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Grunslik t1_j8cwohd wrote

What was the point of going back a century for this study? Women were underrepresented as anything but love interests, objects to be rescued, mothers, nurses, or teachers a hundred years ago. 1920 was the first year women could even vote in the U.S.!

What's more, "artificial intelligence," as we understand it today, hasn't existed in fiction that long. This is the rationale mentioned in the paper: >We have examined films over the course of a century, from 1920 to 2020. The total number of films featuring AI is sufficiently small that this large temporal range results in a corpus that is manageable but meaningful. 1920 is an appropriate start date both because of the rapid development of the cinema in the United States and Europe after the First World War, and because this decade saw the earliest high-impact portrayals of intelligent machines and their creators, in Karel Čapek’s play R.U.R. (1921) and Fritz Lang’s film Metropolis (1927).

While the sample size may indeed be small, that's no excuse for ignoring the representativeness of the sample. In fact, despite the fact that that the earliest representation of a woman as an AI-scientist that they found was in 1997, they included a corpus of the previous 77 years.

I'm all for equality, and women certainly could use more representation in AI, both in fiction and fact, but this is just bad research methodology.


4Tenacious_Dee4 t1_j8czp0h wrote

Good point. I'm also left wondering whether movies should represent reality. If 10% of programmers are women, then should movies have 10% female representation as programmers?


slickhedstrong t1_j8djl9u wrote

women are more likely to be portrayed as plumbers than be the plumber showing up to your house


Im_Talking t1_j8fmy9w wrote

>and women certainly could use more representation in AI,

Isn't that up to women?


TywinASOIAF t1_j8d1d5z wrote

This is just bad. Why use a century as sample size. What we define as AI researchers now, is in not even comparable 100 years ago. Like computers almost were non-existent. A better window would be 2000 -2020.


Ok-Training-7587 t1_j8d1ot0 wrote

Ok but if they’re starting in 1920 you’re pointing out the existence of problems that no longer exist. Do one from 2000-2020 and there might be something worth talking about in there.


emgee1219 t1_j8f0p8p wrote

Correct, this is the main point of 75% of "woke" culture criticism. There's a reason they label it history.


Kalapuya t1_j8dkxan wrote

And 97% of preschool teachers and dental assistants are women. The point?


hellomondays t1_j8emtbx wrote

have you considered the same social expectations that lead to only 8% of "ai experts" portrayed as women in a movie can also lead to what roles that women do go into?


Kalapuya t1_j8eus3h wrote

I’m just following the science, which makes a pretty clear case that, given a more level playing field, men and women naturally gravitate toward some professions more than others. It’s okay that we have differences and different preferences. Diversity is a good thing. Do you really think in a more equal society that 50% of roughnecks would be women?

Sex differences in personality are larger in gender equal countries: Replicating and extending a surprising finding

Relationship of Gender Differences in Preferences to Economic Development and Gender Equality

The Gender-Equality Paradox in Science, Technology, Engineering, and Mathematics Education


hellomondays t1_j8evoqv wrote

Never said anything different. Just that, like your articles show, there's social factors that influence career choice. countries which are and/or have become more "gender equal" over time do not necessarily have weaker gender stereotypes about boys of the sorts which are related with boys' achievements in literacy, for example


Kalapuya t1_j8evzwn wrote

You didn’t read the studies. It’s not social factors - they are inherent preferences driven by biological factors. The point was to control for social factors and the preferences still persisted.


hellomondays t1_j8ewkgs wrote

Go reread, you're conflating equality with the absence of roles and gender expectations. Self-expression can still be gendered or influence distinct personality traits, which is what the findings of the first two papers state. Biological factors play a role but they're to the exclusion of social factors. I'm not talking about the social role hypothesis' theory that changes would narrow, but rather that social factors influence how vocational gender roles are created from multiple directions. B eing more "gender equal" does not necessarily mean that there are less gender stereotypes, that boys and girls are raised in the same manne

That's not really what those papers are about, perhaps that's the confusion?


