What people are really saying, but never will admit, is that they want AI to mirror their own biases, not to have none. That's the reason they are "cleaning" the input data.
That its better to just blindly trust in the output of a blackbox AI output? That naively feeding in all possible data into an AI is the "best" way to do it? It is been well documented that blindly trusting AI just leads to it perpetuating human stereotypes and worsen flawed systems. https://dl.acm.org/doi/10.1145/3531146.3533138
https://www.aclu.org/news/privacy-technology/algorithms-in-h...
It's not some great insight that offering different inputs will result in different outputs and MAYBE we want to get better output with a different input.
Every dataset has a skew to it, which should be accounted for during training. If the authors don't explicitly account for such things, out of ignorance or bias, then that skew, that bias, will be ingrained in the AI.
For example, if I only train my AI on it's knowledge of chocolate from ads for a particular brand, it's going to have some very opinionated, very wrong ideas about chocolate. This example is silly and obvious, but similar skews happen all the time in real datasets that the authors don't have the time or expertise to recognize.
When people who do have that expertise speak up, we should listen to them and fix it, not just blindly trust "the data" and whatever our fancy algorithm does with it. Garbage in, garbage out.
Not, as you completely fabricate, "show no differences between certain groups of people."
You, however, are showing your bias.
How would you feel about AI models that classify all modern white people as racists based on overwhelming historical data about slavery, the KKK, etc.? Hey man, data doesn't lie.
They fixate on white racists while ignoring (eg) the Christmas parade attack — leading to society developing a bias in their beliefs about racism.
I always take these criticism of AI as confession-through-projection of the misdeeds people have engaged in already.
Every institution has biases, and blindly trusting a dataset from e.g. a police department is just as bad as blindly trusting data from CNN or Fox News. The collection, aggregation, and sharing of data is a biased operation. If a political or for-profit entity gives you data, it's going to be data that supports their agenda.
It takes active, conscious effort to combat that bias, in the same way that it takes active, conscious effort to not just get all your news from one source. The concern (which has been demonstrated) is that that isn't being done
I often see that argument made in bad faith, eg when someone who believes conclusions from biased data encounters AIs making data driven conclusions, more than I do when the AI is genuinely biased.
I also have trouble taking it seriously, since that same standard isn’t applied to existing information services (eg, “media ethics” posts condemning their open racism) — only to new information services which might disrupt the current social order.
I should have stated that more directly — but I view these concerns largely as after the fact ethical nits from people whose comfortable myths are confronted by data, projecting their own manipulative behavior onto others.
And many people do criticise the biases of legacy media groups on both sides. But this is an article about AI on a site that is generally more concerned with technical innovations than societal issues, so AI is being criticized.
Besides, bad things existing now isn't really a good argument for why we shouldn't prevent bad things from happening in the future.
> In 2019, race was reported for 6,406 known hate crime offenders. Of these offenders:
> 52.5 percent were White.
> 23.9 percent were Black or African American.
https://ucr.fbi.gov/hate-crime/2019/topic-pages/offenders
White people don’t commit disproportionately many hate crimes — black people do. According to the FBI.
"Unbiased machine learning models" is basically a nonsense idea; a machine learning engine is a discrimination / classification tool, and its entire point is to become biased based on inputs so that on future inputs, its outputs tilt over in a desired fashion instead of just being uncorrelated noise. Making those biases have the desired shape is the art and science of the process.
A statistically unbiased model would be (for example) in which it has the same false positive rate at identifying humans from image data, for varying races, or a model that is equally likely to underpredict as overpredict tomorrow's gas price. These are not nonsense ideas, in fact, they are often good ideas. There are cases, however, where we may want a model to be statistically biased, for example, if the detriment of underpredicting tomorrow's gas price is worse than that of overpredicting it. If the gas price is lower than I expected, maybe it's not so bad that I didn't buy it today.
We (well, some of us) also don't want socially/politically biased models. Examples of those are very devious: redlining, for example, prevented black Americans from equal access to housing, historically, and has caused multiple generations of wealth disparity as a result.
You are using really loose and sloppy language in your talking about ML; you should be using "trained" and not "biased" because bias has a technical meaning that seems to evade you.