Comment by Xcelerate | Hacker News Reader

Comment by Xcelerate | Hacker News Reader

There is a somewhat widespread implicit assumption that having sufficient intelligence to develop increasingly sophisticated machine learning implies that we also have sufficient intelligence to develop an “intuitive” understanding of how it works.

I think that assumption is totally wrong, and trying to reconcile the two is probably a distraction and a waste of time.

Evolution led to human intelligence just fine on its own; why are humans not simply the catalyst in the next stage of this natural process from which something more complex arises?

With regard to the moral questions around machine learning, I think people are overthinking things. There needs to be a sharp (societal) line between inferring causation and the capability for prediction. Maximize the latter any way that you can, but a lot of careful thought should be put into how the results of a prediction are utilized. Instead we are currently going about this backward by trying to haphazardly “clean” the input data so that the output gets a free pass on how it can be used. We can never decouple all of the biases and eliminate intra-predictivity in the input data though, so it’s fundamentally a bad approach.

> There is a somewhat widespread implicit assumption that having sufficient intelligence to develop increasingly sophisticated machine learning implies that we also have sufficient intelligence to develop an “intuitive” understanding of how it works.

I agree with you on that point, but not with the conclusion that this is in any way a good thing.

> Evolution led to human intelligence just fine on its own; why are humans not simply the catalyst in the next stage of this natural process from which something more complex arises?

I think I have the complete opposite view here. Why would "being the catalyst in the next stage of evolution" be anything desirable? Evolution has no "stages", it's just chaos and life trying to deal in the best way with the circumstances at hand. If we blew everything up in nuclear armageddon tomorrow and the only remaining life were archaeae, those would be a perfectly fine "next stage of evolution".

Similarly, we can build a would in which only robots can survive, but why would we want to?

I think, gaining more understanding how the world works is the one thing AI can really bring to the table. If it solves practical problems on the way to that, that's great, but we evidently survived so far pretty well by doing that stuff ourselves.

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.

I think with this response, you reveal more about yourself and your worldview, than any general truth about others.

And with yours you reveal that you have no point at all

What exactly is the worldview you're describing here?

I always thought it was the opposite. That we want AI to have no biases, but we have trouble avoiding it. Choosing data and cleaning is where we insert our bias.

What people want is for the models to show no differences between certain groups of people. That is the standard they use for "no bias." But that itself is bias, so really they just want the model output to conform to their bias that there isn't any difference.

In fact, the article cites the kind of bias that warrants eliminating. One example is the misidentification of people with dark skin by facial recognition technology, as this has led to the arrest or incarceration of innocent people.

Not, as you completely fabricate, "show no differences between certain groups of people."

You, however, are showing your bias.

So what you're really saying is, it's perfectly okay for AI to have negative preconceptions and concrete impacts to certain groups of people, as long as it agrees with you on which groups are bad.

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.

Isn’t the media doing exactly that?

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.

Your deflection about the media is entirely unrelated to AI, but it only backs up my argument. If giant media corporations are biased, why can't AI researchers be?

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 don’t think it’s unrelated:

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.

AI is a developing technology that stands to be used by powerful businesses and governments for all manner of purposes that will likely drastically impact our lives. It's important to criticize what could become the next industrial revolution, and make sure we can get rid of as many systemic issues as possible before they become too big to fix.

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.

I'm gonna need a cite and some statistical analysis to back up your "media is biased" claim. One data point on an event that I easily found reporting on isn't going to cut it.

Will FBI crime statistics do?

> 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.

You failed to show evidence that the media is claiming that white people disproportionately commit hate crime.

Thanks, your point exactly. My subjective and N=1 opinion is that whites are dramatically under-reported as criminals in the media.

Correct. As a tool serving humans, we want the tool to reflect the decisions humans should make given the same broad swath of data.

"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.

You are conflating the statistical meaning of bias with a social meaning of bias.

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.

I'm using biased as it is colloquially used and pointing out that when people ask for "unbiased machine learning" they generally need to be more specific, because the whole point of machine learning is to give better answers than random noise.

You're right; GP is just trying to make an unwarranted political point.

What are you saying?

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.

Assuming that a human stereotype is untrue is just as biased as assuming it is true. If you judge a model by how well it confirms your previous bias, then the model tells you nothing you didn't already know.

I completely disagree. You're right that everyone has biases - including the authors of the AI who will therefore choose a biased training dataset. When the tool they are constructing has unparalleled power, and is very likely to negatively impact the lives of those who the authors are biased against (consciously or not), it's entirely fair for those impacted groups to be concerned.

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.

These imbalances in the training data are there because the world generates data in an imbalanced way. Sometimes you can correct for it, other times it's impossible. You simply can't find good data for all your cases. For example in medical diagnosing or in self driving.

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