Women in STEM*

This is a special post towards International Women's Day (March 8th). Every year I find myself enthusiastically conveying my thoughts about the topic to the people around me, so I thought I might as well share it with a broader audience. As always, this post presents my very limited knowledge/interpretation to a broadly discussed and studied topic. However, it may be a bit off topic for this blog, so if you're only interested in computational stuff, you can focus on section 3.

1. The Problem
Even though we are half of the population, women are quite poorly represented in STEM:

USA: the percentage of computing occupations held by women has been declining since 1991, when it reached a high of 36%. The current rate is 25%. [2016, here] OECD member countries: While women account for more than half of university graduates in scientific fields in several OECD countries, they account for only 25% to 35% of researchers in most OECD countries. [2006, here]

2. The Causes (and possible solutions)

2.1 Cognitive Differences
There is a common conception that female abilities in math are biologically inferior to those of males. Many highly cited psychology papers prove differently, for example:

"Stereotypes that girls and women lack mathematical ability persist, despite mounting evidence of gender similarities in math achievement." [1].

"...provides evidence that mathematical and scientific reasoning develop from a set of biologically based cognitive capacities that males and females share. These capacities lead men and women to develop equal talent for mathematics and science." [2]

From https://imgs.xkcd.com/comics/how_it_works.png.

In addition, if cognitive differences were so prominent, there wouldn't be so many women graduating in scientific fields. It seems that the problem lies in occupational gender segregation, which may be explained by any one of the following:

2.2 Family Life
Here are some references from studies conducted about occupational gender segregation:

"In some math-intensive fields, women with children are penalized in promotion rates." [3]

"[...] despite the women's movement and more efforts in society to open occupational doors to traditional male-jobs for women, concerns about balancing career and family, together with lower value for science-related domains, continue to steer young women away from occupations in traditionally male-dominated fields, where their abilities and ambitions may lie." [4]

"women may “prefer” those [jobs] with flexible hours in order to allow time for childcare, and may also “prefer” occupations which are relatively easy to interrupt for a period of time to bear or rear children." [5] (the quotation marks are later explained, indicating that this is not a personal preference but rather influenced by learned cultural and social values).

I'd like to focus the discussion now on my local point view of the situation in Israel, since I suspect that it is the most prominent cause of the problem here. I would be very interested in getting comments regarding what it is like in other countries.

From http://explosm.net/comics/2861/

According to the Central Bureau of Statistics, in 2014, 48.9% of the workers in Israel were women (and 51.1% were men). The average salary was 7,439 NIS for women and 11,114 for men. Wait, what?... let me introduce another (crucial) factor.

While the fertility rate has decreased in all other OECD member countries, in Israel it remained stable for the last decade, with an average of 3.7 children per family. On a personal note, as a married woman without children, I can tell you that it is definitely an issue, and "when are you planning to have children already?" is considered a perfectly valid question here, even from strangers (and my friends with 1 or 2 children often get "when do you plan to have the 2nd/3rd child?").

Paid maternity leave is 14 weeks with a possibility (used by anyone who can afford it) to extend it to 3 more unpaid months. Officially, any one of the parents can take maternity leave, but in practice, since this law was introduced in 1998, only roughly 0.4% of the parents who took maternity leave were fathers. 

Here is the number connecting the dots, and explaining the salary gap: in 2014, the average number of work hours per week was 45.2 for men and 36.7 for women. The culture in Israel is torn between the traditional family roles (mother as a main parent) and the modern opportunities for women. Most women I know have a career in the morning, and a second job in the afternoon with the kids. With a hard constraint of leaving work before 16:00 to pick up the kids, in a demanding market like in Israel, it is much harder for a woman to get promoted. It poses the high-tech industry, in which the working hours are known to be long, as a male-dominated environment. Indeed, in 2015, only 36.2% of the high-tech workers in Israel were women.

This situation is doubly troubling: on the one hand, it is difficult for women who do choose demanding careers. They have to juggle between home and work in a way that men are never required to. On the other hand, we are oriented since childhood to feminine occupations that are less demanding in working hours. 

