At you the commenter yourself: you seem to be exceptionally harsh on her based on the most minimal information that you’ve purposes to fit your claim of this being “useless things that we shouldn’t care about”. Do you know how much energy goes into training a model? I certain didn’t. Do you think that showing that AI is picking up discriminatory language is something worth looking into? I certainly think having someone look into it would be useful, yes. And Google apparently thought so too. Claiming that these things are idiotic and attacking the author of this paper for bad faith and censure is…very extreme.
Twitter already exists, we don’t need another place online to spend all day spreading false positivity.
A comment that started off with “it seems like Timnit Gebru mat have overestimated the impact…” is not false positivity.
If it is accurate what does it matter?
One final edit, even her co-authors were not ready for publishing the paper (per article), so why is the reason of censorship, supression etc even being discussed. This whole thing has been blown out of proportion and Google likely did the right thing, purely based on her behavior
https://twitter.com/ylecun/status/1277372578231996424
saying:
"Following my posts of the last week, I'd like to ask everyone to please stop attacking each other via Twitter or other means. In particular, I'd like everyone to please stop attacking @timnitGebru and everyone who has been critical of my posts."
I also find your question about why people attacked her, as if people in the internet always have good reasons to attack others, is an incredibly silly attempt to appeal to the Bandwagon fallacy.
Now go and look at how she has handled it and how she argued with Lecun. Then she also thratens people if she does not get her way. There is ample evidence for her behavior. Does she behave like this all the time? Of course probably not. But the fact that she is openly behaving like this is a clear data point. If you can not see this behavior as not suitable in a work environment, then lets hope we never work together.
So yes, there is more evidence for her toxic behavior than there is for her ability deal with disagreements professionally and work in a team environment.
TBH, I think this whole review things is just a reason for google letting her go. They were probably looking for a reason and she using her threatening (aka toxic) nature was just what google needed.
The people who removed her are executives several skips above her. That is unusual.
You keep bringing up LeCun, but LeCun did not get harmed as you imagine - he only conceded to her point because she was frankly the expert in the topic of discussion and he finally acknowledged that.
Because thats not how I have seen it. She threatened to leave. If any of my team had an issue, and their response 'Do X, Y, X or ill leave', even if they are in the right, I would not align with them purely out of the fact that that is not the right behavior. You can still leave any company with out threats.
Also we have not seen the email to the brain group. I wonder how that compared to her exchange with Lecun and her threatening nature.
Lecun was not harmed? How did she contribute to a healthy debate there? Please also link me. Because all I saw were aggressive tones towards someone she disagreed with (even if she was in the right). Again, he behavior (not if she was right or not) is the problem here. You can be right, but still be an asshole.
Also, how do you know some people in her team didnt have problems with her? Do you have here perf reports? I believe her direct manager is not the only person that can fire her. She can be ok good terms with her direct manager and still behave unacceptably towards higher ups
2. How was LeCun harmed? As far as I'm concerned, he accepted the feedback. And just so we're clear on what happened then - Gebru was only one of many with qualifications who were aggressively critical because of his abdication of research responsibilities. I don't know why you're so focused on Gebru in particular.
3. Yes, if this was about performance, her direct manager who oversees 300 people would know.
4. So yes, glad to see we agree higher ups, who she likely has little to no interaction with to supposedly be exhausted of her behaviors, stepped in to get rid of her.
2. Do you really think lecun wouldn’t have left Twitter if he didn’t find that conversation enjoyable? Do you discount the many other people who found her interactions with lecun horrible? Did any of the other people behave as arrogantly towards lecun as she did? Again, no point made.
3. See 1
4. See 1
So, you can either stop ignoring the fact that her own behavior led to this, or you can continue to think many other respected people in the industry who act way more professionally than her are just against her because they don’t support her views or think google is racist.
You are the only person postulating the intention of her firing. I am merely calling out your assumptions because the consensus from the entire community involved has declared otherwise.
Most other people who found her refutations horrible are outsiders who don’t fully grasp the topic LeCun and Gebru and everyone else was alluding to.
Who are the respected in the industry that are against her? Not even LeCun, who has said he thinks her work is important. Nothing in your post has facts at all.
You dont have to grasp a topic to know if someone is debating respectfully. I dont have to be a doctor to know that the person is an asshole if they act like an asshole towards me.
I think lets just agree to disagree. Focus on the behavior. Not what she is writing about in her papers. And imaging someone acted like that towards you in a workplace. Imagine you are a CEO or a manager and someone comes to you and says 'Do x y or z otherwise im leaving'. Id be like wtf, sure, if thats how you deal with disagreements, cya'. Everyone is replaceable especially toxic people.
