requires a comprehensive approach that includes improving data integrity, refining algorithmic design, and incorporating diverse perspectives in the development and evaluation of machine learning models.
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