Don't get me wrong, I'm not here to judge. Being a feminist doesn't entail that the woman must have a career while the man has to stay at home with the children. Each couple can decide on their division of labor as they wish. It's the social expectations and cultural bias that I'm against. I've seen this happening time after time: the man and the woman both study and build up their careers, they live in equality, and then the birth of their first child, and specifically maternity leave, is the slippery slope after which equality is a fantasy. 

To make a long story short, I think it is not women the market is against, but mothers. When I say "against" I include allegedly good ideas such as allowing a mother to leave work at 16:00. While I'm not against leaving work at 16:00 (modern slavery is a topic for another discussion...), I don't see why this "privilege" should be reserved only for mothers. In my humble opinion, it will benefit mothers, fathers, children and the market if men and women could each get 3 days a week to leave work as "early" as at 16:00. It wouldn't hurt if both men and women will have the right to take parental leave together, developing their parenthood as a shared job. This situation will never change unless the market will overcome ancient society rules and stop treating parenthood as a job for women.

2.3 Male-dominated Working Environments 
Following the previous, tech workplaces (everywhere) are dominated by men, so that even women who choose to work in this industry might feel uncomfortable in their workplaces. Luckily for me I can't attest this by my own experience: I've never been treated differently as a woman, and have never felt threatened or uncomfortable in situations in which I was an only woman. This article exemplifies some of the things that other women experienced:

"Many [women] will say that their voice is not heard, they are interrupted or ignored in meetings; that much work takes place on the golf course, at football matches and other male-dominated events; that progress is not based on merit and women have to do better than men to succeed, and that questions are raised in selection processes about whether a woman “is tough enough”."

From http://www.phdcomics.com/comics/archive.php?comicid=490

I've only become aware of these problems recently, so I guess it is both a good sign (that it might not be too common, or at least that not all women experience that), but also a bad sign (that many women still suffer from it and there's not enough awareness). This interesting essay written by Margaret Mitchell suggests some practical steps to make women feel more comfortable in their workplaces.

Of course, things get much worse when you consider sexual harassment in workplaces. I know the awareness to the subject is very high today, an employer's duty to prevent sexual harassment is statutory in many countries, and many big companies require new employees to undergo a sexual harassment prevention training. While this surely mitigates the problem, it is still too common, with a disturbing story just from the last week (and many other stories untold). As with every other law, there will always be people breaking it, but it is the employers' duty to investigate any reported case and handle it even at the cost of losing a valuable worker.

2.4 Gender Stereotypes 
Simply because it's so difficult to change reality; even if some of the reasons why women were previously less likely to work in these industries are no longer relevant, girls will still be less oriented to working in these fields since they are considered unsuitable for them.

From https://www.facebook.com/DoodleTimeSarah/

An interesting illustration was provided in this work, where 26 girls (around 4 years old) were shown different Barbie dolls and asked whether they believed women could do masculine jobs. When the Barbie dolls were dressed in "regular" outfits, many of them replied negatively, but after being showed a Barbie dressed up in a masculine outfit (firefighter, astronaut, etc.), the girls believed that they too could do non-stereotypical jobs.

This is the vicious circle that people are trying to break by encouraging young girls to study scientific subjects and supporting woman already working in these fields. Specifically, by organizing women-only conferences, offering scholarships for women, and making sure that there is a female representative in any professional group (e.g. panel, committee, etc). While I understand the rational behind changing the gender distribution, I often feel uncomfortable with these solutions. I'll give an example.

Let's say I submitted a paper to the main conference in my field, and that paper was rejected. Then somebody tells me "there's a women-only workshop, why don't you submit your paper there?". If I submit my paper there and it gets accepted, how can I overcome the feeling of "my paper wasn't good enough for a men's conference, but for a woman's paper it was sufficient"?