How can you objectively comment on her behavior if you have never interacted with the person ever?
This has been discussed in prior threads. A manager is well within his rights to act upon his decisions emotionally. Employees are well within their rights to express concern. If I work in a workplace with severe safety issues, I would most definitely demand an ultimatum for my personal occupational security. Managers DO NOT impulsively fire individuals unless they are mentally unable to navigate through a conflict. It's far more nuanced than that.
Driving to the library, that is the alternative to training these models, really?
> -- Ok, basically she is pissed as AI is picking normal language, that people use, and not using the vocabulary that is in vogue on certain political circles. Basically censure, and forced speech.
If you had a model trained on a large corpus of data from the pre civil war southern American states, it would have been deeply racist, and would even view black people as possible property. If you had one that was trained on data from the 1950 it would be less racist but still problematic viewed by people from today. Is there really something special with today, that removes these kind of concerns with a model trained with current data?
> I think social engineering b.s. should be kept as far away as possible from science. This is turning true ai research into a masquerade to push certain political agendas.
It seems to me that it would be impossible to do any social science research, and specifically any research on racism. With this kind of attitude.
Some of the concerns brought up in the paper seems less consequential than others. Especially the pollution one seems weak to me, that doesn't make it false, and fair enough that is was brought up. I find the racism issue a lot more problematic, and is something I've run up to working on deep learning solutions myself. Even if it worked fine for my group, and most of our customers, that is definitely not fun, and something practitioners should consider when building these things.
I think this argument applies not just to machine learning, but to learning in general. Any kind of knowledge-acquisition process is going to be biased by the environment in which it occurs. That goes not just for digital neural networks, but also those in our human brains, operating on the same racist data the ML models are. If that means we shouldn’t do machine learning, it also means we shouldn’t do human learning either.
Of course, the preceding is absurd. A more reasonable take is that we should adjust the objective function of our learning processes to try to account for the effects of biases. We try to do that subjectively as any decent person operating in a biased society should, but our ML models can do it more accurately. In fact, I’d argue that such techniques are necessary to more carefully analyze and build evidence describing the effects of those biases. They can provide insights that will even improve our ability to correct for biases in the real world.
I think this sort of veiled personal attack resorting to baseless extrapolation is not a productive line of public discourse.
You should take a step back and think about a) the field of study, b) how the main argument completely ignores the basis of said technical field and what it studies, c) how the argument being created lies on the idea that a self-annointed elite should have the right to manipulate the public to force it to fall in place with it's goals and desires.
I thought that my comment was fitting to the tone of the text I replied too, but fair enough, maybe I shouldn't have included that paragraph.
> You should take a step back and think about a) the field of study, b) how the main argument completely ignores the basis of said technical field and what it studies, c) how the argument being created lies on the idea that a self-annointed elite should have the right to manipulate the public to force it to fall in place with it's goals and desires.
It is unclear to me what this means, is it arguing that studying bias in AI and specifically deep learning is not germane? Is it arguing that it is not acceptable to institute policies and action to create a level playing field for minorities and repressed group because that would be social engineering, and forcing the common man to do the elites bidding? If that is what it means, I have to disagree, I find both of those things worthwhile.
Yes, that was one of the problems.
> It is unclear to me what this means, is it arguing that studying bias in AI and specifically deep learning is not germane?
Let me make it clear for you so that a) we are able to talk about things objectively, b) your options to continue using veiled personal attacks is curtailed.
Either your goal is to model reality and real life, or your goal is to model your idealization of what you feel real life should be.
If you pick option #2 then your model does not reflect real life.
Bias is by definition the way the model returns results that don't match the real world and real life, in frequency and in proportion.
If your goal is to use your model to manipulate and control society based on your own personal criteria, by manipulating it to return results that distort the real world and real life, then call it something else, because bias is not it.
In the context of creating models, it's representative data collected from statistically significant observations of a population, for starters.
> In cases where some communities/countries have (to quote from the article) a "smaller linguistic footprint online", is trying to control for this a form of manipulation of society or a way to fix a bias?
In modelling there is no such thing as control. There's the input data and there's the model generated from input data.
If you are looking for a model that is expected to represent a property intrinsic to a specific community then you use data collected from that community to generate that model. That's it.
Models generalize, and reflect the norm. They are like that by design. That's their point.
