Killer robots are here already

August 25, 2017

At the International Joint Conference on Artificial Intelligence 2017, an open letter was released, signed by over one hundred top scientists and industrialists in artificial intelligence, calling for a ban on the development of autonomous, artificially intelligent weapons, often referred to as “killer robots.”

This vapid gesture is equivalent to calling for a “ban on the development of knives that can be used to murder people.” The problem is that almost any device that can be taught behavior and is allowed autonomous functioning, can be employed as a “killer robot.” And all industrial artificial intelligence research advances intelligence, learning ability, and autonomy of machines.

Elon Musk might be in favor of a ban on the development of killer robots, but his Tesla company works on autonomous self-driving cars. Recent terrorist activities have demonstrated how cars can be used as weapons. You only need to teach a car to hit people instead of avoiding them.

Mustafa Suleyman might want to stop research into killer robots, but at the same time his DeepMind company is the leader in deep-learning research, which aims at allowing machines to learn patterns and respond to them. Such pattern recognizers can be easily placed in smart missiles or weaponized robots to autonomously find viable targets.

Jerome Monceaux signed the letter, while simultaneously heading Aldebaran Robotics, which develops general-purpose robots which can be taught or programmed to do anything — including using weapons and going on a murder-spree.

And the list goes on.

The whole point of artificial-intelligence research is to allow machines to do things that humans can do, preferably more efficiently and effectively, and preferably with a high degree of autonomy. Moreover, almost all modern artificial intelligence research is based on machine learning, i.e., teaching machines to behave in a particular way rather than directly programming them. Consequently, almost any artificial intelligence research can be used to teach machines to help people, or to behave as a weapon. This entails that machines that have the ability to operate as killer robots already exist.

Basically, the call for a ban on the development of killer robots amounts to a plea along the lines of: “Look, we are developing all this great technology which will bring fantastic benefits to humanity but please, please, please do not use it to murder people.” It is a call for sanity on the part of governments, the military, and terrorist organizations so that they won’t use the technology for evil. And we all know that the sanity of governments, military, and terrorists varies.

You cannot stop the possibility of (further) developing killer robots without a world-wide halt on artificial intelligence research altogether. I do not think that that is what any of these people who signed the letter, or anyone else, really wants. Or that it can be enforced, for that matter.

The best you can do is realize what artificial intelligence can be used for and then build in protections against misuse. For instance, autonomous self-driving cars should be strongly guarded against attempts to reprogram them. This is in the hands of Elon Musk and his competitors. Rather than calling for some kind of ban, they should do their jobs properly. And while I think they are trying to do a proper job, their call for a ban sounds like them trying to place the responsibility for misuse of their technology in the hands of others.

Any technology can be misused, and usually is. That is no reason not to develop beneficial technology. The benefits of autonomous artificial intelligence can be great. The dangers of it are lurking in the autonomy — technology which allows machines to operate autonomously, taking autonomous decisions on how to act, should be surrounded by stringent safeguards against the machines taking harmful decisions. But probably the biggest danger is not in the artificially intelligent machines themselves, but in the humans who place unwarranted trust in them to take autonomous decisions.

I applaud the fact that many influential people consider the dangers of artificial intelligence research seriously. The call for a ban, however, sounds like an after-the-fact plea.


Diversity VI: Normal discrimination

August 22, 2017

As a final follow-up to my previous post on the issue of enforced diversity in the workplace, I wish to expand on the criticism that I gave on those scientists who deny the notion that biology has an influence on the different roles of men and women in society. The main argument that is given by these scientists is that the biological differences between the sexes, while existing, are smaller than is generally assumed. From this notion they derive that, based on biology, you should see only small differences between behaviors of men and women. Since this is not what can be observed, they conclude that men and women are treated differently because of (only) cultural influences.

These scientists do not understand the normal distribution.

We may assume that most characteristics of men and women, both physical and mental, are approximately normally distributed. For the sake of argument, let’s say that due to biological causes, men on average have a slightly higher aptitude for abstract thought than women, while women on average have a slightly better social sense than men. If you then give a large bunch of men and women an IQ test (which measures abstract thinking) and an EQ test (which measures social thinking), then perhaps on average men have an IQ of 100.5 and women an IQ of 99.5, while men on average have an EQ of 99.5 and women an EQ of 100.5 (I am pulling these numbers out of thin air, they are just examples). The 1-point difference shouldn’t be noticeable in everyday behavior.

However, the 1-point difference is for the average. At the extremes of the normal distribution, the differences are much more noticeable. If the shapes of the Bell curves for men and women are the same, but with a difference of 1 point for the averages, and we look at the 5% highest scorers for both groups, the average of them differs a lot more. Looking at it in another way, if you are only checking those with an IQ or EQ higher than 150, the 1-point difference for the average can easily translate to 80% of the people with an IQ higher than 150 being men, and 80% of the people with an EQ higher than 150 being women.

