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.

Don't worry, Twitter isn't real life. People have become experts at shouting down people who disagree with them on that platform, they aren't seeking proper arguments and make disingenuous attempts to present them as honest debates.

But fortunately what's popular on Twitter doesn't translate to the average population. Plenty of completely fringe ideas get 50-100k likes/retweets. It mostly just represents the voices of various super-niches living in bubbles.

If you're lucky and the offense is mild you may repent, as it was suggested to Yann: https://twitter.com/le_roux_nicolas/status/12754857390238187...

I wonder how often apologies like this are genuine, versus simply bending the knee to the mob out of fear for one's livelihood.

Small but highly vocal groups can have a seemingly loud and powerful voice. Yet the results of polls and other public signals (even election outcomes) are frequent reminders that what is gospel on Twitter is often detached from 'real life'.

Using a president of a major country is a poor example in this context. But if anything Trump being one of the first major Twitter users strongly reinforces my point. Prior to election he tweeted plenty of things most mainstream US republicans wouldn't touch with a 10 foot poll. Let alone what an average American would say IRL (even right leaning ones).

Not to mention Twitter is a global platform so conversation around local politics can be heavily skewed by people not even in the country.

But otherwise I agree, it is infesting real life far more frequently these days. And it is worrying. Despite everything I said above, big corporations, the media, politicians, etc can't seem to make this distinction and take what is popular there as a direct reflection of the general public. And it creates a negative reinforcing spiral.

It made me feel very uncomfortable, because I wanted to say something like "the issues you are pointing out are very important, but I don't think you are right in this instance" but I was afraid doing so might jeopardize my career at the company.

For the sake of ethical R&D, it's counter-productive to build a hierarchy of investment into the problem. Admittedly, the responsibility of end-results can differ, but the consensus that this ethical work is important should ideally be universal. That said, there should not be some ivory tower where you wash your hands of the ethical problems of your field.

Ok, so what is the solution then? Does she have a concrete set of steps or goals to address the problems she sees? Is there a list somewhere of things that would appease her, and in her mind make ML fair? Honestly interested.

When it comes to problems, particularly in complex subjects which aren’t yet well understood, and where there aren’t yet an overabundance of high caliber researchers, this comes across as dismissive of the problem.

This can be even more concerning if we know there are investors lined up who will happily sell something to the world and who will intentionally hide or minimize known ethical concerns. And then play dumb and shocked later when the very same problems manifest.

I see this dismissal of justifiable and real concerns an awful lot in conversations of all kinds lately.

And from what I’ve seen, no one here is anti-ML, no one on either side here is a luddite. But like so many conversations online, we should quit talking past each other and likely need to quit trying to paint people as if their concerns don’t have very real ethical implications which will, if left unaddressed, manifest in all kinds of negative ways throughout society.

Again, a lack of a neat and tidy solution doesn’t mean the problem doesn’t exist.

As for Timnit and LeCun: a user above notes that the epistemological frameworks they're using to analyze this problem are not aligned. I found that comment pretty eye-opening, honestly.

Shouldn't "I care about ethics" just be assumed? How many people do you know who would say "I don't care about ethics"?

Because it identifies which team you are on. People only recognize the existence of a nuanced point if it's made from someone on their own side.

This is purely a power game in which some individuals have managed to blackmail everyone else into recognising their role or being cancelled. Now and then someone prominent needs to be attacked to remind the others what they risk if they don't fall in step.

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...

I think that the underlying assumption is that science and technology are inherently neutral (which isn't true) and that neutrality would be inherently ethical (which also isn't true).

But I didn't say that science and technology are inherently ethical. I said that most people care about ethics. They might have different priorities or philosophy than you, but almost nobody commenting on a social issue is doing so because they want the immoral thing to happen. Right? So asking everyone to say "of course I care, of course" before everything they say is laborious.

Specific recommendations start at 17 min.

This is an extremely unpleasant position to take, if your point of view is empowered within the status quo. It is much extra work for no discernible benefit to the researcher.

If your point of view is subject to disproportionate suffering under the status quo, then reinforcing current practices by implicitly enshrining them in input datasets will make improving your situation even harder.

As an example, consider the case of public school funding. In a hypothetical system where school resources are provided proportionally based on student success, good schools will thrive and bad schools will get worse. If someone points out this isn't fixing the problem, you can reverse the proportions -- this will cause good schools to suffer while bad schools will get additional funding (disincentivizing student success). In cases like this, it's not enough to just have a purely abstract set of metrics on which to base resource allocation: it will always require actual investigation of why good schools produce good results and why students do poorly in specific schools.

This is sort of obvious, of course, but it isn't being translated into terms that some researchers can or will grasp. It's never enough to just tell someone to 'debias the dataset,' as determining that bias is a monumentally difficult challenge that people have failed to achieve for many generations. A key factor in fact is the propensity for this kind of research to get deployed, today, by people who are not experts in a given domain of investigation, with possibly disastrous results in policymaking. These tools are not abstractions that require a team of experts to translate from research paper to the real world; ML researchers put out results that you can shove into your nearest computer and run.

What Timnit and others are getting at is that it requires thoughtful and careful assessment to get real value out of this sort of research. Ideally, in Timnit's assessment, the researchers themselves would put effort into identifying possible calamities and put as much effort into mitigating them as they do into publicizing the work itself.