If your plan is to have a model that does not reflect the input data but instead forces your biases regardless of the input data then your goal is not to model reality but to distort it to comply with your personal goals.
> If you pick option #2 then your model does not reflect real life.
These deep learning models built by corporations are not scientific models, they are engineering solutions, built to solve problems. Reflecting the real world is only useful if it furthers what the company wants to solve. If they for example remove swear words from their training set, that will make them a less accurate model of the world, but make them more useful for building solutions. But it is probably a trade of they would be happy with. We've also seen example of risk scoring application for felons that seem to end up doing racial profiling, because that is what the data seem to indicate makes sense. But that's deeply problematic and runs counter to laws in some places and seem ethically problematic (https://www.theverge.com/2020/6/24/21301465/ai-machine-learn...).
> Bias is by definition the way the model returns results that don't match the real world and real life, in frequency and in proportion.
Getting a good data set without bias is hard, even if you crawl the whole internet like Google does. Not everything is on the internet and there are systematic drivers that make some part of the human condition over represented (English, science, the views of the affluent and educated), and some under presented (small languages, the discourse of people behind the Chinese great firewall, the poor). So just getting a ginormous data set does not fix bias.
> If your goal is to use your model to manipulate and control society based on your own personal criteria, by manipulating it to return results that distort the real world and real life, then call it something else, because bias is not it.
Positive bias is absolutely something that we use, and while it might seem sinister it does not have to be. The example I'm most familiar with a facial recognition technology. Most groups building that ends up with a model that is better at some groups than others. Asian research groups often end up with models that does well with asians and worse with whites, while European groups usually end up with the reverse. In some sense these results do reflect the reality of these groups, most people in Europe is white and most people Asian is asians, so that you training sets ends up like that is not surprising. But no one is happy with these kind of results, and everyone wants to fix that.
To bring it back to speech and text models, let's say you are building a customer service solutions incorporating a deep learning model, the reality might be that you current customer service representatives treat blacks (or people who use "black" dialects), worse than people who sound white. An accurate model built on this data set will then also do that. But is that acceptable? I hope most companies would want to fix that, and be fine with adding some positive bias in their solution.
As opposed to the more direct “approach” in the parent post?
> Ok, so basically she is a bullshit social engineer, masquerading as an 'AI ethics researcher'
If the large language model analyzes everything, it will veer towards normal. It doesn’t seem so far fetched to think they can build something which also tracks and understands language trends and slang, and how to appropriately use this “special vocabulary”.
I don’t know much about how these models work, but I would hope something trained on the entire internet is smart enough to know to speak to me in modern English, not Civil War English or Shakespeare English.
There are all kinds of research papers; they aren't always describing some new clever trick no one thought of before. Sometimes it's just "hey, we had this question we wanted to know the answer to and so we did some investigation and some math and figured it out." As long as they're questions that some other people somewhere are asking, there's no reason why a respectable conference or journal wouldn't accept such a paper. It's up to them and the reviewers to decide whether the content is a good fit for that venue. If it isn't, it'll get rejected; no reason for Google to involve themselves in the decision.
How rigorously the authors treated these questions in the paper is something we don't know without being able to see the paper itself.
What do you suggest might the article be getting wrong about her paper? Or are you suggesting that the information contained about the paper in the article is inherently invalid and should be ignored altogether?
The dangers of large language models is an interesting topic but it’s not AI research and it doesn’t advance the state of the art of AI.
When it comes to technical substance in the field of AI, her paper is indeed lacking in that unless the article completely failed to mention something that would directly qualify as original AI research.
I also have to say that “AI” is a vastly different area of study than “ethics.” Very strange that Google has these organized in the same hierarchy.
She didn't... which are the hallmark of bad research.
You're reading a non technical summary of an article, and taking it as gospel.
I think I'm going to wait to see the paper before deciding whether or not it's good.
This was just glossed over and maybe slightly off-topic, but what kind of shifts in language did these two movements in particular affect?
Stuff like #MeToo was about raising awareness about sexual abuse, particularly in the entertainment industry, can't really think of how it forced people to change words that they use.
The best example I can think of is people opting to not use "master/slave"... Which is much more debatable and far off from "core tenets" of the BLM movement like police reform and dealing with systemic racism.
I guess what I am trying to say is that when it comes to language, things are a lot less black/white (heh) and we should be careful about people that are setting themselves up as arbiters. Sort of a who guards, the guardians issue I suppose.
Ground-breaking might be a strong word but it does move the industry forward.