For the same reason, I'm uncomfortable with affirmative action. If I'm a woman applying for a job somewhere and I find out that they prefer women, I might assume that there was a man who was more talented/adequate than me but they settled for me because I was a woman. If that's true, it is also unfair for that man. In general, I want my work to be judged solely based on its quality, preferably without taking gender into consideration, for better and for worse.

I know I'm presenting a naive approach and that in practice, gender plays a role, even if subconsciously. I also don't really have a better solution for that, but I do hope that if we take care of all the other reasons I discussed, this distribution will eventually change naturally. 

3. Statistics and Bias

Last year there was an interesting paper [6], followed by a lengthy discussion, about gender stereotypes in word embeddings. Word embeddings are trained with the objective of capturing meaning through co-occurrence statistics. In other words, words that often occur next to the same neighboring words in a text corpus are optimized to be close together in the vector space. Word embeddings have proved to be extremely useful for many downstream NLP applications.

The problem that this paper presented was that these word embeddings capture also "bad" statistics, for example gender stereotypes with regard to professions. For instance, word embeddings have a nice property of capturing analogies like "man:king :: woman:queen", but these analogies contain also gender stereotypes like "father:doctor :: mother:nurse", "man:computer programmer :: woman:homemaker", and "he:she :: pilot:flight attendant".

Why this is happening is pretty obvious - word embeddings are not trained to capture "truth" but only statistics. If most nurses are women, they would occur in the corpus next to words that are more likely to occur with feminine words than with masculine words, resulting in higher similarity between nurse and woman than nurse and man. In other words, if the input corpus reflects stereotypes and biases of society, so will the word embeddings.

So why is this a problem, anyway? Don't we want word embeddings to capture the statistics of the real world, even the kind of statistics we don't like? If something should be bothering us, it is the bias in society, rather than the bias these word embeddings merely capture. Or in other words:

What's this deal with de-biasing embeddings? Shouldn't we rather let embeddings as is and aim at de-biasing society instead?

— Angeliki Lazaridou (@aggielaz) September 30, 2016

I like this tweet because I was wondering just the same when I first heard about this work. The key concern about bias in word embeddings is that these vectors are commonly used in applications, and this might inadvertently amplify unwanted stereotypes. The example in the paper mentions web search aided by word embeddings. The scenario described is of an employer looking for an intern in computer science by searching for terms related to computer science, and the authors suggest that a LinkedIn page of a male researcher might be ranked higher in the results than that of a female researcher, since computer science terms are closer in the vector space to male names than to female names (because of the current bias). In this scenario, and in many other possible scenarios, the word embeddings are not just passively recording the gender bias, but might actively contribute to it!

Hal Daumé III wrote a blog post called Language Bias and Black Sheep about the topic, and suggested that the problem goes even deeper, since corpus co-occurrences don't always capture real-world co-occurrences, but rather statistics of things that are talked about more often:

"Which leads us to the "black sheep problem." We like to think that language is a reflection of underlying truth, and so if a word embedding (or whatever) is extracted from language, then it reflects some underlying truth about the world. The problem is that even in the simplest cases, this is super false."

Prior to reading this paper (and the discussion and blog posts that followed it), I never realized that we are more than just passive observers of data; the work we do can actually help mitigate biases or inadvertently contribute to them. I think we should all keep this in mind and try to see in our next work whether it can have any positive or negative affect on that matter -- just like we try to avoid overfitting, cherry-picking, and annoying reviewer 2.

