Biases in AI Systems

Biases in AI Systems
queue.acm.org

1. Someone from Utah is more likely to be a member of the Church of Jesus Christ of Latter Day Saints than someone from Pennsylvania.

2. Someone from an Arab speaking country is more likely to be Muslim than someone from a non Arab speaking country.

3. Someone who says "eh" at the end of every sentence is more likely to be Canadian.

4. Someone who says y'all is more likely to be from the south.

5. If someone asks me to "Please do the needful" they are likely from India.

I've purposely chosen non extreme examples because there are many basis all over the place. Bais ≠ prejudice.

Ultimately if we artificially restrain AI from being "baised" in any form we are really shooting ourselves and those most disadvantaged in the foot because instead of being able to use AI to discover the basis and then work on fixing it we instead just to pretend it doesn't exist.

Finally a more provocative example. People who get pay day loans are less likely to pay back loans, black people are more likely to use pay day loans, ergo black people are more likely to default on loans. If we try and just force an AI to ignore this then we paper over the problem. If instead we start to examine causality we can start to figure out the root of the issue and how to address.

The causality piece is exactly the issue, right? People who use payday loans have less savings, more likely to work in jobs where their hours are unstable, have other poor financial indicators (past use of a payday loan, for example). Black people may disproportionately fall into this category, but I would argue it is wrong to effectively punish all black people (or conversely give other ethnicities an easier time) simply because of their race.

Biases exist, no argument there. The dilemma is what we do with them.