Yann LeCun and other researchers simply do not believe this is their responsibility; all they want to focus on is the mathematics themselves. I'm sympathetic to this position but I also very much do understand the opposition. One of my favorite movies from childhood, "Real Genius," deals with this sort of issue as the main plot line.

The problem here is that many stakeholders really really* want to reduce the required intervention to a mechanistic increase/decrease of some sort, never mind that different stakeholders want opposite interventions.

No matter how "intelligently" you go about it (whether the intelligence is natural or artificial), this desire to simply boil down policy making to reallocating resources or incentives/disincentives is fundamentally lazy. It reminds me of the mania for diversified conglomerates and corporate management of the firm-as-portfolio (ie. reducing management's function to determining financial allocation between corporate units and deemphasizing the need for operational knowledge) during the 1970s.

I don't think this criticism is fair. Presumably if someone with the title "researcher" has a hand in actually doing what LeCun consider to be the engineer's role, LeCun would say to treat them as an engineer for the purposes of his argument.

>For the sake of ethical R&D, it's counter-productive to build a hierarchy of investment into the problem. Admittedly, the responsibility for end-results can differ, but the consensus that this ethical work is important should ideally be universal. That said, there should not be some ivory tower where you wash your hands of the ethical problems of your field.

Suppose you're correct, should we define this threshold based on just LeCun's heuristic for defining it? Or would it likely be better to have a consensus that your role doesn't matter in acknowledging the important of fairness and ethics?

Would you prefer the world's most prolific researchers being mindful of these issues, even subconsciously? Or to care less, perhaps very little, because it can be deferred to engineers?

Because I would like for the field to unite against building harmful systems and to acknowledge the importance of this work throughout the academic hierarchy.

As for the ethical problems. Are you familiar with machine learning methods? It really is all about the data; that's not a dismissal, it's a fact about current technology. There are other kinds of A.I. which are not based entirely on raw data like this. Machine Learning has been simplistically described as "curve fitting", which I think isn't a bad description. So here you are arguing that the mathematician researching good ways to fit a smooth curve to a series of points in really high dimensions needs to somehow take into account what those points might represent in someone's use of the technology. It seems pretty unreasonable to me to require that they put ethical constraints on it.

I'm not advocating that purely theoretical work of every form should be focused on algorithmic bias. Certain realms of theory (adversarial and robust learning, deployable model theory, computer vision and NLP) lend themselves much more directly to societally-relevant bias than other realms (algorithm and complexity theory, hardware research, performance research, pure statistical learning theory). I work on faster graph neural networks, so I'm actually in latter group.

So? I just want everyone to be on the same page on the importance of work in algorithmic bias. I've seen people dismiss Gebru's work as "pure rhetoric" on Reddit - this is a cause for concern! Acknowledging the validity and importance of similar work is especially important for people who are leaders in the field and who have influence over priorities. Don't people on HN complain about FB's algorithms literally every day? Shall we forget the Myanmar incident?

Let me put it this way: in biology, Watson and Crick were researchers who participated in discovering the structure of DNA. For most of their careers, they were not practitioners. However, James Watson made a huge negative impact on the field by advocating against woman in science and advocating for eugenics (completely ignoring Rosalind Franklin). Setting an aggressive and toxic tone in genetics paid dividends during the Asilomar conference (1975), where reporters and scientists who were critical of big-name organizers got de-badged and escorted out of a conference on ethics.

Our leaders and researchers matter - let's not make the same mistakes. The message should be: "I might no longer tool in TensorFlow, but I care, and so should you." It was easy for Jeff Dean to do that, which I thought was awesome. No senseless purity testing of engineer versus scientist. I think small steps like the new NeurIPS broader impact statement are heading in the right direction.

Edit: I just realized this reads like I'm equating LeCun with Watson... that's not my intention. That would be incredibly insulting, my apologies. I just needed an example of leadership having ripple effects throughout a field. Mea culpa.

I am personally of the opinion that it is fundamentally impossible to advance technology in a one-sided way. Anything with the power to do good can do evil too. Power itself is the danger. There might be a logical proof of this somewhere. Step very far back, and try to describe what a technology like a ML algorithm provides to society: software that can perform tasks as well as a person? discriminate between similar things using noisy observations? extract information that is obscured? The technology which accomplishes this can always be used both ways.

BTW, Watson didn't ignore Franklin- her name is listed in the W&C Nature paper as providing data. What he said in his book was much worse than ignoring her.

which adds a ton of color, and also kind of supports the problem with overly enthusiastic science reporters publishing things irresponsibly early (covid reporting is a good example).

Except for the part where it provides an actual workable solution to the problem at hand.

To me this is an argument between completely different mindsets, one that restricts itself to provable facts and one which restricts itself to political agendas. I don't see how the latter can also work in facts. Or belongs in a technical research discussion at all frankly. You want to make laws that force companies to produce identical/equivalent outcomes for every race somehow? Just go lobby for it. Perhaps it's a good idea. You aren't going to reprogram mathematicians to think in political terms instead of mathematical terms.

Even if sincere good faith and he has real expertise is assumed that approach kind of raises several "huckster alert" red flags.

Sure, you can look at it that way if you like, that commonly results in hiring someone to be responsible for D/I without actually making any other changes.

A better response is more along the lines of "Not in MY Army" which makes it everyone's responsibility at every level.