Sources of Bias in Training Data and How to Avoid Them

Bias in training data can originate from several sources, including: 1. Historical Bias: Pre-existing societal biases that get embedded in the data. 2. Collection Bias: Bias introduced during the data collection phase, such as through unrepresentative samples. 3. Labeling Bias: Bias that occurs when assigning labels or categories to data. Avoiding these biases involves rigorous methodologies for data collection and processing, continuous monitoring for bias, and employing diversity in the teams that collect and analyze data.