I fully understand that this paper title will create friction, people will surely be upset at the mention of feminism in the space of programming languages, and evenmore so because we suggest that PL design can use some feminism, but I hope this short post will help people understand how to read the paper, and what the back story is, so that they can understand better where we are coming from.

Rejecting feminism

Last year, I gave a talk about this idea for a group of young female CS students, and I asked who of them identified as feminist, and almost all of them raised their hands. When I was their age, I surely would not have identified as a feminist (so I guess we are making some progress), and to be honest I am not even sure why.

I think it was mostly that feminism was presented to me as a group of whiners. We had education, abortion, voting rights? What more could they want really? I bought into the ‘lean in feminism’ of the 90 and zeroes: if you simply work hard, you have the same opportunities as men, so just keep your head down and work. Of course it did not help that in my CS program there were, literally, only a handful of women (I was one of 2 out of about 120 students), so I already stood out like a sore thumb, I was surely not going to attract more attention by complaining about that.

So it people reading this and reject the whole idea of feminism and ‘complainism’, I get that!
But over time, my thinking changed.

By the way, the image of feminists being complainers is not something that appeared out of thin air, but was constructed by people opposing feminism, as you can see in imagery from those days, for complaining about the vote, something most people would now feel is… a reasonable thing to want.

Feminism is about examining systems

A thing I do, naturally, is to ask why things are the way they are. Maybe that is a result of my upbringing, where my dad’s parenting slogan was: “We will decide that for ourselves”, when people would have opinions of what we should or should not do. I also hate injustice and inequality.

Over time, I learned that a core tenet of feminism is to ask why things are the way they are, and by that to examine how we can make them more fair.

Now, that really resonates with me, because over time I have developed a few questions about why the PL community is the way it is. The question that I had did not have so much to do, per se, with the lack of women in PL, but about systems: what do we value and why?

Turns out, feminism has a lot to say about systems of power, because systems of power are exactly what feminism has been studying and changing (like: who gets to vote, have a bank account, have power over their own bodies etc.)

What is a programming language?

One of the PL community questions that has been bugging me for a long time is what is and what isn’t a programming language. As long time followers know, I used to work on spreadsheets—I did very cool things like build refactoring tools for spreadsheets—but the only thing people generally cared about was telling me over and over that spreadsheets are not programming languages. And it never became clear why that is. Argumentation that could easily be refuted (yes, spreadsheets are Turing complete, thank you very much) did not help in any case: Saying that spreadsheets are code is outside of the Overton window of acceptable PL opinions, I learned over and over again.

But why? In many other cases, the definition of programming languages is fluid, I remember when Python was not a programming language, but a scripting language, very different. And UML is, I think, universally not seen as a programming language, but it is featured in the book Masterminds of Programming which features “exclusive interviews with the creators of several historic and highly influential programming languages.”

So the way we construct what is a programming language is social, groups decide what is in and out, and if you are out, like spreadsheets, and thus like in early in your career, you cannot participate in the world of PL. If we want to study this phenomenon, we cannot do that in the realm of PL itself, you will need theories about how social constructs work, and that is where feminism can help!

How do we study programming languages?

The other question that has been rolling around in my brain for years, is how we study programming languages. Ever since I met Stefan Hanenberg at ECOOP 2010, when he was working on attempts with Andreas Stefik to get PL to do more user studies, I have wondered why studies with human subjects are so rare in PL. In software engineering (at least before the field was eaten by LLMs….) a broad variety of research methods were in scope, from formal proofs to corpus analysis of code and issue trackers, to observations, interviews and theory building. PL is arguably similar to software engineering in that they both aim to improve te state of software by creating things that people could potentially use to improve that state with. Why are the research methods then so different?

My exploration of feminism has helped me, more than anything else I have tried in the last decade, including explaining to many people that yes qualitative work is also hard and valuable. If you do not understand where they are coming from, what systems of power their beliefs are rooted in, this does not do a lot.

I hope this intro will help people that were confused about this work to place the paper better and to enjoy reading it more!

We recently bought a car, a fancy new car with fancy options like lane assist: a feature that keeps the car in its lane by moving the steering wheel for you. In theory, it is a great feature. In practice, it sometimes fails. Not often, but often enough it will decide it wants to really take an exit, or it will get confused when a road is wide and alternate left and right and left and right, after which I turn it off. For a while. And then I turn it on again.

Part 2: Goodnotes

On a totally different end of the spectrum of software, I am having similar issues. I use an app called Goodnotes, and Goodnotes is my life. I use it all day every day, for making slides, for my todolist (don’t ask me why I don’t use an app for that, I have tried them all and this works for me), and for reading papers. I don’t exaggerate if I say I have been using this tool 10 hours a week for the past decade.

Goodnotes too has implemented AI features, like Universal Object Selection:

Like lane assist, in theory this is pretty amazing. When you are editing and you want to move text or drawing, in the old days, you’d have to switch to lasso tool, select, and go back to pen mode. Now you can just stay in pen mode to move stuff. Similarly, there is the Scribble to Erase.

Very cool and useful, but they occasionally don’t work! If you write a bit hasty, text will be seen as scribble and erased when you write it, and sometimes the scratches don’t register, you will do a few of them and Goodnotes will crash. It is so annoying. I could just use the Lasso and Eraser tool, like I did for years. But I don’t.

