Pollution with Purpose: The Role of Data Quality in Trustworthy AI

Kurzfassung

This paper contributes to the practical implementation of the third pillar of trustworthy AI: technical and social robustness. We enhance the robustness and reproducibility of an AI model through the integration of polluted data into the AI training process. To do so, the YOLO11n model backbone is fine-tuned with a subset of the PASCAL VOC12 benchmark data using different shares and intensities of a horizontal blur polluter in the training data. Through this approach we are able to reach a significant increase in robustness on similarly polluted test data. Hereby, the training of AI systems becomes better aligned with context- and environment-specific conditions. This approach does not only contribute to the technical robustness of AI systems but poses the opportunity to also boost their social robustness, by increasing their adaptability to diverse and dynamic real-world settings.