Kalapuya t1_j8exhjt wrote

I am a scientist and you are incorrectly representing the peer-reviewed primary scientific literature I just provided to you. Actually read it:

> The current study represents the first examination of sex differences in personality across countries with large samples, using a multivariate measure of effect size (Mahalanobis D). The results suggest that past studies, that averaged univariate measures of effect size (Cohen’s d), may have substantially underestimated the size of sex differences in personality profiles across countries. Sex differences were markedly higher when using a multivariate measure of effect size. Considering that personality is inherently multidimensional, in line with a growing number of researchers (e.g., Conroy-Beam et al., 2015; Del Giudice, 2009; Vianello et al., 2013), we propose that this represents a more accurate measure of the true difference.

>Previous research has consistently demonstrated that higher levels of gender equality are associated with larger sex differences in personality (Costa et al., 2001; Schmitt et al., 2008). The current study replicated this finding using a multivariate effect size. The relationship was remarkably high, with gender equality accounting for almost 50% of the variance in sex differences across countries.

Gender roles become stronger as gender equality increases, precisely because men and women a more free to choose the profession they want, and these choices are different. Men and women are different. The science says so. Accept it.


hellomondays t1_j8exv9v wrote

>Men and women are different. The science says so. Accept it.

Yes obviously. But gender equality does not equal the absence of gender roles, you're reading too far to say that it's only biological or 'natural" (whatever that means, weird word for a scientist to use). Look at the measurements they use for gender equality. You're assuming that gender roles are only enforced externally for some reason and that if there is internal motivation, they don't exist or that internal motivation is influenced by social factors.


Kalapuya t1_j8ey5jv wrote

The studies even state that as gender equality increases, gender roles become stronger precisely because men and women are more free to choose their career paths, and these choices are different. It’s counter-intuitive but plenty more research demonstrates this effect as cited throughout.


hellomondays t1_j8eyn8o wrote

Yes, men and women see themselves differently. Again, gender equality by the metrics they use is largely concerned with material equality. That doesn't preclude social factors from influencing how gender roles are conceptualized and expressed. If what you're interpreting from these studies is true, that there is a natural drive to the exclusion of all other motivators in vocational choice based on gender, there would be uniformity in all cultures across the globe when controlled for material concerns, which a weak correlation at best.

Edit to get lengthy: When dealing with a paper about the relationship between "gender equality" and "sex/gender differences," it is important not to assume that the tool used measured what people think it measures. Gender equality indexes tend to be tools which serve a particular purpose (most often political), and there are many conceptual and operational issues which have been raised by researchers (e.g. see Bericat, 2012, Hawken & Munck, 2013, Permanyer, 2015). As Boulicault points out, we should ask ourselves:

>In other words, is it a valid and reliable way to quantify the phenomenon of gender equality?

>The answer to this question depends on the construct definition, i.e. on how “gender equality” is defined. The UN defines gender equality as the “full equality of rights and opportunities between men and women.” However, between the ten words of this definition lie a plethora of details and complications. What does it mean in practice for men and women to have “full equality of rights and opportunities”? Does it matter whether men and women feel equal or is it enough that they have equal rights and opportunities? Should the equality of rights and opportunities be understood differently in different domains, for example in healthcare vs. politics? These kinds of questions have been heavily debated, leading to the identification of different dimensions and definitions of gender equality.

>These complexities are reflected in the ways gender equality is measured. One reason that so many gender equality measures exist (and that these measures are compound indices rather than uni-dimensional indicators) is precisely because gender equality is complex and can be conceptualized and defined, and therefore measured, in many different ways. As such, rather than seeing all these measures as strictly competing, it’s helpful to think of them as different tools, each suited to measuring different constructs or dimensions of gender equality. For instance, if you want to measure gender equality within social institutions, you won’t want to use the GGGI, which is intended to measure gender equality across four broad domains. Instead, an index like SIGI -- which is specifically created to measure gender equality (and gender discrimination) in social institutions -- would be the better tool for the job. In other words, just like you would use a thermometer over a meter stick to measure water temperature, you would use SIGI over the GGGI to measure gender equality in social institutions.

