This is objectively incorrect. One of the largest problems related to bias in AI is that accuracy is not distributed evenly across different groups. For instance, the COMPAS expose revealed that an algorithm being used to predict who would commit crimes had significantly higher false positive rates (saying someone would commit a crime who then didn’t) for black people. Similarly the accuracy was lower for predicting more serious violent crimes than misdemeanors or other petty offenses. It’s not enough to say that an algorithm is accurate therefore it’s not biased it’s just showing truths we don’t want. You have to look very very carefully at where exactly the model is wrong and if it’s systematically wrong for certain kinds of people/situations. There’s a reason this is one of the most active areas of research in the machine learning community. It’s an important and hard problem with no easy solution.

Interesting argument. Anything to back it up?

They linked you proof, and you're still full of shit. You sound exactly like a certain group that rose in the early 1900s.

Incorrect. AI can definitely be biased. Where do you think the data that it's trained on comes from? Another AI? No, it comes from people. An AI is only as accurate as its training data.

For example, a famous example would be that AIs often come to the conclusion that black Americans are more healthy than other Americans and thus do not need as much assistance with their health. In reality, the opposite is true, but the AI doesn't realize that because it's just looking at the data given to it. That data shows that black Americans are less likely to go to the hospital, so the AI assumes that this is because there is nothing wrong with them. In reality, most humans would recognize that this is because black Americans are more likely to be poor, and can't afford to go to the hospital as frequently.

A few more examples that could happen: an AI image-generation program might be more likely to draw teachers as female, since that would be what most of the training data depicted. An AI facial recognition system might be less accurate at identifying hispanic people by their facial features because less images of hispanic people were included in the training data. An AI that suggests recommended prison sentences might give harsher sentences to black people because it was trained using previous decisions made by human judges, who tend to give harsher sentences to black people.

TL;DR: AI technology doesn't exist in a vacuum. People have biases, so AIs also have biases. AIs can have less bias if you're smart about what training data you use and what information you hide from the AI.

>Most likely the Amazon AI was as, or more proficient at selecting qualified candidates than any human HR department.

Why is that "most likely"? Citation needed.

(This reminds me of the experiment where they hired grad students to 'predict' if a light would turn on or not. The light turned on 80% of the time, but the grad students were only right about 50% of the time because they tried to predict the light. The researchers also tried monkeys, who just leaned on the button, and were right 80% of the time.

An AI is like those monkeys -- because 80% of good candidates are male, it thinks excluding female candidates will help the accuracy. But that's not actually true, and you are literally breaking the law.)

What if the truth is that a Resume alone is not enough to accurately predict if someone is going to work out in a position? What if a full job interview is actually required to find out if the person will fit in or be able to do good work? What if EVERY ranking algorithm is going to have bias, because there isn't enough data to accurately sort the resumes?

Having been a hiring manager, I have found that a large fraction of resumes contain made-up bullshit. AI is just GIGO with extra steps.

This reminds me of back in the 80's: Whenever a corporation made a mistake, they could never admit it -- instead, everyone would "blame it on the computer". That's like blaming the airplane when a bolt falls off (instead of blaming poor maintenance procedures.)