Who Said The AI ML Was Fair?

by Jay Cuthrell
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This week we take a look at fairness in A.I. and M.L. and F.A.I.R. data.

This week’s musical inspiration in title and lyrics:

Getting Informed

If all is fair in love and war… then what about AI and ML or data? πŸ€”

To be _fair_, there is a lot of work being done by the word _fair_ this week. Indeed, one could argue that the _fair_ within AI ML concerns of fairness specifically apply when and where a _fare_ is assessed to be paid by the world, humanity, and future generations for their own data. 🧐

FAIR is not a backronym for Frequently Amorphous Inherent Rigamarole. At least, I hope not. πŸ€“

This week I read, listened, and watched a bit more than usual. Here are my reading πŸ“–, watching πŸ“Ί, and listening 🎧 suggestions:

The sun is free enough 🎢

I’m reminded that FAIR principles are less than a decade old. That’s young in IT terms.

As I’ve said before… ethics and empathy are needed.


But if they can, they’ll find a way 🎢

Dystopian fever dreams aside, it can be useful to imagine a system of perverse market incentives that drive opaquely enriched data to become more closed, more proprietary, more paywalled, and more about short term extraction than balanced long term local enlightenment as well as global enlightenment. Indeed, we must continuously balance market demands with the pursuit of science, applied technology, and our evolving human values.

Seeking fairness in AI/ML pursuits and FAIR data frameworks could easily be part of how we govern ourselves in the future β€” or not. Without both, it is not clear how auditing would be possible and could reduce terms such as transparency to being little more than an early 2000s era platitude that exited the zeitgeist almost as soon as it entered.

So, what will be the next big thing in AI/ML fairness and FAIR data?

Until then… Place your bets!


I am linking to my disclosure.



✍️ πŸ€“ Edit on Github πŸ™ ✍️

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