👉 Fumes, in the context of the model, refer to the unintended outputs or side effects that arise when a machine learning model is trained or operates beyond its intended parameters or data scope. These can include biases, inaccuracies, or unexpected behaviors that emerge due to the model's complexity and the vast, often unstructured data it processes. For instance, if a language model is trained primarily on Western literature, it might produce text that reflects Western cultural norms and lacks understanding of other perspectives. Fumes highlight the challenges in ensuring models are fair, reliable, and aligned with ethical standards, necessitating ongoing monitoring and refinement to mitigate these unintended consequences.