Take for example a retrospective look at 2017 NeurIPS papers done in 2019 (https://archive.is/wip/77YrB).
You can disagree with how she and/or Google has handled this whole situation, but please do not denigrate work that has been cited (https://scholar.google.com/scholar?oi=bibs&hl=en&cites=14954...) by papers accepted at the most competitive/prestigious ML conferences.
EDIT: I also do not see how in good faith you can say that VentureBeat, a company who makes the bulk of its revenue from running conferences catering to C-suite execs who can shell out thousands of dollars for a ticket, is "leftist".
Most executives of tech and news companies would bend over backwards just to show how leftist they are in 2020.
(I’m only replying to the part of your comment that answers mine)
I don’t really care about the paper author’s fate, but it seems unsettling to me that she is discussed more than the paper itself.
Even someone like Ben Shapiro recognizes a difference between liberals and leftists (https://twitter.com/benshapiro/status/966081078166421504).
Silicon Valley types would hardly be described as leftists. Numerous studies have been done on the attitudes of Silicon Valley founders and execs (https://www.vox.com/2015/9/29/9411117/silicon-valley-politic...). The distinctions are dramatic.
We see that on average, tech founders are less likely to support vs. even Democrats generally (not just progressives):
* Banning the Keystone XL pipeline (60% vs 78%)
* The individual healthcare mandate (59% vs 70%)
* Labor unions being good (29% vs 73%)
This is to say, the average Silicon Valley type, particularly the C-suite exec or founder, tends not to be on the left wing of the Democratic party.
During the 2020 Democratic primary, even the Silicon Valley billionaires who are openly Democratic-leaning donated to candidates who were not to the left of the field (i.e. Elizabeth Warren and Bernie Sanders) (https://www.cnbc.com/2019/08/13/2020-democratic-presidential...):
* Eric Schmidt -> Cory Booker and Joe Biden
* Reed Hastings -> Pete Buttigieg
* Marc Benioff -> Cory Booker, Kamala Harris, and Jay Inslee
* Reid Hoffman -> Cory Booker, Kirsten Gillibrand, Amy Klobuchar
* Jack Dorsey -> Andrew Yang, Tulsi Gabbard
* Ben Silbermann -> Pete Buttigieg
I'm engaging with you in good faith, and because I was intrigued that in a previous comment you mentioned that you live in Spain (though who's to say you're not a US ex-pat). But calling US tech companies "leftist" is a stretch at best.
Your argument sounds like a red herring. Qualified for what? I didn't noticed the OP making any remark on the author's qualifications.
And by the way, you should think things through before pulling appeals to authority to try to defend the credibility of researchers. See James Watson, a nobel laureate and a founder of modern genetics, and his comments on race and gender.
Did you not read the first sentence of OP's post?
"Ok, so basically she is a bullshit social engineer, masquerading as an 'AI ethics researcher'"
That does not mean at all that the author lacks qualifications.
Pretty obtuse to suggest otherwise.
pretend to be someone one is not.
https://www.merriam-webster.com/dictionary/masquerade
"an action or appearance that is mere disguise or show"
As in social engineering masquerading as AI research?
Honestly, is there any room for doubt at this stage?
I don't expect honest and well-meaning people to continue insisting on this at this point. It feels that these attempts to distort what was said are simply attempts to attack the OP based on misreprentations of what he did said.
Cherry-picked? It's literally the first sentence, the central thesis of OP's entire "argument". If they can't get that down on paper correctly, they're not worth taking seriously.
> You would do well to work on your literacy and reading comprehension skills.
Ascribing unsubstantiated sentiments to someone's post in lieu of what they actually said doesn't fall under literacy or reading comprehension, sorry. It's actually the polar opposite of that.
Why are you claiming to know OP's intentions (despite their own words) better than everyone else here? Is OP your alt account?
> I don't expect honest and well-meaning people to continue insisting on this at this point. It feels that these attempts to distort what was said are simply attempts to attack the OP based on misreprentations of what he did said.
It's extremely bizarre that you're questioning the literacy of people when you're projecting your own unsupported sentiment to OP's post while simultaneously claiming that people who are directly quoting the OP's post verbatim are "misrepresenting" what OP said.
You're being very disingenuous here (as well as borderline manipulative) by trying to shut down any dissent against your flawed position by questioning the good faith of the people doing nothing but directly quoting the OP's words.
Amusingly, the OP got shirty at someone* further down this thread for calling them names, but with an unfortunate textual blunder which exposed the hypocrisy of their own name calling (not to mention total lack of self awareness).