These indexes tend to measure achievement outcomes in particular dimensions of interest, such as "political empowerment" (think the proportion and distribution of men and women in politics). It is worthwhile to highlight the fact that Guiso et al. (2008) use the GGI, but explicitly think of it as "women's emancipation (GGI)."

There are two things to keep in mind here. First, not all of these dimensions may be relevant to specific outcomes. As Else-Quest et al. (2010) remark:

>Some aspects of gender equity may be more germane to math achievement than others; for example, equal access to formal schooling (at all levels) surely has a profound impact on girls’ math skills, but women’s greater life expectancy is probably less relevant.

Second, there is the issue of the concept of gender itself. For many, the research question is whether sociocultural factors associated with gender (gender attitudes, norms, stereotypes, ...) contribute to societal sex/gender differences in outcomes. As Noll explains:

>Understanding gender norms and stereotypes is critical to understanding why gender equality and gender neutrality are not the same concepts. Norms, attitudes, and stereotypes about gender give people information about what is typical and/or desirable in their social context and influence their preferences, beliefs, and behavior. Psychological research has repeatedly demonstrated that gender stereotypes and norms matter for how people conduct their lives and that they contribute to gender differences, and that gender stereotypes and norms are robust even in societies with high gender equality.

Being more "gender equal" does not necessarily mean that, for example, there are less gender stereotypes, that boys and girls are raised in the same manner, etc. For instance, Breda et al. (2020) argue:

>This means that countries that have eliminated the most the male-primacy ideology or “vertical gender norms” regarding women access to the labor market or even leadership positions are also countries that have developed more “horizontal essentialist norms” regarding women’s and men’s appropriate skills, behaviors, or emotions.

Therefore, countries which are and/or have become more "gender equal" over time do not necessarily have, inversely and for instance, weaker gender stereotypes about boys of the sorts which are related with boys' achievements in literacy (e.g. see Retelsdorf et al., 2015, Pansu et al., 2016, Heyder et al., 2017).


EasternAssistance185 t1_j8eb25n wrote

Why is this on the subreddit?


tealcosmo t1_j8olc2a wrote

Because someone doing a degree in TV watching has to do some science somewhere in order to pass their thesis.


Im_Talking t1_j8fdctd wrote

The dangers of 'equal outcomes', which leads to discussions of imaginary problems. Topics like this do nothing but widen any divide because one can easily find something which has disparity in the complete opposite. For example, I wonder what percentage of movies between 1920-2020 show women as violent... 8% perhaps?


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Actaeus86 t1_j8dh3tn wrote

Well anytime you start an analysis in 1920 you should expect a lack of female scientists. It would have been unheard of 100 years ago


tornpentacle t1_j8e4we6 wrote

It appears you are not familiar with much history of the past century :-p there were countless scientists who were women, even many who contributed greatly to many different fields


Actaeus86 t1_j8e57xv wrote

I am sure that there were a few, especially compared to now. But I highly doubt that anyone in the 1920s was like wow there is 1 female scientist in the entire state, I need to make sure when I have this future movie I show women as the lead scientist. Maybe starting 40s-50s it was much more common (again compared to the 1920s, but still minuscule vs now)


mavdoru t1_j8dhhe0 wrote

Does anyone know the percentage of women AI professionals in real life?


heresyforfunnprofit t1_j8dw6vo wrote

I’m in the field. 8% is probably a generous estimate. I’d guess 5% is closer, but it’s easy to fudge numbers by including tangential business fields. One of my friend’s wife describes herself as “being in AI” because she’s on the sales team for the AI group. You could probably get close to 25-40% depending on how much you want to fudge.


scotty_dont t1_j8h5bf5 wrote

The other answer here is just bad. From experience (and I have quite a lot) the 20% quoted in the article does not seem inflated. Attrition is much higher, and women are not well represented in management (particularly senior management), but that is a problem broadly in tech.

Women in ML are not unicorns; I work with them every day.


voyagertoo t1_j8fk558 wrote

Corresponding sources of our corpus


kingboy10 t1_j8u9nor wrote

As a woman AI I’m offended


TheFiredrake42 t1_j8dobwq wrote

Remember when Johnny Depp was an AI?