Part 3: But I can’t stop!

A sane person would just stop using these stupid features (my husband stopped using Lane assist soon after we got the car, mostly because EU law dictates you have to hold the steering wheel and it warns you of that every 3 seconds with flashes and beeps), but I can’t because, well maybe this time they will work! Like a person in a bad relationship, who keeps going back to an abusive partner, I keep thinking “maybe this time it will be better”.

Soon, all software will implement all sorts of “smart” features, which are AI features, and if people are more like me than like my husband, UX will get a lot worse for most people.

A tool that I have been using for years, whose limitation that I had to switch from pen to eraser to selector never ever irritated me, because that is just how software works, now bugs me all the time, with a bug that is not even a bug, because of course an AI feature can’t work all the time, it is statistics after all. UX has now become a Skinner Box, and who does not like a sip of “OMG this just works” sugar water?

What I am missing a lot in the current discussions around LLMs are reasons to *not* use them! This too is visible from the invitation for the panel that “discussion will revolve around opportunities, challenges, and potential solutions”. In that description the only (somewhat) negative word is challenges. The things I am describing in this post aren’t challenges, things to be addressed, but fundamental issues that cannot be fixed.

So let’s dive into some, fundamental, issues that curb my enthusiasm for LLMs in many applications, including education.

Why not 1. Climate impact

According to recent research the BBC, generative AI will soon exert as much CO2 as the whole of the Netherlands. So simply said, we put all those solar panels on roofs, and all those windmills in the sea only to have it all nullified by software. And software to do what? To cure cancer? To end hunger? No software to generate cat videos and to save us from reading API documentation. On a planet that is burning and drowning, do we really find this to be the right reason to make it so much worse?

I think a very fun paper to write (maybe one day I will if I have the time) is to calculate the carbon footprint of the most recent ICSE, not in the amounts of CO2 we are burning to fly there (which are much, but can, in my eyes, be justified by science being a social process) but with all the LLM training and querying. Is it worth it?

Why not 2. Exploitative business practices

I no longer buy fast fashion, because I can’t explain to myself that I am willingly participating in the exploitation of people, in supporting their terrible working conditions (while others benefit of their labor) Instead I buy second hand, or I make my own clothes. Everyone of course is free to decide for themselves what they find ethical consumption, but using LLMs, whether you like it or not, is supporting the continuous exploitation of labor in the developing world.

In addition to exploiting underpaid and overworked content moderator, I feel LLMs are also exploiting me, personally. The Hedy repo contains maybe a hundred thousand lines of code, which I made public so that people could learn from it. Our EUPL license states, for example, that a licensee can “modify the Work, and make Derivative Works based upon the Work” which I am totally ok with, if it is done by a person, for example if someone wants to make Hedy Javascript version, they can absolutely copy my grammar and reuse the transpiler where applicable.

But open source licenses were never really designed to prevent AI usage (in retrospect, they should have!) and the EU license that we use states that “Those rights can be exercised on any media, supports and formats, whether now known or later invented, as far as the applicable law permits so.”

Does that media include gen AI? I am not a legal scholar, so I don’t really know (and I believe that in this case the jury is still out, quite literally, in a few law suits) Maybe it violates the Attribution right that states that the license information should be shared with the code, which clearly is not happening with LLMs.

But the law does not decide what I find morally correct, we all know that many things that were immoral were legal, and I feel gobbling up my source code, repacking it, separate from its intended context, and then selling it for profit, violates the informal contract I had in mind when sharing the code.¹

Why not 3. Bias in output

Several recent studies have shown that LLMs exhibit large amounts of bias: simply ask GPT who made Hedy and it will not be me, but a man. Of course a logical closing of a sentence about who made a programming language is a male name, and that is just scratching the surface. Brilliant and genius are associated with men, and written text that uses African American forms of English are judged to be more lazy and dirty that white coded English. Do we want the limited progress that we have made in diversifying CS to be nullified by algorithms that will present students with 10 white men if they are about who contributed most to programming?

Why not 4. Leaning into LLMs will lead to deskilling of teachers, and diminish the value of the profession of the teaching profession

The last few decades have seen immense growth of universities; the university I went to more than doubled in size in the last 20 years (5000 students when I went there, 12.000 now). In the Netherlands, this can be attributed to two factors: 1) more international students as more BSc and MSc programs switch to English as language of instruction, and 2) more people that are eligible for higher education since more people follow “academic high school” (VWO).

Even though more staff were hired, the growth has made professors more overworked, not only because of the number of students but also because of a lower level, international students will not command English as well as Dutch people do Dutch in many cases, and more students eligible for uni will mean, like it or not, lower levels of prior knowledge. Plus of course a highly competitive academic field (esp. outside of the natural sciences) means that demands on scientific work come on top of teaching duties.

This situation creates very fertile soil for (gen) AI: if I have to grade 80 essays in a day, or if I don’t have time to update my powerpoint slides with new research, using AI suddenly seems like a reasonable or even necessary. But grading or preparing isn’t a purely productive activity, I would argue that it cannot be optimised or made more efficient, because the goal is not only to grade the essays, the goals is also to learn from what students are submitting to improve teaching, and the goal of making slides is not to make the slides, but to read a few more papers about my field and update the slides with those I find will have value for the students.

