Fair AI is the amalgamation of technologies and economic incentives that, together, ensures AI evolves in a way that is beneficial to everyone who enables or uses it. There are some core principles to Fair AI — ownership, permission, and fair compensation. Specifically, fair compensation for the contribution of data, compute, and content to datasets. Consider this example — a user could give permission to contribute their data from their social profile – say, Twitter/X. This is not dissimilar from the process that happens when you give certain applications access to your calendar. These contributions create new datasets for developers to use that belong outside of what’s publicly available and because of this, that person contributing their data to then build a more robust AI ecosystem, would then receive compensation in the form of an on-chain asset. This type of contribution is distinctly different from the current direction of Big AI. You are not compensated when the decades of your Google Search activity is used by Gemini to inform a generative output for another service.
