Informed consent: considering steganographic techniques to fingerprint Generative AI output
Artificial intelligence (AI) is a polarizing topic. For every reasoned assessment of the technology and its potential to make some of our smaller, onerous, or more repetitive tasks easier, there are probably 100 reactive pieces predicting some radical overhaul of societal norms, from the service industry receiving new intakes of out of work software developers to laypeople taking on roles traditionally occupied by those of a college education, if they just start “asking their AI the right questions“ ¯\_(ツ)_/¯
The amount of AI-propaganda is draining, and the reaction is often spread across the board too, some cheer leading, some decrying, plenty taking their time to offer skilled and nuanced rebuttals, or suggestions for improvements.
I find myself largely trying to stay out of the conversations. A lot like blockchain conversations 8 years ago, it will take another half decade for the hype-cycle to plateau for us to see where it can truly complement our work.
One part of the conversation that is increasingly harder to ignore, is being informed about when AI has been used in the generation of text or images. It is the property of knowing, or having the tools to know is what I feel is the most important.
How can we be better informed about when AI is used, so that we are better prepared as consumers, to receive and understand content?
In this blog I want to explore the potential for steganography techniques to be used in the output of AI to fingerprint content and provide a way for front-end mechanisms to identify it, as we might file formats using magic numbers, so that users can be given the chance of informed consent: the opportunity to opt-in or out of whether we engage with AI content or not.
