Dataset Bias

Dataset bias occurs when the data used to train a machine learning model isn't representative of the real-world diversity or situations it will encounter. This can happen if the data focuses too much on one group, perspective, or type of example, leading the model to perform poorly or unfairly on others. Essentially, the model learns patterns from biased data, which can result in inaccuracies or discrimination when applied broadly. Recognizing and addressing dataset bias is crucial to developing fair, accurate, and reliable AI systems.