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References:
[1] Cross-national patterns of gender differences in mathematics: A meta-analysis. Else-Quest, Nicole M.; Hyde, Janet Shibley; Linn, Marcia C. Psychological Bulletin, Vol 136(1), Jan 2010, 103-127.
[2] Sex Differences in Intrinsic Aptitude for Mathematics and Science?: A Critical Review. Spelke, Elizabeth S. American Psychologist, Vol 60(9), Dec 2005, 950-958.
[3] Women's underrepresentation in science: Sociocultural and biological considerations. Ceci, Stephen J.; Williams, Wendy M.; Barnett, Susan M. Psychological Bulletin, Vol 135(2), Mar 2009, 218-261. 
[4] Why don't they want a male-dominated job? An investigation of young women who changed their occupational aspirations. Pamela M. Frome, Corinne J. Alfeld, Jacquelynne S. Eccles, and Bonnie L. Barber. Educational Research And Evaluation Vol. 12 , Iss. 4,2006
[5] Women, Gender and Work: What Is Equality and How Do We Get There? Loutfi, Martha Fetherolf. International Labour Office, 1828 L. Street, NW, Washington, DC 20036, 2001.
[6] Quantifying and Reducing Stereotypes in Word Embeddings. Tolga Bolukbasi, Kai-Wei Chang, James Zou, Venkatesh Saligrama, Adam Kalai. 2016 ICML Workshop on #Data4Good: Machine Learning in Social Good Applications.

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^*STEM = science, technology, engineering and mathematics

Crowdsourcing (for NLP)

Developing new methods to solve scientific tasks is cool, but they usually require data. We researchers often find ourselves collecting data rather than trying to solve new problems. I've collected data for most of my papers, but never thought of it as an interesting blog post topic. Recently, I attended Chris Biemann's excellent crowdsourcing course at ESSLLI 2016 (the 28th European Summer School in Logic, Language and Information), and was inspired to write about the topic. This blog post will be much less technical and much more high-level than the course, as my posts usually are. Nevertheless, credit for many interesting insights on the topic goes to Chris Biemann.¹  

Who needs data anyway?
So let's start from the beginning: what is this data and why do we need it? Suppose that I'm working on automatic methods to recognize the semantic relation between words, e.g. I want my model to know that cat is a type of animal, and that wheel is a part of a car.

At the very basic level, if I already developed such a method, I will want to check how well it does compared to humans. Evaluation of my method requires annotated data, i.e. a set of word pairs and their corresponding true semantic relations, annotated by humans. This will be the "test set"; the human annotations are considered as "gold/true labels". My model will try to predict the semantic relation between each word-pair (without accessing the true labels). Then, I will use some evaluation metric (e.g. precision, recall, F1 or accuracy) to see how well my model predicted the human annotations. For instance, my model would have 80% accuracy if for 80% of the word-pairs it predicted the same relation as the human annotators.

Figure 1: an example of dataset entries for recognizing the semantic relation between words.

If that was the only data I needed, I would have been lucky. You don't need that many examples to test your method. Therefore, I could select some word-pairs (randomly or using some heuristics), and annotate them myself, or bribe my colleagues with cookies (as I successfully did twice). The problem starts when you need training data, i.e., when you want your model to learn to predict something based on labelled examples. That usually requires many more examples, and annotating data is a very tiring and Sisyphean work.

What should we do, then? Outsource the annotation process -- i.e., pay with real money, not cookies!


What is crowdsourcing?

The word crowdsourcing is a blend word composed of crowd (intelligence) + (out-)sourcing [1]. The idea is to take a task that can be performed by experts (e.g. translating a document from English to Spanish), and outsource it to a large crowd of non-experts (workers) that can perform it.

The requester defines the task, and the workers work on it. The requester than decides whether to accept/reject the work and pays the workers (in case of acceptance).

The benefits of using "regular" people rather than experts are:

1. You pay them much less than experts - typically a few cents per question (/task). (e.g., [2] found that in translation tasks, the crowd reached the same quality as the professionals, with less than 12% of the costs).
2. They are more easily available via crowdsourcing platforms (see below).
3. By letting multiple people work on the task rather than a single/few experts, the task could be completed in a shorter time. 

