Comment by yummyfajitas | Hacker News Reader

Comment by yummyfajitas | Hacker News Reader

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The term "bias" and "prejudice" even outside statistics are defined by the dictionary as being about getting the wrong results: "preconceived opinion that is not based on reason or actual experience".

This agrees more or less with the statistical term, it's just less precise. That's also NOT what machine learning algorithms do.

If you want to declare an algorithm biased because it optimizes what it was designed to optimize rather than some random tangential goal, then the term has become meaningless. Similarly, my cell phone is broken because it can only make calls and is ineffective at pulling trains. A locomotive is also broken because it doesn't make tacos.

It seems that what you're arguing is that the algorithm shouldn't be blamed in these cases, but in addition, you're declaring that there's no such thing as prejudice or bias...what the algorithms are doing is pulling some (uncomfortable for gatekeepers) platonic truth out of the ether (without any acknowledgement of the possibility of biased input.)

I have no idea how you could possibly think I made this claim given that I explicitly listed two examples of algorithms which are biased. I then hinted at how algorithms can eliminate this bias. Consider rereading what I wrote.

If you want more details on how algorithms correct biased inputs (it's not based on "ether") read the blog post I linked to downthread: https://www.chrisstucchio.com/blog/2016/alien_intelligences_...

Or more simply, you're using this article as an excuse to push a favorite "race realist" agenda which you use to justify a purist libertarianism (and which I agree it requires.)

I have no idea why you think "race realist" agendas require purist libertarianism, or vice versa. That's a random political tangent and totally unrelated to this conversation. I'm beginning to think you are seeking to derail the conversation rather than discussing statistics in good faith.

In the hopes that I've misunderstood you, I will nevertheless provide you with a good faith clarification. I made no normative claims regarding libertarianism or anything else. The only normative claim I've made is that we should not wrongfully apply an existing term (bias) to completely unrelated concepts (having undesired social outcomes).

> That's also NOT what machine learning algorithms do.

A machine learning algorithm run on non-representative input could be said to lack experience with the portion of input that isn't represented in the training set.

E.g. I think it's pretty obvious that machine learning algorithms exhibit far more vernacular-bias than purely logical reasoning techniques.

> I made this claim given that I explicitly listed two examples of algorithms which are biased.

Your parent is pointing out the very real possibility that technically-unbiased algorithms can produce vernacularly-biased outputs. I.e., that statistic's definition of "bias" does not sufficiently capture the notion of bias as it's used in the vernacular.

I don't think you've refuted that claim.

At the end of the day, your approach toward side-stepping the issue of vernacular bias is intellectually lazy. Instead of tackling the problem head on, you're hiding behind a mathematical object that happens to have the same name as the actual thing under discussion.

> I'm beginning to think you are seeking to derail the conversation rather than discussing statistics in good faith.

I think your characterization of bias in terms of statistic's technical definition is already skimming the edges of good faith. You know what the reporter means when they say "bias", and that's not the meaning that statisticians use in technical settings.

I know what the reporter means: "bias" refers to "outcomes the reporter dislikes and thinks he can generate clickbait with". I'm explicitly advocating against this practice and in favor of using clear and precise language instead.

At the end of the day, your approach toward side-stepping the issue of vernacular bias is intellectually lazy. Instead of tackling the problem head on, you're hiding behind a mathematical object that happens to have the same name as the actual thing under discussion.

I'm not side stepping the issue. I'm advocating in favor of describing the problem with clear language - once that's done we can begin useful discussion on how to address it.

In precise terms, what do you think the problem is?

Didn't he make that clear?

> Your parent is pointing out the very real possibility that _technically-unbiased algorithms can produce vernacularly-biased outputs_. I.e., that statistic's definition of "bias" does not sufficiently capture the notion of bias as it's used in the vernacular.

> "bias" refers to "outcomes the reporter dislikes and thinks he can generate clickbait with"

That's quite a straw man for someone who claims others are trying to derail a conversation.

> I'm explicitly advocating against this practice and in favor of using clear and precise language instead.

The language you're suggesting masks the problem.

It's nice that statisticians came up with a mathematical object that happens to have a politically charged name, but the conflation you're making is not an argument, and doesn't address the underlying (inherently political) problem.

> In precise terms, what do you think the problem is?

The precise problem is that things like skin color etc. often have strong predictive power. We've recognized this unfortunate fact -- and its societal implications -- and decided to create laws and social norms that regulate (either restrict or sometimes even require) the use of these properties in certain high-impact decisions.

Unfortunately, algorithms sometimes use these features to make decisions in settings where a typical human wouldn't (or might even get into legal hot water if they did).

Notice that an algorithm can exhibit this behavior without being (statistically) biased.

Now, perhaps you think this problem isn't actually a problem. And you can certainly make the argument that any form of vernacular-discrimination should be OK as long as it doesn't exhibit statistical bias. But that's a separate conversation, and you can't defend that position by playing with definitions. And if you're not making that argument, then it should be clear why vernacular-bias differs from statistical-bias.

You and pessimizer have just demonstrated pretty clearly why Techcrunch's language choices are bad.

Pessimizer thinks the article is discussing inaccurate conclusions and that race realism is wrong ( https://news.ycombinator.com/item?id=13158797 ). You think race realism is accurate but we should ignore it. You both read the same article but drew totally different conclusions due to it's imprecise language.

Many examples in the article don't fit your description of the problem at all (e.g. difficulties in image processing, Tay the trolled chatbot, filter bubbles), so I don't think you are correctly describing what the article means by "bias" either. I can't figure it out either; the best I can come up with is "AI-related things that make the author have negative feelings".

I didn't argue in favor of or against your mysterious "vernacular-discrimination" - how could I when I don't even know what it is? The only thing that I argued is that the imprecise language used by Techcrunch is confusing people and that this is bad.

You seem to be missing a key point, which is that the definitions of racial bias are and have always been contentious and up for debate. The definitions have always been in flux and have evolved heavily over time. This is basically true for many politically contentious topics, as different parties repeatedly try to redefine and reframe terms to their advantage.

If you insist on precise language, then we basically can never have any conversation about race, racial bias, or any other topic where the terminology hasn't been completely nailed down.

The only thing I'm advocating for in this thread is clear thinking and clear communication - in particular I oppose overloading a perfectly well defined and clear statistical term.

Similarly, I advocate against new-age uses of the word "energy" - energy is a specific conserved quantity, not some mysterious unobservable thing that emanates from crystals.

What benefit do we gain when other people can't even understand the words we use?

It's almost as if you want to use a Motte-and-Bailey argument to confuse people ( http://slatestarcodex.com/2014/11/03/all-in-all-another-bric... ). Which is of course something that people do on this very topic: https://www.chrisstucchio.com/blog/2016/propublica_is_lying....

> in particular I oppose overloading a perfectly well defined and clear statistical term.

The overloading happened the other way around...

> What benefit do we gain when other people can't even understand the words we use?

Similarly, what benefit do we gain when we use a term of art in settings where most of the audience doesn't have the technical background to be quite sure what they're even discussing?

Also, there is a notion that's distinct from statistical bias and that is highly political and controversial. Today we can say "bias" and most everyone will know we're talking about that topic. Whatever word or phrase we use in place of "bias" will be just as muddy and ill-defined. And probably some mathematician will come along and build some incomplete model and name if after that new word. And then we'll have this conversation again. The problem you're trying to avoid is unavoidable.

It's 100% clear from the article that 'bias' is in the political, or common sense of the word. It's an existing, established usage, and not one that the authors simply made up.

I fail to see why you insist that the authors use a word in its statistical sense when they clearly aren't making a statistical argument -- especially since the the statistical sense of the word bias is ultimately derived from the common usage, and not the other way around. The use of 'bias' in the sense of 'prejudice' in English dates from the 1570s, well before the development of statistics [1].

If anything, we should complain that the statisticians overloaded a perfectly good English word, instead of coming up with some Greek or Latin neologism for their own technical use.

[1] http://www.etymonline.com/index.php?term=bias

> Similarly, I advocate against new-age uses of the word "energy" - energy is a specific conserved quantity,

Sounds like a stilted way to look at the world. If someone says 'I'm feeling really energetic today', would you object on the grounds that they haven't shown that their biochemical energy reserves are larger than normal, or that their body is burning calories at a faster rate than usual?

If you read your source, you'll realize the term "bias" referred to a slant in the floor of a ball game providing an advantage to one player. This slant would (I'm inferring) cause one player to have an advantage over another equally matched player depending on which side of the slant they are on.

This agrees perfectly with the statisticians definition, albeit applied to one particular example. Specifically, the slanted ball game field would provide a biased measurement (in the statistical sense) of player ability. Score 1 for the mathematicians.

I agree with you that this techcrunch reporter is using the term in some other mysterious "vernacular" way. It's this usage that I'm objecting to since it's misleading and confusing.

If someone says 'I'm feeling really energetic today', would you object on the grounds that they haven't shown that their biochemical energy reserves are larger than normal, or that their body is burning calories at a faster rate than usual?

If an article were discussing how coal is more "energetic" than nuclear energy, but was actually referring to energetic in the psychological sense (or maybe in the hippy energy crystal sense), I would in fact object. That would be similarly misleading.

> This agrees perfectly with the statisticians definition, albeit applied to one particular example. Specifically, the slanted ball game field would provide a biased measurement (in the statistical sense) of player ability. Score 1 for the mathematicians.

That's silly. It's not a competition between mathematicians and all other people for the 'correct', Platonic definition of a word -- a word is defined by how people use and interpret it. Logical, mathematical deduction from first principles is not the only way to look at the world.

Unless I'm mistaken, I think your objection can be summarized as 'we shouldn't talk about things that we don't have airtight definitions for (like racial bias or racial prejudice), because it might confuse people'.

I happen to strongly disagree, since grappling with fuzzy definitions is precisely how we get to having better definitions. Otherwise, we should give up on ever talking about anything except math, where we have clear axioms to work with.

Even in math, people were profitably doing calculus before we had clear definitions for infinitesimals, integrals, derivatives, or proofs that our procedures produced correct results. The logical machinery and deductive logic often comes after people spent time grappling with fuzzy, intuitive things with unclear definitions.

Unless I'm mistaken, I think your objection can be summarized as 'we shouldn't talk about things that we don't have airtight definitions for (like racial bias or racial prejudice), because it might confuse people'.

No. I give a concrete example of what I want in this comment: https://news.ycombinator.com/item?id=13161470

Specifically, the word "bias" makes virtually everyone think of one particular concept. I claim expanding the word "bias" to describe two separate concepts misleads people into thinking the second concept is the same as the first.

My sole objection is with confusing people by conflating multiple disparate concepts under one label, which is already a perfectly good label that clearly refers to something else (according to everything besides throwaway's mysterious and uncited "vernacular definition").

> You think race realism is accurate but we should ignore

No, I do not think race realism is accurate. I just think that race realism is not an adequate defense of racial discrimination, AND ALSO that race realism is inaccurate.

Pessimizer and I agree.

> Many examples in the article don't fit your description of the problem at all

You asked me what I think the problem is, not what I think the article's author thinks the problem is. The fact that I emphasize different things is not proof that I misunderstand the article's author.

> The only thing that I argued is that the imprecise language used by Techcrunch is confusing people and that this is bad.

Who, exactly, is supposed to be confused?

Subject matter experts should know the difference, and if they don't, that's really their own problem.

The public at large doesn't even know what statistical bias is, so why in the world would they be confused?

No matter how confusing the current state of affairs is, it would be far more confusing if suddenly everyone starting using bias to mean statistical bias. Mostly because 99.9% of the people using the term wouldn't have an adequate grasp of mathematics to even know to which object they are referring. So if your priority is clarity, then keeping around the messy moralized and politicized vernacular notion of bias is far preferable.

> I didn't argue in favor of or against your mysterious "vernacular-discrimination" - how could I when I don't even know what it is?

Not everything in the world can be mathematically formalized. Most things don't even have fixed meaning over time. If your criticism is genuinely about imprecision, you've got a lot of much lower hanging fruit than "discrimination" and "bias"...

The precise problem is that things like skin color etc. often have strong predictive power...Notice that an algorithm can exhibit this behavior without being (statistically) biased. - throwaway729, 2 hours ago https://news.ycombinator.com/item?id=13159539

No, I do not think race realism is accurate. - throwaway729, 22 minutes ago https://news.ycombinator.com/item?id=13160709

Whoops!

The fact that I emphasize different things is not proof that I misunderstand the article's author.

Fine - what do you believe the author thinks "bias" means? I.e. what is the underlying concept behind all his examples?

Who, exactly, is supposed to be confused?

Pessimizer, for one, who thinks the problem is inaccurate predictions. 2 hours ago you disagreed with this, but 22 minutes ago you agreed. I'm getting kind of confused.

If your criticism is genuinely about imprecision, you've got a lot of much lower hanging fruit than "discrimination" and "bias"...

Sure, but this is my field. I give talks about misuse of p-values and the multiple comparison problem, even though there might be other issues worth worrying about. It's just what interests me. The fact that bigger problems exist doesn't mean we shouldn't fix this one.

> Whoops!

I take race realism to be the sort of thing that posits an inherent relationship between skin color/other physical features, and other innate/intrinsic/genetic properties such as intelligence or criminality -- especially as a prior and when the trait is normative and negative. So "dark skinned people are less intelligent" is race realism, but "dark skinned people are treated poorly by the political establishment" is not race realism. (We're far afield, I don't mean for this thread to devolve into a definition or debate about race realism -- I'm just saying what I take race realism to mean.)

The important point isn't a precise characterization. Rather, the important point is this:

It's possible for race to be predictive without there being inherent differences between people of different physical attributes. Setup a system in which I take away all green people's life savings at the end of the day with probability .99 if they don't have a house and .5 if they do have a house. Both numbers are .2 for purple people. An unbiased lender can choose not to lend to green people without exhibiting statistical bias -- in fact, color has huge predictive power.

But that predictive power comes from the social system we setup specifically to screw green people, not from inherent differences that are causally related to color posited by race realism.

But this is an extremely obvious observation, so perhaps you meant something different by race realism? In any case, I don't think the disagreement you posited really exists.

The rest of the points you're making are covered elsewhere.

I deal with Real numbers every day, but I don't barge into discussions of epistemology and demand my axioms be used in discussions of a vaguely related but definitely different topic. And I'm careful in pop lectures to point out that Real numbers are just a mathematical object and no more more "real" than natural numbers.

If people in your statistics lectures don't know what bias means, then you should account for this in your teaching and lecturing. I know my statistics lecturer did.

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