Leaning into the idea that LLMs can do the deep thinking work required will inevitably lead to less skilled teachers that are no longer learning form their students’ mistakes and from new research. Also, it will hamper activism of professors against work pressure, which traditionally has been relatively successful. In a pre-GPT era, having to grade 80 essays in a day might have led to people going on a strike (students and professors) but now that it is “possible” to use an AI, the problem is not so visible in the direct sense, only in a slow (but sure) erosion of the profession.

Soon, the Netherlands will have a right wing government, and if the polls are any indication, so will the EU, and probably the US again after November too, and those governments hate science and education and want to budget cut the hell out of us all. If we, the scientists, are already saying AI can replace us, even if we are careful about what it can and cannot do, it will be used as a reason to reduce funding even more, and we can all easily predict, without an AI, where that will lead. This holds especially true for computer scientists, who will be asked more than other about their opinions (while probably being impacted less)

Addendum

Why so few objections? This is part of a longer set of posts that are upcoming, but I am reflecting on the field A LOT lately. I am a bit of an academic nomad, going from PL to SE to CSed to PL and most recently I am doing some work in the history and philosophy of science applied to programming, mainly because I am so curious about why our field is as it is. Why am I often the only person talking about climate impact and bias? While there are, of course, a gazilion reason, a paper I read recently showed that being a social activist is a strong detractor for studying computer science², and so it being artistic. So (compared to the general public) already very few people that care about social justice are entering our field, and then or source our culture does a great job at making care less about others.

I know I sound like a broken record for my regulars but a field that has a Von Neumann medal, named after a guy instrumental in killing hundreds of thousands of civilians does project some values on the inhabitants of that field (although many, like me for a long time, might be utterly unaware of his involvement, which is a bit of an excuse, but also another sign that we just don’t care).

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1. It is also somewhat disorienting to see a paradigm shift happening in real time. I vividly remember the fury with which professors, when I was young, hated Microsoft, because they were making MONEY off of SOFTWARE. Even if Linux did not work well and their community was toxic as hell, there is one thing that is worse and that is running a profit.
   
   To see a whole field do a 180 and suddenly be exited about CoPilot, which is not only software for profit, but it profit from open source software, is… something ︎
2. Sax et al., Anatomy of an Enduring Gender Gap – Journal of Higher Education 2016. ︎

The organizers told me that the “discussion will revolve around opportunities, challenges, and potential solutions” and I was allowed to discuss one statement. Since they only gave me 7 minutes (and no slides :() I thought it’d be fun to elaborate a bit more here.

My chosen statement is:

Why

What has been bugging me in the discourse around LLMs in CSed a lot, is that the core sentiment seems to be: let’s use them, because we can (in a great contemporary Dutch saying that can’t really be translated apty: “niet omdat het moet, maar omdat het kan”). We don’t discuss specific problems, just solutions, we see that in the description of the panel too 1¹.

As the kids would explain it!

But what do we even think LLMs can offer? Let’s look at the reasons people give in favour or the use of LLMs.

Why 1. Professionals will use LLMs, so students will need to (learn how to) use them too

I actually don’t hate this argument, I think it is a pretty good point: we need to prepare our students for professional careers. However… LLMs are quite new, and we don’t really know how professionals use them yet (although of course academics are now performing one study after another to better understand their use in practice), and we do not know whether in a few years, when our current students graduate, the situation will be the same.

Here is what we do know:
Programmers don’t use LateX, they use Word or maybe Markdown.
Programmers use GitHub.
Programmers refactor code.
Programmers use debugging tools.
Programmers perform code reviews, programmers use CI/CD tools, programmers deploy code on the Cloud, etc, etc.

So if we are shaping our curriculum by the future usage of professionals (which again, I do not object to at all) why don’t we teach all the above tools, and why don’t we stop immediately with teaching and promoting LateX, a system proven to be worse than Word and not used in industry. If we do the one but not many of the others, what does that tell us about our real motivations? Could it maybe be the case that we think LLMs are very very cool (like LateX) and let that cloud our judgements? Seeing how little actual tools and practices from industry are being taught, I think this is a reasonable hypothesis.

Why 2. LLMs can replace teachers, so we must explore how to use them

The next underlying reason that people rush to use LLMs in education, is that they, fully or partly, believe that LLMs can teach students, rather than professors. Let me firstly throw in this amazing comic by Doug Savage.

I like teaching! I like grading, and making powerpoint slides, not only because they are simply part of my job, but also because grading gives me unprecedented insight into my own teaching, and preparing slides helps me outline my thinking. I don’t want to automate those inherent parts of teaching, and neither should you. If you want to automate teaching, why are you a teacher??

However, me saying that “grading is teaching” and “preparing is teaching” creates interesting discussion. What is teaching? Saying that AI can automate some parts of teaching leads to deep philosophical questions about education which I feel most CS professors are not equipped to properly discuss, because of the lack of philosophical and educational theoretical grounding.

Because in order to discuss what LLMs might mean for “education” we need to define education, and that is harder than you think.

To connect this to point 1, what even is the goal of CS education? Is it to train programmers? Is it do train future scientists? To train programming “though leaders”? ²And should education (any education, or CS in particular) be about teaching skills, or “ways of thinking”, or about giving people the vocabulary and mental tools (like math) to deal with all sorts of issues.

It has been possible, for a very long time, to learn most content of a CS undergrad program by yourself, when I was a kid in the 90s I learned a lot from books from the library, then with YouTube, MOOCs and now (people are saying) with LLMS. Yet, registration numbers for CS programs are soaring! So we must offer (at least in the views of 18 year olds and their parents) a value. What is that value? I would say the value is context, camaraderie with fellow students, connections with teachers, and learning what you did not know you did not know. None of these are automatable with a machine, so what do we even think the LLMs are doing?

And more important thing is not only what the LLMs are doing, but also for whom. In the school of education, where I teach pre-service CS teachers (lerarenopleiding, in Dutch), we tell our students that there is no such thing as a good intervention. Any decision you make while teaching is good for some students, but worse for others. More explanation is good for kids with lower prior knowledge. More group work is good for students in a dominant group.

So who are we designing CSed programs for? For kids that already know they like programming and what to become programmers, or for people that don’t know what programming means? We have to constantly make trade-off.s For example, about equity. If in a given class of 50 students, 10 people are confused and 10 people are bored, who do we care about most? Who do we address and help first? Because we don’t talk about these type of high level design goals, many people design their teaching for people “like them” (likely to be excited about all sorts of technology, including programming and LLMs)

Answering questions like these (which is hard and messy and imprecise) needs to come before deploying any kind of tool. We are already seeing results (very much in line with what learning theory would predict) that students with more prior knowledge are helped most by LLM use, so who are we helping and who are we disengaging (even more)? If you answer with “for all students” you lack an understanding of teaching theory.

Why 3. Students will use LLMs to do their homework!

As I said two years ago at the SEN symposium, I am excited to change introductory programming to be less about programming, and to get rid of autograders. When LLMs came along, I had some level of optimism that this would be the end of programming exercises involving a lot of syntax, but the opposite has been true. We are now leaning into the fatalistic notion that LLMs in CS education are inevitable, and refraining from further reflection upon our teaching.

Let’s dive in a little bit more about what exercises for programming courses usually look like. I think more or less like this:

We give students a prompt, and they have to make a plan how to solve the exercise, choose the right concepts and combine them together in a running and working program. These individual steps, I believe but I base this belief in cognitive science, represent different types of activities. Steps 1 and 2 mainly use the Working memory, fitting a solution in your head and weighting different options. Step 3 mainly uses your long term memory, you will have to remember the right syntax to correctly implement your idea.³

Despite the fact that these are really different things, we grade only the final product, which students struggle a lot to to arrive at, sometimes because their plan was flawed, something because they don’t command the syntax well enough.

Programming education Is NOt aBouT SYntAx!!!!
_{(but we only check if you can “do the syntax”)}

The reasons I think are threefold: 1) This is how we all learned, so we think it works, 2) because most CS professors lack the educational theoretical vocabulary to distinguish the steps, and 3) because we can check code easily, so we must (“niet omdat het moet maar omdat het kan”). And not only do we only grade syntax, we also only explain syntax. We never explain to students how they should make a plan, evaluate a plan, choose concepts etc.

In my opinion the deepest issue that we currently have in programming education, is that we do not split these steps into separate exercises and assignments. It would be trivial to do, give students 20 prompts and just have them write down a high-level plan, so they practice this skill, and then transform the plans into lists of concepts, and then to syntax. Only after practicing those skills in isolation, they should be combined. Everything we know about learning supports this way of teaching (in math, we practice many concepts and skills in isolation before combine them; in language learning we do the alphabet, vocabulary and grammar separately) and yet we do not do this.

Because doing this creates the question of how would we grade the natural language plans and lists of concepts? My answer would be that multiple choice is actually a fine strategy, but maybe there even LLMs would make sense, because we have a problem in which natural language needs to be processed! (There are different reasons to not use LLMs though, but at least this is a LLM like problem).

Yes instead of adapting our teaching methods and finally stopping the tyranny of autograders… we keep doing the things we do, but students have access to LLMs and now they are learning even less.

As I also wrote about last week, when I was a young assistant professor, and I was complaining about cheating (pre-GPT) a wise old professor once told me: “When students cheat, *you* are being unreasonable.” I of course thought that was a silly thing to say, the students were just being lazy!! But, in retrospect, this colleague made a great point, we are asking unreasonable amounts of learning in an introductory programming course, so of course students are going to use help, their peers, the internet and now LLMs. If we would make our education more reasonable, there would be less “cheating”.

Do you think students would immediately go to LLms if we’d simple ask them to explain in words how to find the longest alphabetical substring, after they had seen a few similar examples in the lecture? I don’t think so.

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1. Not to pick on the organizers! This pattern is common everywhere. ︎
2. I have talked before (in Dutch) about the opaque goals of CS in higher education: https://www.agconnect.nl/carriere/arbeidsmarkt/waar-is-een-universitaire-informaticaopleiding-voor ︎
3. In reality of course there is no linearity in this process, people go back and forth between steps. In this example a student might only realize at the implementation stage that a for loop does not give you easy access to the next letter, needed for this assignment and then switch to a while loop. ︎

You can’t stop students from doing X

Firstly, we have been saying things like this for decades, if not longer. “Don’t collaborate on homework”, “Do this exercise without a calculator”, or when I was a student “Don’t use Wikipedia”.

Why do we see so much more fatalism in algorithm use then in collaboration or other tools? Why do we give up so quickly and assume students will “cheat anyway”? One reason I think is the narrative that AI companies are pushing: there is no escaping the AI wave, everyone will use these tools in the future, all the time. That creates a situation in which teachers assume that all students will use AI in their daily life also, so, why prohibit them now.As they say “resistance is futile”?

Is it though? Haven’t we all been quite successful at prohibiting students in elementary schools to use calculators, even though professionals use them all the time? Most educators agree kids should still learn the tables of multiplication even though they can easily be automated. Kids complain about that 9as they do) but we all keep this line: you need to be able to do small calculations in your head; the whole tower of math skills leans on that!

Why do students cheat (with or without AI)?

A wise old professor once told me, when I was complaining about cheating in the pre-GPT era: “When students cheat, *you* are being unreasonable.” I of course thought that was a silly thing to say, the students were just being lazy!!

But in retrospect, this colleague made a great point: Students come to university to learn, they might be lazy a bit, but if many of them are, is your assignment clear enough? Is it doable? Are there many other deadlines? I think if we critically examine CS ed from a learning perspective, it is totally reasonable for them to cheat, and it we’d make it more reasonable, they would not cheat (as much).

Professors set the norm, even if students don’t follow them

Much as with “don’t collaborate on this”, we can set norms even if we know damn well not all students will listen. This is because setting rules also set norms. Contrary to popular belief, laws often shape, not follow what people think! For example, when gay marriage was introduced in the Netherlands, a minority of people were in favor, and the law helped increase support. After all, what is allowed, must be good, and what is forbidden is not.

Students doing an exercise with AI, knowing it is not allowed, will feel like their are breaking a norm, and will thus differently about their work, and that matters.

Ring Theory, from Wikipedia.

Originally this theory was designed by a women with cancer, she did not want to comfort her friends and family, even though of course she also understood their grief and worry. I think it is an excellent model, that we should all follow. Don’t complain to a sick person that you are so worried, save those complaints for people further away from the situation (dump out) and only be kind to the sick person or someone closer to them than you (comfort in).

I recently though about to similar situations in which two friends (on separate occasions) mentioned how I was treated sexist/unfair by a third party, at events where I was not even present.

These situations were interesting since I was not really “in crisis” before the friends had made their remarks, they were in a sense causing the crisis, because I thought it was pretty heavy that people are so openly sexist or negative about me without me present! But then I was in crisis, and I was expected to also comfort my friends saying things like “well, I am sure you did your best to mitigate the situation” or “what a weird person that they’d go off to you about me!”, so it became a “comfort out” for me.

So let’s try to adopt a “defend out” mode for sexism and other injustices please. It people are sexist about non-present people, defend them “out”. And, important for this context, do not bring your knowledge in, and certainly do not do it without thinking about it! If other people are shit about me, I do not necessarily need to know! In addition to these two friends (in the span of a week days) I have heard so many of these remarks “ow such an such called you this or that, you will not believe it.” Well I will but I’d rather not know, you can shield me from that.

So I guess the ring theory for injustice should be “shield in, defend out”.

After an American media frenzy of a few weeks, the new president of Harvard has resigned after the shortest term ever, only 6 months.

This saga began with a US congressional hearing in December at which the presidents of drie major universities (all three women) were asked to say something about the increasing anti-Semitism at their universities. Only the subject is of course immediately complicated, and one can and may (and should, perhaps) wonder why the US Congress finds it necessary to hold a hearing on rising anti-Semitism, while meanwhile they also support Israel, a regime that systematically murders and imprisons civilians including kids without trial. It’s a preemptive strike to scare anyone who even considers showing pro-Palestinian sentiments.

But that’s not fair. As much as an increase in anti-Semitism can be seen, and as grim as it is, even in the Netherlands, speaking out against the post-October 7 horror that cannot and should not be called “blanket” anti-Semitism; criticism of a country’s actions is incomparable to hate of a race or religion. Being against the regime of Afghanistan is not islamophobia, and being against anti-vaxers in the biblebelt (including our own) isn’t Christian hatred.

Gay herself calls the hearing a “well-laid trap,” she does so, remarkably enough, in a piece in the NY Times, who were themselves instrumental in her downfall, by continuing to report on what I think has no equivalent in English, but what we do so nicely in Dutch “fophef,” a storm in a gals water. Indeed, Gay said in her response that it is tricky to just say whether students are allowed to use the term “intifada.” Tricky she said, because there is also such a thing as freedom of speech, even stronger in America perhaps than here. In a sense, Trump’s lawsuits on Jan. 6 are also about freedom of speech versus inciting violence.

After the lawsuit, American far-right opinion makers immediately called for her resignation, but initially that led to nothing, reportedly because Barack Obama, among others, backed Gay.

But that did not sway conservatives. This is atypical, as right-wing America is not exactly known to be such a fan of the Jewish cause, so there must almost be something else at play as well: Gay is black, the daughter of Haitian immigrants and researches the effects of slavery and the role of race in elections. That obviously played a role in the attacks on her work and character.