The obvious observation is that the quality of a worker is not as good as the expert; in crowdsourcing, it is not a single worker that replaces the expert, but the crowd. Rather than trusting a single worker, you assign each task to a certain number of workers, and combine their results. A common practice is to use the majority voting. For instance, let's say that I ask 5 workers what is the semantic relation between cat and dog, giving them several options. 3 of them say that cat and dog are mutually exclusive words (e.g. one cannot be both a cat and a dog), one of them says that they are opposites, and one says that cat is a type of dog. The majority has voted in the favor of mutually exclusive, and this is what I will consider as the correct answer.²

The main crowdsourcing platforms (out of many others) are Amazon Mechanical Turk and CrowdFlower. In this blog post I will not discuss the technical details of these platforms. If you are interested in a comparison between the two, refer to these slides from the NAACL 2015 crowdsourcing tutorial.

Figure 2: An example of a question in Amazon Mechanical Turk, from my project.

What can be crowdsourced?

Not every data we need to collect can be collected via crowdsourcing; some data may require expert annotation, e.g. if we need to annotate the syntactic trees of sentences in natural language, that's probably a bad idea to ask non-experts to do so.

The rules of thumb for crowdsourcability are:

• The task is easy to explain, and you as a requester indeed explain it simply. They key idea is to keep it simple. The instructions should be short, i.e. do not expect workers to read a 50 page manual. They don't get paid enough for that. The instructions should include examples.
  
• People can easily agree on the "correct" answer, e.g. "is there a cat in this image?" is good, "what is the meaning of life?" is really bad. Everything else is borderline :) One thing to consider is the possible number of correct answers. For instance, if the worker should reply with a sentence (e.g. "describe the following image"), they can do so in so many ways. Always aim one possible answer for a question.
• Each question is relatively small.
• Bonus: the task is fun. Workers will do better if they enjoy the task. If you can think of a way to gamify your task, do so!

Figure 3: Is there a cat in this image?

Some tasks are borderline and may become suitable for crowdsourcing if presented in the right way to the workers. If the task at hand seems too complicated to be crowdsourced, ask yourself: can I break it into smaller tasks that can each be crowdsourced? For example, let workers write a sentence that describes an image, and accept all answers; then let other workers validate the sentences (ask them: does this sentence really describe this image?).

Some examples for (mostly language-related) data collected with crowdsourcing
(references omitted, but are available in the course slides in the link above).

• Checking whether a sentence is grammatical or not.
• Alignment of dictionary definitions - for instance, if a word has multiple meanings, and hence has multiple definitions in each dictionary - the task was to align the definitions corresponding to the same meaning in different dictionaries.
• Translation.
• Paraphrase collection - get multiple sentences with the same meaning. These were obtained by asking multiple workers to describe the same short video.
• Duolingo started as a crowdsourcing project!
• And so did reCAPTCHA!

How to control for the quality of data?

OK, so we collected a lot of data. How do we even know if it's good? Can I trust my workers to do well on the task? Could they be as good as experts? And what if they just want my money and are cheating on the task just to get easy money?

There are many ways to control for the quality of workers:

1. The crowdsourcing platforms provide some information about the workers, such as the number of tasks they completed in the past, their approval rate (% of their tasks that were approved), location, etc. You can define your requirements from the workers based on this information.
2. Don't trust a single worker -- define that your task should be answered by a certain number of workers (typically 5) and aggregate their answers (e.g. by majority voting).
3. Create control questions - a few questions for which you know the correct answer. These questions are displayed to the worker just like any other questions. If a worker fails to answer too many control questions, the worker is either not good or trying to cheat you. Don't use this worker's answers (and don't let the worker participate in the task anymore; either by rejecting their work or by blocking them).³
4. Create a qualification test - a few questions for which you know the correct answer. You can require that any worker who wants to work on your task must take the test and pass it. As opposed to the control questions, the test questions don't have to be identical in format to the task itself, but should predict the worker's ability to perform the task well.
5. Second-pass reviewing - create another task in which workers validate previous workers' answers. 
6. Bonus the good workers - they will want to keep working for you.
   
7. Watch out for spammers! Some workers are only after your money, and they don't take your task seriously, e.g. they will click on the same answer for all questions. There is no correlation between the number of questions workers answer and their quality, however, it is worth looking at the most productive workers: some of them may be very good (and you might want to give them bonuses), while some of them may be spammers.

