The researchers found that humans alter their behavior when they know their actions train AI, suggesting a reciprocal influence in human-AI interactions.
Dr. Philip R.O. Payne emphasized the need to understand trainer behaviors in AI development, highlighting the importance of measuring and mitigating inherent biases.
Lead researcher Lauren Treiman noted that if humans adjust their driving behavior to train self-driving cars, it may not reflect real-world situations accurately.
Treiman's reflection on social media algorithms illustrates how user interactions influence AI training, showing how slight behaviors can drastically affect content delivery.
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