AI Attribution and Provenance
The explorations surrounding collaboration with Artificial Intelligence (AI) tools, such as OpenAI’s ChatGPT, lack an essential practical consideration: how do we best signal AI assistance when publishing without diminishing trust?
In this post, I want to explore how we might establish AI attribution frameworks and increase the transparency of provenance.
You might have encountered the term provenance in a gallery heist film or read about missing art forgeries. The more relatable application of provenance is traceability. So before we explore what this means for artificial intelligence, let’s start with a snack and a cuppa.
When you bite that sandwich at lunchtime, do you ever wonder where the food is from? Maybe you pondered on the origin of the ingredients as you stood in the supermarket aisle, trying to figure out what to choose. I often wonder how far these products have travelled, and these questions have only increased over my lifetime.
Consumer demands for information about where food is from and what’s in it have increased considerably over the last two decades. The need for more food transparency continues to rise as we pay more attention to our health, sustainable practices, climate and food miles.
A 2020 report from the US-based FMI, The Food Industry Association and Label Insight found that 81% of shoppers think transparency is important or extremely important when shopping online or in-store. A jump of 12% from 69% in 2018.
The shifting consumer demands in the food industry are an interesting parallel to how we might respond in the coming months to the emergence of AI tools that support our creativity and productivity.
The call for greater transparency about what lies beneath the magic act will only get louder.
Just as I want to trust the eggs I buy are ethically farmed, with investment in high levels of animal welfare and a commitment to sustainable industry standards; I want the same from the AI bots I collaborate with.