I pointed this out but got downvoted to oblivion of course. Classic goose / gander politics.
* (and rightly so btw, that kind of conduct has no place on HN)
Your goal here wasn’t to make any useful point about how it was actually a reasonable decision for her to resign/be fired from Google. It wasn’t to comment on the process or difficulty of having these discussions at scale or in public, or even respond to the specific points raised in the article. These would all be valid discussions to have. It was specifically a personal attack based on a paper you haven’t actually read intended to demonstrate how much better you are than everyone else.
Absolutely done with this shit.
For example:
> Ok, basically she is pissed as AI is picking normal language, that people use, and not using the vocabulary that is in vogue on certain political circles. Basically censure, and forced speech.
I think a stronger version of this argument, which can appeal more broadly, would be: an automated system that is constantly being trained on data from social media can more quickly pick up new trends in human speech than a manually-programmed expert system. Furthermore, even if humans are constantly updating an expert system, the biases inherent in the composition of the company-hired set of experts will limit the representation of minority trends in the data. On the other hand, a model examining the entire data set can pick these up automatically. For example, I can easily get AI Dungeon (powered by GPT-3) to communicate using language and concepts that are only present in a minority community that I’m part of that I’ve rarely seen represented in literature.
When arguing against Timnit Gebru or their ilk, you don’t need to just give them the support of liberals. We actually make up the majority of techies, so it’s a bad position to put yourself in if you want your cause to succeed.
Exactly this. Framing it in terms of alternate options makes AI the more reasonable choice.
Google AI appears to have drawn unnecessary attention to themselves for relatively benign paper. Most of the arguments in this paper have already been discussed elsewhere and could be refuted in simple terms.
I think it is precisely for these reasons this debate is so important, because it is truly not a clear cut improvement in my opinion.
We call it “FU money,” but these guys aren’t satisfied with being able to say “FU” and still live comfortably; they want to be in charge of things that are seen as significant.
I believe it will always take more than money to get thousands of largely independent adults to do what you want.
Spot on. Naive scientism is used as cloak to advance political agendas, instead of searching for truth:
"When a domain becomes respected source of Truth, there is incentive for hijackers. There is a huge window of opportunity (from decades to centuries) until the public opinion figures it out. Many (not all) still trust Harvard credentials, as Cardinal credentials in the past." [0]
This is the trend for "Believe in Science", a magical catchphrase evoked to put trust in frauds like Steven Pinker and Paul Krugman:
https://books.google.com/ngrams/graph?content=believe+in+sci...
[0] https://www.academia.edu/26772813/The_Decline_of_Violent_Con...
basically: instead of reform, and fixing whats broken; to tear it down, as it is just another means of oppression.... (yes, that's the mindset of the movement).
Her research, looks to be mean spirited, and made to discredit google, so it can be blackmailed into whatever the ideology wants.
It is not meant to fix/improve the situation, but used as political amo. Hence it is just a political paper/propaganda, and not proper ai research.
I find the sequence of decisions made by Google problematic.
Until they describe in intimate detail what their management has been planning to filter they are effectively retracting all research from their institution as having this unknown bias. Like a tabaco lab that blocks all results against their products. That this was shoddy means nothing unless someone would have caught a Google positive piece as shoddy and actually stopped it at this process. I would guess no.
The right thing to do was let it be published, get external critique and end the relationship if most people with no money in the game thought it was bad science.
They didn't say they shouldn't make them, they said "prioritize energy efficiency".
normal language, that people use
As they said, language changes. Do we want a model that acts like the average person of the last 20 years? Or do we want a model that acts like a person who has just been through 2020?
You're free to use the model you believe is best for your goals.
I you use real world data from the last 20 years to generate the model then your model will reflect an amalgamation of the real world data collected in the last 20 years.
If you prefer to focus on last month then go ahead. You'll have less real world data and seasonal patterns will have a deeper impact and the model will be less robust, but go right ahead.
If, instead, you want a model that is not generated from real world data and instead just uses your own artificial data that is blatantly and openly manipulated to reflect your personal biases and political preferences then just call the spade a spade.
Please take a look at (Wieringa, R., Maiden, N., Mead, N., & Rolland, C. (2005). Requirements engineering paper classification and evaluation criteria: A proposal and a discussion. Requirements Engineering, 11(1), 102–107) available here: http://www.cse.chalmers.se/~feldt/advice/wieringa_2006_re_pa... Specifically read the section 3 (which is on p. 4). There you will find that research in software engineering can be broadly grouped into the following categories: Evaluation research, Proposal of solution, Validation research, Philosophical papers, Opinion papers, Personal experience papers.
