Without Acknowledgment: Cultivating Gratitude in an ML age

Creativity support tools based on machine learning hide the human authors that contributed to the work. Does it have to be that way?

Ken Arnold


January 12, 2023

Generative AI tools can be really helpful for our creativity. Engaging with many and diverse examples has been shown to help human creativity; the systems themselves have engaged deeply with diverse examples and can fluently retrieve them for creators to use. But unlike when we a search engine, we can’t even acknowledge our inspiration if we want to. Rather than being able to give credit to a specific human author, or even to a creative team, the model makes us credit it, as the amalgamation of all of the human labor that it was trained on. The sheer scale of the data these models work with makes this hard: being trained on close to all human work becomes essentially the same as not being human work at all. If gratitude is a virtue, it seems like generative AI is positioned in opposition to it.

Does it have to be this way? Yesterday I referenced an LM that tries to cite its sources for factual knowledge; citing sources for ideas and inspiration is technically and conceptually harder. But here are some things we might explore: