skysight-inc/HackerNews huggingface dataset:
How do you de-bias datasets, when the raw data (coming from the real world) starts biased to begin with?

For example, there are already attempts to use expert systems in the courts - to "scientifically" determine flight risk (and thus bail), and even chance of re-offence (and thus punishments). Those are trained on datasets of convictions. But we know that both conviction rates, and arrest rates, are themselves indicative of bias - e.g. some people get arrested more often for a crime, for which other people might get a verbal warning from police.

So ML takes those biases, and entrenches them further. Worse yet, because people tend to believe that "computers cannot be biased", the fact that it's ML that's producing a specific result, is taken as prima facie evidence that said result is immune to criticism from that perspective. And with most advanced models, you can't really crack it open and make sense of what's inside - so you can't easily prove that it is biased.
Qualcomm made a modern smartwatch chip: Meet the Snapdragon Wear 410
I watched the thread and LeCun did a pretty thorough explanation of why his argument was more complex than "the dataset is biased"

You can see in the responses however that many people were dissatisfied with his response.

[1] https://twitter.com/ylecun/status/1275162528511860737
It’s not as though the designers of the system set out to train it on a biased dataset; we can assume they were trying to be balanced from the start.

And that’s the deeper problem here: “it’s just a biased dataset” is a misdiagnoses. It’s a whole system of biases that leads to people thinking they are training with balanced data when they manifestly are not.

You’re never really going to achieve this mythical “balanced training data” until you untangle all of the other implicit personal and organizational biases. There are a whole host of ethical discussions that need to happen to even begin to flesh out what “balanced” might even mean for, say, facial recognition software intended for use in law-enforcement, but the same biases that lead people to skip right past those discussions and begin training are often the very ones that result in the biased data to begin with.
Balanced how? It could be balanced to represent a population, but which population? The US? The university where it was created? The world?

It could also be balanced so that the evaluation metrics were similar for each subgroup (possibly ending up with a sample that's very different from the population). But what are the subgroups? For any commonly used definition of race, there is a lot of intra-group variety.

Maybe balancing the training data is enough. But figuring out what balance even means is a huge question.

I don't think there's a ready CS/stats solution to this problem, so it will require interdisciplinary engagement and listening to the people who have been on the wrong end of facial-recognition bias is likely a place to start.
Well, the "law" is more than what's written in statute; you have to merge that with case law. My understanding is that in Dowling v. United States, the Supreme Court specifically ruled that copyright infringement is not theft (specifically in reference to the section of US code that you link to, no less):

> Since the statutorily defined property rights of a copyright holder have a character distinct from the possessory interest of the owner of simple "goods, wares, [or] merchandise," interference with copyright does not easily equate with theft, conversion, or fraud. The infringer of a copyright does not assume physical control over the copyright nor wholly deprive its owner of its use. Infringement implicates a more complex set of property interests than does run-of-the-mill theft, conversion, or fraud.

https://en.m.wikipedia.org/wiki/Dowling_v._United_States_(19...
Culture impacts legislation, it's not really that surprising.
I think there's two different issues here.

One, if datasets are biased --- if you build your system to only work on white males --- then it may have suboptimal results for other groups. This is a common problem: you use your company's faces, or college students enrolling in data gathering exercises, etc, who are not representative of the population at large. We can fix this by being careful about dataset bias.

But the second issue hits right at the heart of a major societal problem/debate. When we use AI to make decisions about people, will the system become racist--- even with representative datasets? If you train something to predict, say, odds to default on a loan-- will it figure out things that correlate to race and be making mostly racial decisions? Different races do have different default rates, but we've decided as a society that it is unfair to use race to determine an individual's probability of default. But if we choose things that are correlates of both default rates and race, when is that fair and when is it just veiled racism (redlining)? What things are measuring a causal relationship and what things are just racism in disguise?

This second problem is much worse with ML, because we have the ability to accept a whole bunch more things into our models and explaining the rationale of why decision are made is much harder.

And of course, the first problem-- both bias in datasets during use and biases during research and development -- makes the second problem worse. It can't even necessarily be addressed by broadening the dataset and retraining: if, in this case, you do your research and training with just white faces, and report positive results, it may not generalize to work as well for everyone with a broader dataset.
Just browsing her Twitter made me cringe and I am a female PoC. Her response to every argument is "Ah yes the problem with the world is a PoC doing this and that" when the original argument hasn't even suggested that.

Recently seems in support of a black scholar who cried racism because her non peer-reviewed work that was only posted on arxiv wasn't cited in a lecture on GANs ...

Voicing these opinions would probably label me as racist in their book ironically.
You don't have to wage a war upon a broken treaty. China even complained that UK was considering giving path to citizenship to British National Oversea passport holders. Many things can be done. Point is, an international treaty is broken and it doesn't make sense to justify because "china is big and powerful", we need to hold China accountable (and US/UK/western countries definitely have the power to do so)
But how do you de-bias that datasets when the world itself is biased? You can't just ignore that bias in the real world because any ML will at best mirror that bias if not amplify it.

For example imagine some ML algorithm that predicts the potential value of a customer (or their likelihood to steal from you) as they walk in the door of your business. How do you remove the racial bias from a system like that when racial biases exist in society that result in the average White person being in a higher socioeconomic class than the average Black person? The ML algorithm will tell you White customers are more valuable than Black customers. It is doing exactly what you told it to do, but the end result is that Black people get worse service in your business.
At a certain point it becomes a national security issue.
Well, if the cost of riding a motorcycle is millions of dollars, then it must be that they act like curses in the real world; otherwise you can't explain people doing it.
Jesus. If I was subscribed to this, in my local currency this would cost me 347.61 moneys each month right now. Ugh.
One perspective is that humans weren't meant to communicate so broadly and with such low costs. The result is the emergence of toxic dynamics that are usually held in check by the difficulty of communication, which raises the cost of signalling. When signalling is cheap, as it is on Twitter especially, then signals lose value in the same way that currency loses value in hyperinflation. People have to resort to more and more extreme signals in order to get a reaction from the network.

By making highly viral communication easier, Twitter has done something equivalent to removing control rods from a nuclear reaction. The reaction is fine when it's under control, but if it reaches a certain level of intensity, then different and highly damaging behaviors can emerge.
Mildly questionable and actively stupid. They have a multi lane road and force the one lane with seated protestors open. I am not sure that there is any context that could explain that choice unless the police is actually prohibited from routing traffic around an obstacle.
From the author's github[1]

> We have noticed a lot of concern that PULSE will be used to identify individuals whose faces have been blurred out. We want to emphasize that this is impossible - PULSE makes imaginary faces of people who do not exist, which should not be confused for real people. It will not help identify or reconstruct the original image.

This is just another way to generate realistic faces that match a pattern. The "bad use cases" I can imagine involve unmasking real people, which is not what the model does.

[1] https://github.com/tg-bomze/Face-Depixelizer
If you put it on earth, it has to survive whatever renders the earth uninhabitable. Given the very large number of potential bad things that could happen, something that can confidently survive all of them would be very difficult to make.

If it's on another world, it is obviously already in a pretty inhospitable environment that is expensive to access, but that environment is unlikely to get catastrophically worse at the same time as the earth, and the costs of building it can be reasonably estimated.

If you take out a 500k life insurance policy, if you take into account the time value of money it will on average cost you more over the course of your life than the policy actually pays out. You are technically losing money by having the insurance. However in exchange you put a known upper bound on the amount that you lose as opposed to the person who makes the rational choice to forego the policy and assumes unlimited risk. Likewise beefing up the one basket we keep all our eggs in is almost certainly more economical than getting another basket, but it is inevitably still a single point of failure.
Yeah well no shit, if your dataset is _exttemely_ imbalanced, you're gonna end up with predictions that lean towards the majority class, so to speak.

Doesn't mean that the model is broken or prejudice. It _does_ what it has learned to do.

If you want to fix the model by taking into account the said imbalanced data, that's one thing - but the dataset still remains the same.

Prejudice or biad would be to build a dataset by improperly cherry-picking your data, or other sampling errors.

Don't need to be a Ph.D to see or understand this.
If you're on a linux server, it almost always has vi(m). It's worth learning for that reason alone :)
"the maximum amount I could theoretically spend on games per year? $120"

For someone who complains about the quantity of my supposing, it's strange that you are supposing counterfactuals explicitly rejected by the OP.
Why does everything need 50 disclaimers before anyone can say anything.

Shouldn't "I care about ethics" just be assumed? How many people do you know who would say "I don't care about ethics"?
It is unethical (and, in many places, a crime) to sell a firearm to somebody whom you know, or reasonably suspect, intends to use it for criminal purposes.
Is that attenuated by making the system "blind" to race in those decision-making processes?
Scrum is being applied like a shiny new hammer to everything that might have seen or heard of nail a once.

The primary reason scrum should be used for software development is when iterating closely with a knowledgeable customer. The pattern basically outsources the discovery of what to build onto the customer - and if the customer can tell what good looks like, shazam! scrum is downright magical!

Guess what, most software teams are isolated from the customer, or the customer/product owner doesn't know what good looks like, so no wonder scrum is getting a bad name as teams struggle to get value out of scrum activities.

There is a reason why teams need to have flexibility to modify their R&D patterns and processes. Without that flexibility you get to do scrum and the results make you wonder if you are doing it wrong.
My understanding is that in Dowling v. United States, the Supreme Court specifically ruled that the very section of US code to which you link does not mean that copyright infringement is theft:

> Since the statutorily defined property rights of a copyright holder have a character distinct from the possessory interest of the owner of simple "goods, wares, [or] merchandise," interference with copyright does not easily equate with theft, conversion, or fraud. The infringer of a copyright does not assume physical control over the copyright nor wholly deprive its owner of its use. Infringement implicates a more complex set of property interests than does run-of-the-mill theft, conversion, or fraud.

https://en.m.wikipedia.org/wiki/Dowling_v._United_States_(19...
Everyone thinks they want a la carte, but (most) everyone is wrong. Bundling is generally better for consumers and producers.

https://cdixon.org/2012/07/08/how-bundling-benefits-sellers-...

Stratechery also has written about this
"a case report". Nuff said. Nothing to see here, move along.
hey.com's bullying worked on Apple, I guess their hoping it works on you too. Keep your chin up.
Not a reputable journal, as far as I can tell:
https://www.sciencemag.org/news/2013/11/open-access-group-sa...

Meanwhile the sole researcher's entire career seems to be based on promoting microbiome changes as a cure-all for various ailments. I am nothing like an expert but this doesn't pass the smell test:

https://progenabiome.com/our-story
I pay for YouTube Red because I can skip through ads. It's worth it.

In YouTube TV you get to see unavoidable ads, unlike in TV where you can switch the channel while those ads are running.
Mine is v% :)
My experience in ML and computer science is that it cannot be assumed. Consider companies like Palantir and their Gotham system. Consider the facial recognition systems deployed in Detroit, which fail 96% of thee time. It especially cannot be assumed when you attempt to offload that responsibility to other people.

Computer science programs around the country promise their young, bright-eyed undergrads that they'll change the world. Very few of them teach them the ethics they'll need to do that in a thoughtful way.

The assumption that science and technology is inherently ethical has unfortunately led to dangerous ideas over the past 150 years. Some, like geographic determinism and eugenics, have directly led to the suffering of millions of people. I hope we tread carefully and take action when we see harmful models. [0]

[0] https://twitter.com/SpringerNature/status/127547736519656652...
A variant of this is that both ppl are answering the same question but on different time scales. So one is pro (on a generational timeline) and the other is con (on a 4-year timeline).

Both might be right, for their respective time scales, which is why it's so hard for either one to back down.

Many many ways to talk past one another. We need a small book on techniques to short circuit these situations and salvage conversations.
Every Day News
Maybe Twitter isn't uniquely the worst at this but it's certainly in the top 5.
LIGO and Virgo detected a collision between a black hole and a mystery object
I resonate with this as well. Anything strongly spatial, such as tying knots, navigating really any sort of space, even after just a single visit, and things such as sports come very easy to me. But even visualizing an Apple in any capacity (as an arbitrary example) seems to escape me, And asking me to, for instance draw one, would rely on a list of facts that I know about apples:
* Apples are red
* Apples are generally spherical
* Apples have a stem on one side, and a little flowery bit on the other.
* There are indentations around those bits.
* An Apple's shape is a bit heart-ish.
* Bits of the apple are bumpy when as swept around it's primary axis (I.... think only on the bottom???)

Maybe that gives you a sense of my ability to visualize.

While in stark contrast, I can often take a few looks at an already tied knot, and reverse-engineer/decompose it's construction down to a series of steps that I can feel spatially I can then often, much later be able to reconstruct that knot based on the combination of the few bits I remember, and the general knowledge I have about space in general and knots in specific.
Deciding what is fair and what is biased is a political question that impacts a lot of people who aren’t software engineers and it shouldn’t be up to them to decide.
At a moral absolutist standpoint: yea, absolutely. Whether it's harmful or not, the intent is still to avoid paying for what you got, which is pretty straightforward.

For almost any other positions: it's murkier. If you take a berry off a bush on public property... what's the morality? Or growing from private property overhanging public that's just dropping fruit and the owners are either not present or clearly allowing fruit to drop? It's theirs, they absolutely could harvest it, but they're not and it's clearly implied that is not going to change. Now what?

Yes that's still further towards harmless than you can likely argue software piracy is (... ignoring the network effects of everyone knowing how to use X because everyone pirated it, or I could probably come up with others), but my point is that strict black and white separations have dividing lines and breaking points. Whether there is black or white somewhere isn't really useful to anyone, it's where you draw the line.
There is really no way to know or to say with logical justication that a data set, or the deep learning model that results from it, is free from bias. That's the issue at hand. The social dimension of the application must be discussed broadly to make any sense of it. It's a matter of opinion, and properly so. I worship the guy and I think he was attacked for things he didn't say, yet if Mr LeCun had acknowledged the limits of dnn black box models, it would have been helpful for everyone following along. Timnit Gebru should also have made the same point.
Whatever. I'm sure LeCun will be dying for another job.

Gebru undermines her own cause by being a shitty ambassador. While asking for deep empathy in a radically marginal area of inter-sectional study, she seems to lack any of her own. I'm sure berating an adult for a technical assertion - one that is prima facie true - is an excellent tactic that has worked to change minds and open up insightful public discourse.

Gebru's commentary smacks of narcissism. Want a little air time and some notoriety? Call a famous person a bigot and see what happens. She has little to risk and much to gain.
As an engineer

> files or objects, tweets or 5TB videos

means rsync, curl, and Twitter API integration. It obviously does more than this, since I can throw something together that does that in a few hours. Where is the list of everything it supports?!?

That should be front and center.
Bold of you to assume we'll reach the long term.
California accuses Cisco of job discrimination based on Indian employee's caste
Because ML models don’t exist in a vacuum. The intentions and biases of the people who build and use them affect which models exist, and how they’re used. Creating a perception that models are unbiased mathematical oracles because the dataset is unbiased can be used to support harmful uses.
Yes, but certainly guns are designed to kill people. Just like an AI.system might be designed to catch "criminals"
When cops get to purge their own disciplinary records and keeping their misconduct secret; they don't get to be called 'heroes'.

They are a tool of social control only interested in the perception of truth, trust and transparency. Pushing whatever narrative serves their interests.

https://www.azcentral.com/story/news/local/arizona-investiga...
It's pretty hard to make the system "blind" to race. If you blind a system predicting recidivism to race, but it is able to see a lot of things that are correlates of race... it can end up being racist anyways.

E.g. for a non-ML example -- redlining is theoretically "blind" to race, but makes extremely racist decisions.
Because at the same time they've struggled to move past the Gecko engine and create a product that can reasonable keep up with Google Chrome. It was the wrong choice made at the worst time, wasting time and resources.
This is the business model that sunk the cable TV and opened the market to netflix, hulu etc. The fact that a Silicon Valley company made a fancier app with smarter DVR capabilities will not save this doomed business model.
Yeah, that's intentional. It's a reference to an old comedy sketch.
What are some examples of racial bias in ML models which cannot be solved by just changing the training data?
The problem isn’t social media, the problem is unmoderated social media that uses engagement metrics to drive their algorithms. They’re selecting for the most hateful, divisive content.

It’s YouTube, it’s Facebook, it’s twitter.

Those three sites are the problem. Reddit has a lot of hateful bullshit but you need to go out of your way to find it. YouTube and Facebook feed you a heroin drip of it all day long.
I heard way worse stories than this.
So you can't trace a picture using tracing paper?
I looked around but didn't find it. Thanks a lot.
In my case, I get 1 Gb/s (symmetrical, no data cap, and they gave me the modem/router) internet from CenturyLink for $65/month, and we decided YouTube TV was the best choice that gave us local channels (not their subchannels, darn it, and CW is still "on demand" rather than live from the local affiliate), which is important to us because the apartment we're in is the worst possible place to have an antenna. Even with the YouTube price increase it's well under what Mediacom was socking us for after the first year for 60 Mb/s down, 10 Mb/s up, tightly data capped internet and an admittedly wider choice of TV, so for now we'll stay (but start researching alternatives). (Sports? I have the Rhett Butler attitude towards them.)
"Netherlands became rich not poor while adapting to life below sea level "

LOL, we can all relax and look forward to a prosperous future then.
As a POC, it is exhausting to have folks like Timnit spin everything into a problem devoid of a solution. I hope we take a scientific approach to problems and solve it rather than driving away folks who can help in solving the problem.
YouTube TV has nothing at all to do with YouTube proper. It's purely branding. TV is comparable to Sling or DirecTV or your local cable TV option.
There are quite a few remnants of the Whaling industry around NZ... it's clear that one of the major reasons was they were so damn near extinct it wasn't anywhere close to energy profitable to hunt them anymore.

For some phantasmagorical stubborn up yours world, various nations continue to hunt them. Sea shepherds have been fighting that sort of crassness.
I used Vi, and then Vim, for a good 15y before I switched to Emacs. I made the switch because I had heard that Emacs let you manipulate your buffers with lisp as one would any other data in a programming language. I find I only really do that when using Org Mode or REST mode, but I'm still glad that I made the switch. The editor is ... mine. Mine in a way that a professional's tool should be an extension of themself.

I've been thinking of trying evil, but I don't really miss the modal editing as much as I thought I would, and it's been another five to ten years.