While I agree that the usual way to deal with the criticism of the work is to redo the work with better evaluation (and thus, more strongly supported arguments), I think that in any ethics research you'd need more than just publications that fall under Evaluation research (which is what you are likely referring to as "true AI research").
What has this got to do with anything? How is driving to a library the "oposite" of AI?
We had very effective search engines for decades before AI.
The thing is: The language that people use is often enough offensive: racial slurs, sexism and other forms of discrimination, plus seemingly innocent "code words" like "globalist".
AI developers should know about these problems and keep them in mind while developing training data for AI models. The consequences of ignoring such bias is continuing discrimination - with the most obvious and low-tech reminder being the "racist soap dispenser" which had only been trained on white people's skin and failed to dispense soap for people of color.
The danger in such discriminatory models is that while it's an annoyance that a soap dispenser does not dispense soap for a person of color, with algorithms running more and more of our daily lives, the impact is way higher: whether being refused credit or affordable insurance rates because an AI "thinks" the applicant is black and living in a black, poor neighborhood is grounds enough for that, or "predictive policing" AIs sending in massive police responses to everyday calls, this directly hits on people's survival without human oversight.
That is why ethics in AI are so important and why "social justice activism" is so desperately needed!
It’s just basic intellectual honesty, sorely missing in the SJW circles.
Also, I believe it’s best not to accuse your interlocutor of lacking intellectual honesty unless the evidence is very clear. Perhaps they just see the world differently to you.
I’m happy about people viewing the world differently and having an honest, good-faith discussion about the issues. But I’ll criticise intellectually dishonest arguments, like the moat-and-bailey you just pulled here.
I never argued for that. My point is that researchers (as well as everyone!) have biases, which may manifest in the end as racism or other forms of discriminations. To prevent these biases from manifesting, (AI) ethics experts are needed as oversight.
> the company that employs them is racist
To take the example of the "racist soap dispenser" again: while the individual people who have developed it may not have been racist, they have failed to think outside their biases and ask themselves "can people with non-white skin use the product?". Also, management has failed here, because more diverse staff would have resulted in at least one internal test candidate of color noticing "hey, I can't get soap!".
The result of this lack of diversity and out-of-the-ordinary thinking was that a person of color was rightfully offended at not being dispensed soap. Therefore, the company acted racist even if it never intended to do so.
> the whole country is racist
Just short of half of the country recently elected a President who openly spouted white-supremacist conspiracy theories. For half the people in the US racism is not a dealbreaker for the highest office it has to offer!
He was elected by the votes of ~27% of eligible voters, and about ~20% of the population, not “just short of half the country”.
You could say it was a little less than half the people who felt it is worthwhile to vote in a system where votes matter so little that getting the most of them doesn't mean you win, but then the story is about alienation from the electoral system more than support or even indifference to racism.
... which is a bit different from saying that half the country is racist. Perhaps they thought the alternative was worse.
For the other half, sexism isn’t. (Biden selected his vice-president on the basis of sex.)
In a certain context it just means jews. And they didn't write racist soap dispenser, they wrote "racist soap dispenser".
Do look inwards.
It's a white-supremacist dog whistle for "jew": https://www.haaretz.com/us-news/.premium-how-did-the-term-gl...
> the soap dispenser wasn’t “racist”, it was just “bad”
For a person of color who wants to wash their hand and sees that it dispenses soap for white people but not for them it is yet another piece of everyday racism.
As for “globalist”, I don’t see an issue with criticising globalism and those who promote it, be they Jews or others. Clearly a lot of Jews (in Israel) are very “localist”, and I suppose there are some that are “globalist” as well. So?
Just because a view is also supported by some or other “vile” people (that are almost always a tiny minority, if disproportionately loud and/or salient), doesn’t make that view invalid or immoral.
Please define "often". And "offensive" (as in to whom). And of course, you also need to factor in whether being offended is justified.
As an extreme counterexample, a deeply racist person would be offended by a mixed couple. And racists on both sides currently are. I see no reason whatsoever to pander to either side on this, so just there being someone who is or claims to be offended is no guide.
If you find "globalist" offensive, then you are the problem. And, the vast majority of Americans are find this type of PC culture and social justice activism offensive, including the vast majority of "marginalized" groups.
https://www.theatlantic.com/ideas/archive/2018/10/large-majo...
In fact, the only group marginally in favor are white, rich and highly educated. Hmm...