The fact that companies and institutions want to hire “the best” for crucial positions, entails that they discriminate to look for the high-end tail of the Bell curve which describes the population distribution for the traits which they need the most. Even if on average there is little difference between the aptitudes of men and women to do particular jobs, when you discriminate to get the best, you will generally end up with a severely skewed distribution of the sexes.

This effect is magnified by the fact that for many traits, the variance differs for the sexes. In particular, it has been reported that men tend to have a higher variance than women. This means that men fit a flatter Bell curve than women for many traits, which translates to there being a denser population of men at both extremes, even if there is no difference between the averages. In layman’s terms: if in a particular area there are more genius men than genius women, then this is offset by there being more moronic men than moronic women (though there will, of course, still be both genius women as well as moronic women).

Even if the aptitudes of men and women for a particular job are the same, fitting the exact same Bell curve, then there is still the fact that the interest for them to do that job may differ. Again, if interest in tech is normally distributed over the population, and there is on average little difference between the interests of men and women for tech (which might be the case: most people are not really interested in tech jobs), then you might still find big differences between the number of men and the number of women who are highly interested in tech, i.e., whose interest is at the high end of the Bell curve. Which means that even if women are equally able to do a tech job, that does not mean that you will find many women actually preferring to do it over some other job.

Note that the same may be noticed in high-end jobs which see a dominance of women, such as medical specialists. At a time when few women entered the job market, physicians were usually male. Nowadays, when about 40% of jobs are held by women, physicians are predominantly female — either because being a physician requires a skill set for which the best candidates tend to be women, or because it is a job that women have, on average, a higher preference for than men. Regardless, being a physician is a high-end profession where women rule — which makes mincemeat of the notion that some kind of male oppression is keeping women out of high-end jobs.

Enforcing an equal distribution of the sexes for those positions for which high skills and/or high motivation are needed, leads to either lowering the quality of how the work is done, or appointing people who would rather be doing something else.

By the way, the discussion above does not mean that there actually are only small biological differences between the sexes. In my previous post I gave three reasons to suspect that the biological differences are quite significant. However, even if you assume that there are only small biological differences between men and women, distribution of the sexes in high-end jobs will usually be unbalanced.

Diversity V: Nurture vs. nature

August 11, 2017

I feel the need to make a few more statements about the firing of James Damore by Google, which I discussed before, in particular insofar it concerns his claim that biology may be partially responsible for observed differences between the interests of the average man and average woman in society, and how this claim seems to be interpreted.

As Google CEO Sundar Pichai wrote on the reason for Damore being fired: “to suggest a group of our colleagues have traits that make them less biologically suited to that work is offensive and not OK.” I read Damore’s treatise, and nowhere in it I can find him saying that his female colleagues are less biologically suited for their work — he only states that the biological differences between men and women may make the average woman less suitable for tech and leadership than the average man. He is not saying that his female colleagues are less suitable than his male colleagues. He only says that these biological phenomena may explain why we see less women in tech and leadership, and that trying to enforce an equal representation of the sexes in tech jobs in Google may be misguided.

The only way that someone can interpret Damore saying something about his colleagues is if you assume that what he says about the average woman holds for all women — and he quite clearly says the opposite; he even spends a considerable portion of his treatise discussing that it is a mistake to regard each individual as representative for their sex. That, however, is what is done when you treat women differently than men just because they are women (which is what Google does). You might think that a CEO who fires a person would at least check whether the reason that he gives for the firing holds water, but it looks like Damore’s firing was not because of what he factually said, but because many people found him offensive — the numerous tweets and responses on the Internet suggest as much.

So what could people find offensive about his text? It cannot be the statement that people should not be equated to the average of their sex — only the opposite could be found offensive. Considering Pichai’s statement, and what I read in many of the comments on Damore’s text, what is found offensive is Damore’s suggestion that the cause for observed behavioral differences between men and women may be partially found in biology.

If you examine literature, you will find that there are certain scientists who state that, while there are definitely biological differences between men and women, they are much smaller than generally assumed, and that all behavioral differences between men and women are the result of culture. If you believe that what these scientists say is undeniably true, then you may feel offended by anyone who is merely suggesting that there are innate differences between men and women. In comparison, if you believe that the Bible speaks holy truth, then you will probably feel offended by anyone who says that there are indications that the Earth is 4.5 billion years old. Such a person is an infidel and should be burned at the stake.

However, what these scientists say is not scientific consensus. Other scientists state that, while culture obviously has an influence on behavior, there are clear, experimentally demonstrable, indications that the innate biological differences between men and women influence interests and behaviors, which partially explain the differences that we observe between the averages of men and women on a population level. Even without running any experiments, I’d say that there are numerous easily-observable indications that biology influences behavior. I give three of them:

  • In most animals, mammals in particular, there are clear behavioral differences between the sexes. Animals do not have cultures, so these differences are all biologically driven. I see no reason why humans would be exempt from these biological influences.
  • Biological differences between men and women result in the production of different quantities of particular hormones. We know that hormones not only influence physical development, but also behavior. In particular, in puberty, when the hormonal production starts to get radically different between men and women, the behaviors of men and women start to diverge extensively. Moreover, if you feed people particular sex-related hormones, their behaviors change too. Again, this shows that biology influences behavior.
  • In western countries, people have been given a lot of freedom of choice. In north-western Europe, the cultural pressures on men and women to stick to particular roles in society have been mostly eradicated. If the theory that all behavioral differences between men and women are the result of culture would be correct, the differences in interests between men and women would dwindle away. This has not happened. Women have entered the job market, but on average choose more people-oriented jobs than men; for instance, 50 years ago there were many more male physicians than female physicians (as few women had a job), but these roles are reversed now. However, 50 years ago there were many more male engineers than female engineers, and these roles have not changed. Also, 50 years ago it was not common to have a part-time job for men or women, but nowadays women predominantly work in part-time jobs, while men do not. If release of cultural pressure increases differences in behavior, that is a clear indication that the differences are at least partially caused by something else than culture.

Moreover, the arguments that I read on why some people believe that only culture plays a role in determining differences in interests between men and women seem faulty to me. For instance, it is pointed out that the roles of men and women are not innate, as there is an African tribe that has a matriarchal structure in which women have the role that is traditionally associated with men, and vice versa. To me, it seems that the existence of such a tribe is the exception that confirms the rule: everywhere in the world, one tiny place excepted, men take a particular role and women another, which shows that these roles are nearly universal. The fact that the exception exists only shows that culture may overrule the traditional roles. Now, I am not an expert on this topic, and thus I might have missed some very convincing arguments for assigning all responsibility for behavioral differences to culture. However, I think that if convincing arguments would exist, the scientific world would have reached consensus by now, and that clearly is not the case.

The claim that only culture influences behavior is much, much stronger than the claim that behavior is partially the result of culture and partially the result of biology. If you want to make the first claim, you have to come up with strong evidence that biology does not play a role. I have not seen that evidence. And since there are no ethical experiments that you can perform to exclude biology as a partial cause for observed differences between men and women on average, it will be very hard to come up with it.

I am not saying that it is definitely false that only culture is responsible for behavior. It might be true, though considering the observations that we can make, it is unlikely to be true. The problem is that just like you cannot prove that biology has no influence on behavior, you cannot disprove that only culture is responsible for behavior. However, claiming that biology has no influence means upholding a belief in the face of observable facts that point in a different direction. It is a belief that has to resort to shaky claims like “unconscious biases” to explain why certain observable facts exist. And since it is hard to use factual arguments to defend this belief against grounded criticism, believers may feel a need to attack the credibility of non-believers by accusing them of being reprehensible sub-humans, i.e., “sexists.”

James Damore’s statements seem innocent to me, but to someone who upholds an ideology that is based on the premise that there are no biological differences between the sexes, they are dangerous, and must be attacked. Labeling him a “sexist” is an easy way of making him harmless.

Diversity IV: Google ideology

August 8, 2017

Senior Google engineer James Damore wrote a memo criticizing the Google mantra that the underrepresentation of women in tech and leadership is solely caused by biases. He poses that there are scientific indications that biological traits may make women on average less interested or even less suitable for such roles. Note that he is very careful in underlining the “on average,” explicitly stating that there is an enormous overlap between the populations of men and women in all their attributes, and that people should not be assessed as the average of their groups, but on an individual level. He also explicitly states that he does not deny that sexism or biases exist, and that he values diversity and inclusion. His main issue is that he found that he is working in a psychologically unsafe environment, since even suggesting that anything but biases are the cause for different representations of men and women in the tech industry is cause for shaming and misrepresentation, and risks being fired.

Google promptly confirmed his statements by firing him for “perpetuating gender stereotypes.”

Contrary to many of those who accused James Damore of sexism, I read his 10-page document and examined the links he included (which, unfortunately or maliciously, were removed by Gizmodo when they republished his memo). It is a well-spoken treatise, in which the only sexist statement that I could find amounts to the suggestion that there are biological differences between men and women, and providing scientific support for that statement. I know that in many circles such a statement is considered sexist, but that means that in these circles scientific facts are considered sexist.

I do not agree with everything that Damore states, as I think some of the underlying science is oversimplified. But the point is not whether Damore is correct in everything that he says. The point is that bringing up viewpoints which challenge the reigning ideology should lead to an open discussion, not be a reason for getting fired.

There is overwhelming scientific evidence that there are biological differences between men and women (it feels weird to even have to make such a statement — I mean, most people know how sexual reproduction works, right?). There is evidence that these biological differences lead to some innate differences in interests and aptitudes between men and women on average (emphasis on the “on average”). Even if you wish to marginalize the biological causes for the average differences in interests and aptitudes between men and women, these differences can be observed. That means that yes, the stereotypical man differs from the stereotypical women. It does not mean that every person is like the stereotype of their sex. It just partly explains the differences that we can observe on a population level. To what extent are these differences caused by biology, and to what extent are they the result of cultural biases? We don’t know. And if we are not allowed to have a discussion about it, we will never know, and we can never come to diversity programs which tackle the biases in an effective manner.

I have argued before that some of the enforced diversity programs that I have observed are misguided. I have been cautious in expressing that opinion, as I know it can lead to fast accusations of being sexist, which is a label that you better avoid. However, I still felt sufficiently safe in expressing that opinion as I think that I am working in an environment where people are at least willing to examine and discuss evidence rather than condemn automatically. Having to work in an environment where expressing a well-founded idea can lead to getting fired seems horrifying to me.

Science stops where ideology takes over. It seems to me that a company such as Google, which is founded on technical and scientific research, is doing itself a great disservice by trying to silence those who hold a contrary opinion or are trying to challenge ideas.

AI storytelling

August 6, 2017

Recently I read in a newspaper a list of average predictions of AI researchers on when certain achievements in AI would be reached. There were several predictions for the coming 5 to 10 years, such as an AI winning a game of StarCraft against a human champion (2022), and composing a top-40 song (2027). Only one prediction was made a considerable number of years in the future, namely writing a New York Times bestseller (2050).

I was not surprised about the short-term predictions. These were all straightforward extrapolations of today’s research. For instance, a lot of time is invested in creating StarCraft AI, and we know that a computer already has a huge advantage over humans in its speed; it just needs to get a bit better tactically to defeat human champions. Similarly, computers already write music that is indistinguishable from what humans compose, so I can see a computer write a top-40 hit today — the main problem I see for writing a song that lands in the top-40 is that the quality of the song is only a very small factor in determining whether it becomes a hit.

Why is writing a bestseller considered to be much more difficult than any of the other AI tasks?

Writing a novel is very different from composing music or creating a painting. When listening to music or watching a painting, people give their own interpretation to what they hear or see, and the computer can get pretty far by simply recombining elements of music or paintings that it has been trained with. For instance, David Cope’s first attempts at letting a computer compose music amounted to hacking Bach’s sonates into measures which were stored in a database, and then recombining these measures by making sure that the last note of each measure was the same as the last note that originally came before the measure that was chosen next. This resulted in thousands of sonates which sounded more or less like Bach sonates. The computer did not need to understand what it was doing. In contrast, when writing the text of a novel an AI needs to understand what it is writing, otherwise the text will not make sense.

You might think that no real understanding is needed to create a text, as we already see human-readable text produced by computers today. In particular, newspaper articles are often written by computers. Examples are weather reports, stock market analyses, and sports reports. However, in these cases the computer is not really producing an original text. The computer simply gets the data that need to be reported (temperatures, market fluctuations, goals scored) and translates the data according to specific rules into a text. No creativity is needed. To produce a novel, the computer must come up with an original, sensible plot that has relevance to humans, and turn that plot into a captivating text. As far as I can see, the computer cannot do that without quite deep understanding of the human condition, human emotions, human language, and the human world. And at the moment, we have no idea how we can give a computer such understanding.

Someone who has heard of “deep learning” might think that it is sufficient to train a computer with existing novels to allow it to produce a new novel. But what are you then really training the computer with? You are training it with strings of words. This might lead to a computer being able to recognize that certain strings of words are likely to be sensible sentences, but not that a string of 40,000 words is a sensible, well-readable novel. Examining words does not equate examining the plot, the meaning, or the literary quality of the novel.

You might think that we may solve the problem of making the computer create a bestseller by simply letting it produce random texts, and then assess the quality of the texts with respect to them being a bestselling novel. Using an evolutionary approach, this might reasonably quickly lead to a novel that scores high on bestselling quality. This approach might actually have merit to it, if we could give the computer an algorithm that rates a text as a bestseller. We do not have that, as even humans cannot predict whether a novel will be a bestseller.

Take, for instance, the first Harry Potter novel, which was rejected by virtually all British publishers, and only produced in a very small quantity by the last one because his 8-year-old daughter liked the book. Considering Rowling’s unskilled writing and weak plot construction, it is not surprising that the publishers did not see her combination of a mid-20th-century boarding school novel with a childish version of Lord of the Rings as likely to succeed. Expert humans did not assess Harry Potter to be the commercial success that it came to be. So expert humans cannot teach a computer to do it for them.

If expert humans cannot tell a computer how to rate a novel, you might still envision an approach by which a computer determines by itself an evaluation function for bestselling quality. If you have millions of books which are all labeled with their relative sales figures, and extra data with respect to the time when and place where the books were a success, you may be able to use them to train a computer to come up with an evaluation function that can accurately predict from the contents of a novel whether or not it will be a success. Perhaps that is possible. Perhaps not. Frankly, I think that if it was easy, then all those publishers who rejected Harry Potter would have internalized an algorithm like that and would at least have seen some value in the book, but evidently they did not.

If a computer would have a much deeper understanding of the world than any human has, it would have insights that humans cannot have. And with such insights, be able to predict bestselling quality. I believe that in principle it is possible for a computer to have much deeper understanding of the world than humans have, but we are far, far away from having such a computer since, as far as I know, nobody has any idea on how to give a computer what is needed for it to gain understanding. The conclusion that I must draw is that it probably is not impossible to get a computer to write a bestseller, but that creating a computer that can do that is not a straightforward extrapolation of the state-of-the-art in AI. Therefore, attaching any year to it is unwarranted.

So where is the year 2050 coming from in the minds of the average AI researcher? I think it represents “About 25 years in the future? Who knows what we can do by then!”

Basically, predicting an AI achievement for 2050 is equivalent to AI researchers saying “we have no idea.”

Ethical cars

June 28, 2017

The first completely autonomous machines that will invade society as a whole might very well be self-driving cars. With “completely autonomous” I mean that these cars will perform their duties without any interaction with their owners, making their own decisions. Obviously, there is great commercial value in such transportation devices. However, allowing them to take responsibility for their own actions in the real world may involve considerable risk. For how can we be ensured that the decisions of these cars are in alignment with what we humans find morally acceptable?

A typical scenario that I get confronted with, is a self-driving car which has to swerve in order to avoid hitting a dog, but if it does that, as a consequence, hits a human. While obviously we would prefer the car to avoid hitting both dogs and humans, if there is no choice but to hit one of them, we would like the car to then choose to hit the dog. A potential solution to this scenario would be to outfit the car with ethical rules along the lines of Isaac Asimov’s three laws of robotics, e.g., with a rule that says “do not harm humans” given priority over a rule that says “do not harm dogs.”

However, the specification of such rules is not a trivial matter. For instance, it is logical that a rule would state “you have to obey the laws of traffic.” This would entail that the car is not allowed to drive through a red light. But what if the car stops for a red light, while a traffic warden motions it to continue driving? You may update the rule stating that an exception is made for directions given by traffic wardens. But what if there is no traffic warden, the car has stopped for a red light, and a police car sounding its horn is coming from behind and cannot get past unless the car drives forward a bit (through the red light) to park to the side? You may update the rule even more to take that situation into account, but is this then covering each and every situation in which the car is allowed to break the rule that it should stop for a red light? Probably not.

The point is that human drivers ever so often break the rules of traffic to avoid a problematic situation. You are trying to pass another car which drives fairly slowly, and suddenly that car speeds up. You can still get past, but you have to drive faster than the speed limit for a few moments. So that’s what you do. Or you are at a crossing in a deadlock with two or three other cars. One of them has to break the rules and start moving, otherwise they will all be stuck there forever.

The point is that human drivers improvise all the time. They know the traffic rules, they have been trained to recognize safe and dangerous situations, and know how to anticipate on the behavior of other drivers. And sometimes they bend or break the rules to avoid problems. A self-driving car that cannot improvise is dangerous. However, a consequence of the need for improvisation is that any rules that we would want to impose on the car, it should be able to break. The only alternative would be to envision each and every situation in which the car could find itself and specify the exact behavioral rules for it to deal with all those situations. Clearly, that is impossible.

So how do we get a car to behave like a responsible driver without laying down an endless list of rules? The answer is: by training it. First, we let the car drive in a highly realistic simulation, punishing it every time that it causes a situation that is undesirable, and rewarding it when it manages to perform well. A learning structure can incorporate the lessons that the car learns, thereby bringing it ever closer to being a model driver. Once it is perfect or almost perfect in the driving simulation, it can be let loose on the road under the guidance of a human, continuing learning. In the end, it will behave on the road as well as, and probably a lot better than, a good human driver.

How will such a car deal with a choice between hitting a human or a dog? It is likely that similar situations will have cropped up during the training process — maybe not with exactly the same race of dog and the same human as in the real situation, but as the car has been trained instead of having been given specific rules, it has the ability to generalize, and it will make the choice that is closest to what the training would have rewarded while avoiding choices that the training most likely would have punished. In other words, it will choose to hit the dog to avoid hitting the human, just as it would likely hit a cat, a moose, a badger, or a duck in order to avoid hitting a human.

It might, however, in a situation where someone pushes a mannequin in the road, hit a dog to avoid hitting the mannequin — not because it thinks the mannequin is a human, but because the situation of hitting the mannequin more closely resembles hitting a human than the situation of hitting a dog does. If we do not want the car to make that choice, we should ensure that its training regime includes situations in which it has to deal with objects that resemble humans but are not humans. This, however, could lead to a situation in which it chooses to hit a highly inert human to avoid hitting a dog. That’s the problem with allowing a car to make its own choices based on how it is trained: you can probably always find an exceptional situation in which it is not doing what we hoped it would do. The same is true for humans, of course, and in the end the self-driving car will probably still be a much safer driver than any human.

So if one wonders how we can be sure that the ethics of a self-driving car will be acceptable to us humans, the answer is that we can only draw conclusions based on observations of how the car deals with tough situations. We will not be able to open up the car’s brain and examine some kind of ethics module to read how it will deal with situations that come up. Therefore there is no way for us to be “sure.”

We can only draw comfort from the fact that if at some point the car takes a decision that we find doubtful, we can punish it and it is likely to make a different decision when a similar situation comes up again. It will be less stubborn than the average human in that respect.

Diversity interlude

April 24, 2017

I don’t know if I am yet done with the topic of diversity. The discussions about diversity are currently rather intense, and I feel I still have to say quite a bit about it. In general, I have noticed that my position is that diversity is based for at least a considerable part in biology, while those who are on the side of social engineering are of the opinion that it is all culturally determined. In that respect, I discovered a fascinating series of documentaries by the Norse comedian and sociologist Harald Eia, called “Hjernevask“. I haven’t yet completed watching them, but up to now I am pretty surprised about some of his discoveries. The two things that stand out to me are: (1) the role of biology in determining differences between genders and races is much bigger than I had previously assumed, and (2) evidently in “enlightened” western countries such as Norway the thought that differences can be the result of biology is actively shunned even by people whose job it is to know better. Heartily recommended.

Diversity III: Diverse equalities

April 8, 2017

This post is a follow-up to my post on the wage gap, and my post on the glass ceiling. In these posts I showed that there is no reason to assume that gender discrimination is at work in determining the salaries or promotion chances of women.

In The Netherlands, “equality” is generally seen as an ultimate good. Which is why underrepresentation of women and minorities in professions and organizations is often seen as inherently wrong. Certain political movements have made fighting inequality into a main feature of their program. For instance, minister Jet Bussemaker is quite miffed about the fact that only 18% of full professors in The Netherlands are female, and she therefore forces universities to appoint more women to such positions. For 100 new female full professors she makes extra money available for the next five years, and on top of that she also wants universities to put women on 200 chairs that are currently held by men.

Basically, this means that Bussemaker wants to change the top selection criterion for appointing someone to the position of full professor from “we want the best person for the job” to “we want someone who has no Y-chromosome.”

For me, a major question is why she (and with her many others, such as professor Derks) believes that the underrepresentation of women in this area is the result of “inequality.” The straightforward line of thinking that is commonly held, is that 50% of society consists of women, and thus, if women were treated equal to men, 50% of full professors would be women. Naturally, such a thought process is too simplistic. It is a silly notion to divide Dutch citizens according to particular criteria such as gender, immigration status, age, sexual orientation, or physical attributes, and then expect that the percentages you end up with are reflected by a certain profession. As I showed in the previous two posts, if you take into account unequal participation in the job market, the 18% female full professors that we have now are close to what you may expect if men and women have equal opportunities (assuming that in academics the statistics for job participation are about average for The Netherlands).

The point is that what minister Bussemaker and others like her want to see are not equal opportunities, but equal outcomes. What they fail to acknowledge is that if you confront 100 men and 100 women who already hold a good job with the prospect of another job with more status, offset by a greater workload and higher responsibility, 80 of the 100 men will accept that job, while 80 of the 100 women will walk away. In our society where people are given a lot of freedom of choice, women tend to make different choices than men, and thus equal opportunities do not lead to equal outcomes.

Assuming that minister Bussemaker does not want to take away freedom of choice, there are only two ways open to get a higher percentage of female professors: either making men less ambitious, or lowering the requirements for women (and only for women) to get the job. I have no idea how you would accomplish the first, so I am not surprised that she chose the latter.

Yes, I know that there are female associate professors who are ready to become full professors. But there are far more male associate professors with the same ambition. In an equal-opportunities environment, which we have now, you end up with more men being appointed than women, because with the same quality there are more men than women available for the job. When I say that Bussemaker lowers the requirements for women to become professors, I am not saying that that puts universities in a position where they are forced to promote female associate professors who are not ready to become full professors — I am only saying that these women no longer have to compete on a level playing field with men. It is possible for them to do a worse job than some men would do, and still be chosen for promotion over those men.

Professor Derks states that women do not want to adapt to the standards set by men. I assume that she means that many women want to be full professors in a part-time job with a nice balance between their work and home life. This sounds a lot like having your cake and eating it too. In practice, as with most high-demand jobs, being a full professor entails sacrificing much of your home life for your work. It is really a tough job. Maybe that is a standard set by men, but it means that if, as a woman, in an equal-opportunities environment you want to compete with men, you have to meet that standard. By replacing the equal-opportunities environment with an equal-outcomes environment, women can get away with doing a worse job than men.

In sports, women usually do not compete with men, as men tend to be physically stronger and faster. Consequently, female sports are seen as inferior to male sports. By creating a special academic division for women to compete in, where standards are lowered, you create a situation in which female academic achievements are seen as inferior to male academic achievements. If I were a female professor today, who clearly got the job because she competed successfully on a level playing field with men, I would not be happy about that.

The 100 extra positions that Bussemaker creates are filled by women only, which leads to a situation where some top-quality men see full professorships going to women who, even if they do a good job, do a worse job than these men would do. And if the rest of Bussemaker’s plans get executed, they know that their chances to ever be promoted are diminishing rapidly, as they have to compete for a decreasing number of positions. If I was a young man who was contemplating a career in academics today, considering the unequal treatment of men and women and the lowered career prospects, I would probably choose a job outside the academic world, regardless of how much I love scientific research. All in all, the policies of Bussemaker may do serious harm to the quality of Dutch academics.

In conclusion, the belief that forcing universities to appoint more female professors will resolve some sort of societal injustice is misguided. In fact, it creates a societal injustice by lowering the standards that women need to meet to become full professors, while increasing them for men.

I wish to point out that there is another solution for the problem that certain female associate professors are not getting promoted, even though they are ready for it. That solution is to make the promotion to full professor an automated process when you meet certain criteria. This is the case in many countries outside The Netherlands, such as the UK. If you do the work, you get the position. I am in high favor of that.

Making the promotion to full professor based on individual accomplishments rather than the academic structure with only a limited number of available chairs, has the added benefit that there will be more full professorships in The Netherlands, and thus the requirements for the job may be slightly lowered for men as well as women.

Diversity II: Diverse choices

April 6, 2017

This post continues my previous post on the topic of diversity, in which I discussed the first part of the sentence “Women still earn less and are slower to be promoted” (lifted from an article in De Volkskrant). In the present post, I delve into the second part. Is it true that women are slower to be promoted, and if so, is that (as is often suggested) the result of gender discrimination?

What I assume the author is referring to with the second part of their statement is the fact that the representation of women in higher positions in companies and organizations is considerably less than the representation of men. In the academic world in the Netherlands, for instance, only 18% of full professorships are held by women.

There is no debate about the fact that women are underrepresented in higher positions. What is up for debate is whether this is the result of gender discrimination. Despite the often-expressed but seldom-substantiated notion of some sort of “glass ceiling” that keeps women down, I would argue that the state of affairs is mostly the result of personal choices.

In The Netherlands, almost every job can be done part-time. In practice, we find that men usually take full-time jobs, while women predominantly choose to work part-time. According to the Dutch Central Bureau for Statistics (CBS), in The Netherlands in 2017, only 33% of working men held a part-time job, compared to 78% of working women. The ministry of OCW (Education, Culture, and Science) reports that, on average, women have a work-week of 25 hours, while men have a work-week of 36 hours, i.e., on average women work 1.5 days less per week than men. It also reports that only 71% of women between 20 and 65 years of age have a job, as opposed to over 82% of men.

Referring to statements of professor Derks in the interview I mentioned above, working part-time has a big influence on career opportunities — thus, if you are interested in having a career, you should realize that working full-time is more or less a necessity. The detrimental effects of working part-time are two-fold:

First, when working part-time you simply accomplish less in your career in the same number of years than someone with the same abilities who works full-time. This leads to a lower status among peers, and a less impressive CV.

Second, professing the desire to work part-time shows a lack of ambition and motivation, which consequently leads to a reduced willingness of an employer to hand out a promotion.

The second effect is in line with differences between the average man and the average woman in how they view work (see, for instance, the report from Monsterboard). Typically, men are more driven by status than women: men far more than women seek a higher salary, possibilities for personal growth and development, independence, and responsibility; women far more than men seek possibilities to work part-time, possibilities to work from home, and a nice balance between work and home life. You may translate that as “on average, women are less invested in their work than men.” That cannot help in having a career. The employer who says: “What I really want from my personnel is that they focus on their home life” has not been born yet.

Note that by no means I am saying that all women lack the motivational drive, the capacity, the experience, and the willingness to have a career. Obviously a considerable number possess these attributes, which is why we see such women in higher positions. They made the choice to invest in their work rather than their personal life, just like many men do.

It is a great benefit of our society that one can actually choose how to balance work and home life. But when someone chooses to be less invested in work, he or she should expect having a hard time getting into higher positions. That holds for women as well as for men. Since, in general, women tend to be much less invested in work than men, it is not surprising that in higher positions, the number of women dwindles. Whether or not the prevalence of women in part-time jobs fully explains the low number of women in higher positions is unknown. Saying that it is the result of gender discrimination, however, is unwarranted.

The only extensive scientific research on the topic that I could find is an article by Williams and Ceci of Cornell University in PNAS, who found that for tenured assistant-professor positions in biology, engineering, economics, and psychology in the US, if you compare female candidates with male candidates of equal quality and matching lifestyles, the chance for female candidates to be hired is twice as high as the chance for male candidates. The conclusion is that gender discrimination is at work here, but it is to the considerable advantage of women.

I wish to finalize this discussion with a simple calculation. Knowing that 82% of men have a job against 71% of women, and that 67% of working men have a full-time job against 22% of women, if we assume that there are about the same number of men and women between the ages of 20 and 65, that means that 78% of the people who work full-time are men, against only 22% women. If we compare that 22% with the 18% of women with full professorships in The Netherlands, it strikes me that these numbers are pretty close. So, while 18% sounds low, it is actually close to what you can expect if you take into account the generally accepted assumption that working full-time is a requirement for getting a high-end job.

In summary, there is no reason to assume that the underrepresentation of women in higher positions in companies and organizations is the result of gender discrimination. Still, there are political forces at work that try to enforce placing more women in higher positions. These forces are particularly strong in the academic world, where universities are ordered by ruling politicians to appoint more women as full professors. What I think about those forces I will discuss in a follow-up post.

Diversity I: Diverse wages

April 1, 2017

The term “diversity” refers to the uniqueness of individuals. In policy making, the theme of “diversity” refers to the inclusion of underrepresented groups of people in particular functions or domains.

In recent years, the theme of “diversity” has infiltrated many aspects of society, and now has serious impact on policies in governmental matters and professional life. I am quite wary about the effects of the diversity discussions. It is such a complex theme, however, that it is hard to formulate statements on it, without running serious risks of finding oneself under a bombardment of accusations of being some kind of –ist or –phobe, which then puts an end to any chance of getting into a civil parlay.

I think that the discussions around this theme, now they are widely politicalized, will only intensify in the coming years. As a scientist, I have the responsibility to deal with these matters as objectively as possible. As I am increasingly involved in policy making and providing advice in matters of policy, it is important to me to be able to express opinions in this domain that are as close as possible to the truth, without letting emotions and political pressure get in the way.

Today’s ranting is instigated by an interview in the Dutch newspaper De Volkskrant with Belle Derks, professor of Social and Organizational Psychology at Utrecht University, specialized in “gender equality.” I had many thoughts while reading this interview, and I might get back to it in the coming days. For now, I want to zoom in to the second sentence of the main text of the article, which is “Vrouwen verdienen nog altijd minder en klimmen minder snel op naar hogere functies” (“Women still earn less [than men] and are slower to be promoted”). For clarity, I wish to point out that this is not a statement of professor Derks as far as I can see, but part of the introduction of the journalists.

The quoted sentence is factually correct, but it suggests something that is factually wrong, namely that women get discriminated as far as their wages and promotion opportunities are concerned. With respect to wages, one only needs to read the report of the CBS (the Dutch Central Bureau for Statistics, a governmental organization that prepares reports on all aspects of Dutch social and economic life) on this matter, which is available from their website. This report investigates specifically gender differences in salaries in The Netherlands, and is updated up to the year 2014. In this report one can read the following:

For jobs in companies, the hourly salary of women is about 20% less than that for men. For government jobs, the difference is 10%. However, these numbers are not corrected for differences in actual work, i.e., it just averages the hourly wages of all women that have a job, and all men that have a job, regardless of position, education, or responsibilities. If you correct the differences for about 20 factors, which include age, experience, and position, then the percentages drop to 7% and 5%, respectively.

Does that mean that gender is the explanation for the remaining salary differences? The CBS report states that there is no reason to suppose that gender discrimination is at work here, as there are quite a few factors that can explain the remaining differences, which they were unable to take into account for lack of detailed information. These are, among others, motivation, job level, and secondary employment conditions such as exchanging salary for extra vacation days. As the report states that women are far more likely than men to work part-time, one might expect that in particular women would take the opportunity to sacrifice some salary for extra spare time, though the report does not delve into that.

As there are plenty factors available that may explain the remaining small differences in average wages between men and women, the CBS found no indication that gender discrimination is an explanation for these wage differences. I think that settles the matter.

What I found particularly interesting is that the report also made clear that up to 36 years of age, women actually earn a higher salary than men for doing the same work. However, again this has nothing to do with gender, but with the fact that among younger people, women have, on average, a higher education than men. Between 36 and 45 years of age, there are no significant differences in salaries between men and women. Over 45 years of age, men tend to earn more than women, for which an easy explanation is that in those age groups, men tend to have a higher education and more job experience than women.

How these trends will be extrapolated to the future is hard to say. If many women between 30 and 40 years of age are losing interest in work and accept lower salaries for, for instance, a more extensive home life, the picture might remain as it is now. If women keep focusing just as much as men on their careers and earnings, clearly in about 30 years, women will on average earn more than men in all age groups.

The discussion above has contextualized the first part of the sentence “Women still earn less and are slower to be promoted,” and refutes the suggestion that women earn less than men because of gender discrimination. The second part needs another discussion, which I will get to later.

For now, the conclusion is that in The Netherlands a small gender wage gap exists, but that there is no reason to think that the explanation for it has to do with gender discrimination.