A new line of attack was opened: plagiarism in her research. Despite the fact that an internal committee had previously cleared Gay of misconduct, it pressed on. “Plagiarism is never allowed,” even though this was e.g. a thank-you note that sounded a lot like that of a lab mate, and a description of statistical results that always sounds formulaic: “our research shows that variable X and Y are strongly correlated with each other” can only be written in a few ways, she had to go. Extreme right-wing activist Chris Rufo began an unprecedented hate campaign, even admitting that he had planned its timing to maximize damage to her career, and, like a bad guy in a B-movie, explained live on Twitter and later even in the Wall Street Journal how his little plan would succeed, but in contrast to most, villains, his plan did succeed, and exactly as he said it would: more respectable “leftist” media like the Washington Post went along with the frame, one thing led to another and “here we are,” Gay is as Rufo himself calls it, scalped.

What’s going on here is the “weaponization” of data analysis; anyone, even someone not interested in the difference between a thank you note and a paper, between stealing someone’s work and using a standard phrase, can easily run anyone’s pieces through a checker and put “you see” by the screenshots, and the fuss is happening. Then all you have to do is wait for it to blow up further. Fuelling hatred of Gay is reminiscent of GamerGate and of “has Justine landed yet” from the early days of social media.

Now you may still think: oh well, do we care…. It’s very very annoying that this woman is paying the price for misjudging public opinion about Palestine/Isreal and the reaction is unfair, but and children die every day in Gaza and in Ukraine, so yes….

But the Gay case does not stand alone, there is a hate campaign going on in America against the whole idea that non-white non-males are also claiming their right to participate in business and science, including, for example, Elon Musk, one of the most powerful men on earth. Diversity policy, that is unfair, that is racism and that gives people we still do not “like” but access to power and public debate, it should be over with, and soon. Disgruntled men went to the Human Rights Board twice already to complain about the “preference” of women, once in vain, once successfully, although TU Eindhoven adjusted the rules, after which they were approved.

And that while the advance of women in science has actually stalled, the LNVH reports this year that growth has stalled at a scant 30%. Even in my age group, professors under 40, parity in chairs is not achieved; only 42% of these positions are held by a woman. And that’s for the generation of which some 50% were female graduate students (this was true for the first time in 2006). Scientists with children are still at a big disadvantage, if they are women. So there is not really a basis for the idea that we as women will soon drive men out of science, and nevertheless, according to some people, it is going too far.

It is very tempting to think that all this is not so bad, that the rights we now have as, e.g., women in science are acquired and can never be taken away, and that there are now enough of us to counter this misery. But remember that it takes effort to keep reporting on this, I could have spent the last hour on research instead of this column. In the immortal words of Toni Morrison:

Distracting, frightening has a function, but there is also plenty to be afraid about, rights we have can be taken away just like that, and positions of mach become unattainable. E.g. I was standing there the other day clapping my ears that there was ever (in my lifetime even!) a female Democratic governor of Texas. You can’t imagine that now. And how about this photo of (sadly unnamed) Iranian parliamentarians in the 1970s (source). Iran also had a female minister in the 1960s, but 10 years later than the Netherlands, Farrokhroo Parsa.

Dot should make us realize once again that the rights of women, and other people who historically have less power, will have to continue to be fought for.

Now I think allowing more women, people of color, people with disabilities etc into “power” whatever that means, from science to journalism, is a matter of human rights: research, news, it concerns us all so we should all be allowed to participate. But that seems a difficult standpoint in the present time. Then I have another one that might go down well with the BNR audience…. Diversity is good for the wallet!

Research by McKinsey shows that companies with more women and people from ethnically and culturally diverse backgrounds at the helm perform better financially. Companies with 30% senior women outperform those with fewer women at the top by up to nearly 50%. And the most culturally and ethnically diverse companies did up to 36% better than less diverse organizations. It’s unfortunate that that has to be the reason, but let it be so, and let everyone unite in the fight for more power and influence for more diverse people!

Listen to this column on BNR (Dutch)
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Teachers!

Last weekend I read a great piece in the NRC, editor Patricia Veldhuis spent the past year as a special intern at a school, where she also taught and the result is a beautiful piece about the vulnerability you have to deal with as a teacher (Do am I doing it right? How do I ensure that students pay attention and learn what I want them to learn?)

I would actually like to copy the entire piece here, as a teacher it was really hitting it close, but let’s pick out a few parts.

What did you think was most important?

The most relevant thing for everyone to hear is the enormous work ethic of teachers that is evident from the piece. The cliché of “plenty of vacation” is expertly undermined, teachers, even in the most positive scenario without overtime, work more than their contractual hours throughout the year and therefore earn their vacations themselves.

And in addition, almost every teacher at her school structurally works more than those hours, for example people who work 4 days on paper, but are simply at school for 5 days, for example to prepare or consult. I find that image very recognizable. I don’t want to put anyone’s stress down, but when I hear colleagues at university complain about the workload due to many meetings, I think: “Yes, you really do that yourself, you really don’t have to do everything”.

On a school day I sometimes literally don’t have time to go to the toilet, but you really have to experience that at most higher education institutions or offices!

