How To Avoid Bias In Computer Vision Models

Algorithmic bias in machine learning, particularly within computer vision models, can be understood from both social-ethical and technical perspectives. While ethical implications are significant, the technical aspect focuses on detection and mitigation to improve model performance and business outcomes. Bias occurs when a model's average error is influenced by inappropriate weighting of factors, leading to poor predictions. To combat this, adopting a data-first approach is crucial, ensuring large and representative datasets that mirror real-world deployment environments. Active learning, which involves continuously updating the model with new data that challenges its weaknesses, is key to enhancing model accuracy. Techniques such as performing model error analysis and running health checks for class balance are essential for identifying and correcting biases. Additionally, avoiding duplicate images in datasets prevents skewed training and evaluation metrics. Roboflow's tools, like automatic duplicate removal, assist in maintaining data integrity, thereby reducing bias and improving model generalization. Engaging with community platforms like discuss.roboflow.com can further aid in refining models and sharing insights.