The text discusses the persistent issue of bias in computer vision datasets, emphasizing the principle of "garbage in, garbage out" in data science. Bias in datasets can lead to skewed outcomes when training machine learning models, with notable examples like Amazon's gender-biased recruitment algorithm and Microsoft's controversial chatbot, Tay. The text identifies various types of bias—such as uneven sample classes, selection bias, and category bias—that may infiltrate datasets through human influence or unintentional dataset simplification. To mitigate these biases, it suggests strategies such as observing class distributions during annotation, ensuring datasets represent the target population, clearly defining annotation processes, establishing quality assurance benchmarks, and regularly assessing model performance. The article highlights the role of Encord, an AI-assisted active learning platform, in reducing bias by providing tools for data annotation, active learning, and model performance analysis, ultimately enhancing the accuracy and fairness of computer vision models.