Furthermore, the administrative pressure is simply unprecedented, everything a student does (forget a book, leave homework unfinished, go to the doctor) has to be recorded and if you are unlucky, you also have to answer to a parent for it. Time for administration and meetings has increased in recent years from 10 percent to now 40 percent, also under pressure from parents who want to be able to keep track of exactly what is happening in a classroom, including in secondary education!

Speaking of parents… that wasn’t the only education news that caught your eye this week, right?

No, maybe it’s selection bias, but Trouw also had a nice and relevant piece about differentiation in the classroom. In many primary schools you have within a class, say a group of 5, level groups, the stars, suns and moons, for example, which are then explained separately. Teachers find this annoying, because it is a lot of extra work to explain three times separately, but more importantly, research shows that it is not necessarily better for the faster students, but very bad for the slower students , because the teacher will no longer hold them to high standards in the slower group, and because they cannot learn from classmates who are further along. The reason why these types of interventions have been introduced is clearly not the research that shows that it is not a good idea, but the society, which mainly consists of parents who say that their little prince is not challenged enough. Their worst image is that their child is bored at school, but as a result, policies are introduced that mean that other students learn much less, just like the administrative pressure at a secondary school, a sign that the outside world simply does not know what is good for schools and for students, and We have to stop shouting about how things should be different.

That’s why a big compliment to Veldhuis, who, instead of typing opinions, has experienced it himself and substantiated it and tells it honestly.

Yes, back to the NRC and the advice!

The most moving part of the NRC article is at the end, when students at school ask an overworked colleague why he actually remains a teacher, why he still likes the profession. We have to read his response literally, the man says:

‘There are a lot of professions where you die a little every day on the inside. I don’t have that feeling here. And that’s because of you.’

You simply cannot describe the teaching profession more aptly. You with your spreadsheets, meetings, days off and I don’t know what you have in the business world, with your great salaries and a nice lease car on the sidewalk, enjoy it, but we education people do not have a midlife crisis, we do not have to to ask ourselves “what we do it all for”, because that is crystal clear every day.

So here is the advice to the teachers of the Netherlands (and beyond)

Don’t be fooled, we have the best profession there is, and let’s all ensure that we can continue to do that profession without input from outside! They don’t know what they’re talking about.

This column on BNR (Dutch)
This column on Spotify (Dutch)

An “incredible” video this week on Twitter/X:

Wow!! With the app HeyGen you can effortlessly speak multiple languages! Alexander Klopping says something in Dutch, and then his own face and voice appear, in Italian or Spanish or Chinese. Of course you can see that his lips move a bit weird, but it seems very real. The conclusion of some is that all translators in the world can actually retire, because they are no longer needed.

In response to this, a piece appeared on nu.nl this weekend, in which the NGTV (Dutch Society for Interpreters and Translators) responds to this new AI capability. They say they are not yet that afraid of the AI, because: ‘Translating language is one thing, understanding it is another’.

Funny side note, I tried to translate the above sentence with Google Translate, and it became: ‘Understanding language is one thing, understanding it is another’. Point in case!

It is a nice piece, in which they explain the importance of their own profession: sometimes more context and substantive knowledge is needed than an AI can provide, and in some situations (e.g. the medical domain) it is very important that things are really really correct.

However, they clearly lack the perspective of someone who really understands AI well (understandably, that’s not their job either!). They rightly emphasize their own strengths, but you should also carefully consider what the weaknesses are of translation AI.

What do those weaknesses consist of?

There are a number of important perspectives. A well-known American programmer, Hillel Wayne, has formulated a rule of thumb for the use of AI, which I like to call Wayne’s law:

You should use AI in situations where it is difficult to come up with the information yourself, but you verify it yourself.

E.g. If you want some nice ideas for a story or an advertisement, you can then choose for yourself what you will and will not use, what is and isn’t nonsense. Or, for example, for generating computer programs. If it doesn’t work, the code will not run. Translating something is therefore completely on the wrong side of Wayne’s law: you generate something that you no longer understand, but for which you are responsible. Even though the chance of things going wrong is small, do you really want to put something on your site that you read out loud yourself, that you don’t understand?

Ok, but the chance that it is wrong is not great, is it?

The nice examples you see on HeyGen are often simple sentences that do quite well (as far as we can tell, maybe Klopping says in Chinese that he loves a poop sandwich), and they stick. But if you read scientific research in this area, you see a much less rosy picture.

I read the paper “Hallucinations in Large Multilingual Translation Models” for our listeners, and I can briefly summarize this. For example, if you translate the sentence “Sharks rarely attack humams” from English into Hungarian, language models say (in Hungarian, that is): It is “necessary to translate this sentence from English into English into English”. Translate some simple information about Luxembourg from English into Vietnamese, and the model translates: “This is an English sentence, so there is no way to translate it to Vietnamese”. It’s a bit of a shame if the Luxembourg tourist office will soon have that on their site…

On a larger scale, this is also evident from the paper “Towards a Comprehensive Evaluation of Large Language Models in Multilingual Learning”. While GPT gets it right 88% of the time on tasks in English, for Thai and Vietnamese this is only 68% and 65%. A big difference! Things often go very well in Dutch, which may explain why we often think that the technology itself is very good. When I was in Botswana in March and said that I often use Google Translate, they looked at me in surprise. For their language Setswana it often doesn’t work at all!