Ethical issues in crowdsourcing

As a requester, you need to make sure you treat your workers properly. Always remember that workers are first of all people. When you consider how much to pay or whether to reject a worker's work, think of the following:

• Many workers rely on crowdsourcing as their main income. 
• They have no job security.
• Rejection in some cases is unfair - even if the worker was bad in the task, they still spent time working (unless you are sure that they are cheating).
• New workers do lower-paid work to build up their reputation, but underpaying is not fair and not ethical.
• Are you sure you explained the task well? Maybe it is your fault if all the workers performed badly?

The good news is that, from my little experience, paying well pays off for the requester too. If you pay enough (but not too much!), you get good workers that want to do the task well. When you underpay, the good workers don't want to work on your task - they can get better paying tasks. The time to complete the task will be longer. And if you are like me, the thought of underpaying your workers will keep you awake at night. So pay well :)⁴

Important take-outs for successful crowdsourcing:

• Work in small batches. If you have 10,000 questions, don't publish all at once. Try some, learn from your mistakes, correct them and publish another batch. Mistakes are bound to happen, and they might cost you good money!
• Use worker errors to improve instructions (remember: it might be your fault).
• KEEP. IT. SIMPLE.
• Use quality control mechanisms.
• Don't underpay!
• Always expect workers to be sloppy. Repeat guidelines and questions and don't expect workers to remember them.
• If your questions are automatically generated, use random order and try to balance the number of questions with each expected answer, otherwise workers will exploit this bias (e.g. if most word-pairs are unrelated, they will mark all of them as unrelated without looking twice).
• Make workers' lives easier, and they will perform better. For instance, if you have multiple questions regarding the same word, group them together.
• If you find a way to make your task more fun, do so!

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References
[1] Howe, Jeff. The rise of crowdsourcing. Wired magazine 14.6 (2006).
[2] Omar F. Zaidan and Chris Callison-Burch Crowdsourcing translation: professional quality from non-professionals. In ACL 2011.

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1 And I would also like to mention another wonderful crowdsourcing tutorial that I attended last year at NAACL 2015, which was given by Chris Callison-Burch, Lyle Ungar, and Ellie Pavlick. Unfortunately, at that time I had no personal experience with crowdsourcing, nor believed that my university will ever have budget for that, therefore made no effort to remember the technical details; I was completely wrong. A year later I published a paper about a dataset collected with crowdsourcing, on which I even got a best paper award  :) ^↩
2 For more sophisticated aggregation methods that assign weights to workers based on their quality, see MACE. ^↩

3 Blocking a worker means that they can't work on your tasks anymore. Rejecting a worker means that they are not paid for the work they have already done. As far as I know, it is not recommended to reject a worker, because then they write bad things about you in Turker Nation and nobody wants to work for you anymore. In addition, you should always give workers the benefit of the doubt; maybe you didn't explain the task well enough.^↩

4 So how much should you pay? First of all, not less than 2 cents. Second, try to estimate how long a single question takes and aim an hourly pay of around 6 USDs. For example, in this paper I paid 5 cents per question, which I've been told is the higher bound for such tasks.^↩

Take #1

I've recently had my first scientific paper published. Some of my family members and friends were truly interested in what I do polite enough to show interest in the result of a year of hard work, and they actually tried to read the paper. Most of them lost it during reading the abstract. Some of them probably gave up reading the title.

This incident was my inspiration for this blog; I actually think that every scientific concept could be explained in a way that non-professionals could understand. I'm still relatively new in my field, and though I'm capable of reading and understanding papers (at least those related to my field of interest), I understand much better when being explained with examples and easy words.

I hope that after this long introduction, I will find the time to write in this blog. I plan to convey my thoughts about some work-related topics and everyday life in the context of computer science. And in a more personal tone, I want to give people a glimpse to what I do, and deprive them of their excuses not to talk about my work ;)