But it’s also more insidious than that, because the far reaching implications of this bias often cannot be predicted. For example, excluding all gender data from training ended up making sexism worse in this real world example of financial lending assisted by AI and the same was true for apple’s credit card and we even have full-blown articles showing how the removal of data can actually reinforce bias indicating that it’s not just what material is used to train the model but what data is not used or explicitly removed.
This is so much more complicated than “this is obvious” and there’s a lot of signs pointing towards the need for regulation around AI and ML models being used in places it really matters, such as decision making, until we understand it a lot better.
What is obvious to you is not always obvious to others. There are already countless examples of AI being used to do things like sort through applicants for jobs, who gets audited for child protective services, and who can get a visa for a country.
But it’s also more insidious than that, because the far reaching implications of this bias often cannot be predicted. For example, excluding all gender data from training ended up making sexism worse in this real world example of financial lending assisted by AI and the same was true for apple’s credit card and we even have full-blown articles showing how the removal of data can actually reinforce bias indicating that it’s not just what material is used to train the model but what data is not used or explicitly removed.
This is so much more complicated than “this is obvious” and there’s a lot of signs pointing towards the need for regulation around AI and ML models being used in places it really matters, such as decision making, until we understand it a lot better.