Finally, you not only have a responsibility for yourself but also the future of the entire internet rests on your shoulders!

A major risk, also called the “enshittification of the internet” by journalist Cory Doctorow, is that AIs will fill the internet with nonsense and that we will no longer be able to distinguish true from false. This of course applies to click farm sites with deliberately incorrect information, but it may still be possible to filter this out. If reliable sites, let’s call it pepsi.com, suddenly have something crazy in a text translated into Dutch, such as one of the ingredients of Pepsi Cola is motor oil, then soon no one will trust anything anymore and you will have nowhere to turn .

And now the advice:

Translate by hand, even if it costs money for a good translator. If you need multiple languages, translate it by hand into some major languages (English, Chinese, Spanish), which many people can read. And think carefully about what could go wrong. If it’s just for a menu, fine, if it’s about the strategy for your company, then maybe not. I sometimes actually use an AI for the English translations of these posts but (as evidenced by the failure above) that does not always work, and I *am* fluent in English so I can actually check what I publish.

And perhaps a little bonus tip for tech enthusiasts, learn a little more about the techniques behind the software you show. If you call something “incredible”… maybe you shouldn’t believe it either.

This column on BNR (Dutch)
This column on Spotify (Dutch)

(after feedback on previous posts, the links are now at the top instead of at the bottom)

Let’s talk about the AI act, again…

The European Commission talked about the AI act last week, and there are some good points in it, so so far, good. But… the approach is special, in their press statements, including on Twitter/X:

Mitigating the risk of extinction from AI should be a global priority.

Erm…. Why are they suddenly talking about the risk of extinction due to AI (and not about other things that were previously discussed, such as deep fakes)? It is a frequently heard argument in this area: the exclusion risk. Because it is mentioned so much, and because AI is so elusive for many people, I thought it would be good to delve deeper into it today.

The risk of extinction due to AI is consistently mentioned by a number of thought leaders, including Elon Musk, scientists George Hilton, Sam Altman of ChatGPT, but also the less well known, but very influential Eliezer Yudkowsky.

What exactly is the AI that people are very afraid of? We are not talking about a specific AI as we know from Deep Blue for chess or for a self-driving car, which can only do one task, but general intelligence, which they often call AGI: Artificial General Intelligence. It is generally accepted that AGI is much more difficult to create than a dedicated single-task AI. After all, we already have robots for vacuuming with a bit of AI in them!

But the people who are now calling for a ban are experts, aren’t they? Well, let’s first look at the predictions of, for example, Hilton and Musk. They’ve been saying for years that even narrow AI will work great. In 2016, Hilton said that we can stop training radiologists because they would be redundant within 5 years, that has not yet happened, and Musk’s repeated predictions that self-driving cars and trucks are imminent have also proven to be wrong. So we cannot call them good forecasters.

How exactly will AI cause human extinction? It is therefore striking that there is a complete lack of a realistic scenario. It remains only vague, Hollywood-like scenarios that the AI will exterminate, but there is no real story. This is a stark contrast to, for example, climate activists who substantiate their stories about risks (existential or otherwise) with data, or virologists who have long warned about a Covid-like pandemic due to zoonosis. But, people say, including former MP Kees Verhoeven: “it could be possible”. Yes, anything is possible, maybe FC Volendam will become champion this year and Max Verstappen will go figure skating tomorrow. But it is truly remarkable that such vague, unsubstantiated statements receive so much attention.

Is the call to stop developing AI already having an effect? Well, it’s also “funny” (in bold quotes) to see that the makers of AI don’t stop making more AI. If they were really that concerned, they would just delete their own AIs right? But Palantir’s Alex Karp said that defense should fully focus on AI, which sounds very contradictory. Elon Musk was explicitly asked by scientist Deborah Raji why he continues to put AI in self-driving cars if he considers it dangerous. The fact that he didn’t respond to that question says a lot. It’s not that dangerous!

But do you think it is good to continue developing with that AI? No, and that is what makes the matter so complex. I also think that we should be careful when using AI! But many risks that scientists and journalists point out are not mentioned in the EU discussions, or, as was the case last week, in the AI insight at Congress in America. A major risk, also called the “enshittification of the internet” by journalist Cory Doctorow, is that AIs will fill the internet with nonsense and that we will no longer be able to distinguish true from false. That’s no direct extinction risk, but it’s already happening and it’s preventing people from finding reliable information. Or the risk that AIs are less trained on data of certain people, not only the well-known “facial recognition performs worse on black people” but also new research that shows that GPT works much worse in Hungarian or Vietnamese. So when Western companies use this worldwide, some people immediately have much poorer data quality. These risks (although fortunately they are partly covered in the AI act) are much less in the news, and that is not surprising because those risks hardly affect white Silicon Valley men. In fact, the only disaster that can affect the top 1% is a disaster that affects absolutely everyone. In fact, the only disaster that can affect the top 1% is a disaster that affects absolutely everyone. SO viewed that way, it’s actually logical that extinction worries them… what else would?

And then the advice to the European Commission, but also to the Dutch government, where something similar is happening with the AI petition: Be widely informed about the real risks, keep asking questions, and don’t listen to people who say no but do yes!