I actually probably would have been ok with that.


marketrent OP t1_j8cs47a wrote

Findings in title quoted from the linked summary^1 and its hyperlinked journal paper.^2

From the linked summary^1 released 13 Feb. 2023:

>A new paper in Public Understanding of Science and an associated report by Stephen Cave, Kanta Dihal, Eleanor Drage, and Kerry McInerney shows the results of an analysis of the 142 most influential AI films in history, establishing that gender inequalities in film are more extreme than in real life.

>Just 8% of all depictions of AI professionals from a century of popular film are women – and more than half of these are shown as subordinate to men.

>This gender imbalance is even bigger than in the real-world AI industry, in which 20% of AI professionals are women.

From the hyperlinked journal paper:^2

>The aim of this study is to examine the gendering of portrayals of AI researchers in influential fiction film over the past century, 1920–2020.

>[We] explain our choice of media and period; our criteria for ‘AI researcher’; how we have coded gender; our criteria for ‘influential’ in film; and the corresponding sources of our corpus.

>We have examined films over the course of a century, from 1920 to 2020. The total number of films featuring AI is sufficiently small that this large temporal range results in a corpus that is manageable but meaningful.

>1920 is an appropriate start date both because of the rapid development of the cinema in the United States and Europe after the First World War, and because this decade saw the earliest high-impact portrayals of intelligent machines and their creators, in Karel Čapek’s play R.U.R. (1921) and Fritz Lang’s film Metropolis (1927).

>Of the 1413 films in our corpus, we identified 142 as featuring AI. Of these, 86 films clearly showed or referred to an AI engineer or scientist.

>The total number of AI engineers or scientists shown was 116, as 63 films showed only one such figure, 16 films showed 2 and 7 films showed 3 figures that met our criteria.

>Of these 116 AI engineers or scientists, 88 were men, 10 were male robots, aliens, animals or AIs, and 9 were corporations led by men, giving a total of 107 male figures, or 92% of the total. Seven were human women and two were female non-humans, giving a total of nine female figures, or 8% of the total.

^1 Who makes AI? Inequality in AI films, Leverhulme Centre for the Future of Intelligence, 13 Feb. 2023,

^2 S. Cave, K. Dihal, E. Drage, and K. McInerney (2023) Who makes AI? Gender and portrayals of AI scientists in popular film, 1920–2020. Public Understanding of Science.


azazelcrowley t1_j8cwoxx wrote

I think this may have to do with tropes about AI as well as gender roles. I'd be interested to see a breakdown between "Ai goes rogue" stories and more benign portrayals and whether this influences the figures.

My suspicion is that narratives built around AI being dangerous and a bad idea will overrepresent men more than positive or neutral portrayals and somewhat close this gap though not entirely. I think its difficult to draw a conclusion about women being underrepresented in a prestige field without evaluating how that field is being portrayed.

"If it goes wrong men did it and if it goes right women did 50% of the work" isnt quite the misogynistic portrayal some might draw from these findings. I think more analysis is necessary to draw conclusions about this data given the absolute glut of "AI bad" stories.


Skaindire t1_j8d0b89 wrote

And what roles were those AI's performing? How many warbots were male or female? How many ships AI's were male or female? What about space stations, what about power armors?

The 'gender' of the AI changed along with it's role, not with it's artificial existence. Just like another commenter said, it's pointless data.


tornpentacle t1_j8e4n09 wrote

You didn't read the title, let alone the actual post... please don't comment until you understand what it is you're commenting on. It saves the mods a lot of time


codesnik t1_j8di44h wrote

because women know better?


[deleted] t1_j8cu2oy wrote



What-Fries-Beneath t1_j8czuvj wrote

In the least gender biased countries women tend to choose more people oriented work, and men gravitate towards math and machines.

Check my post history if you care to. I am a huge advocate of degendering everything. Biologically personality and sex are correlated to a significant degree. Less than most people seem to think, but again it's significant.

Women in general around the world don't want to spend 60 hours a week coding. No bias needed. I know some incredibly talented data scientists who happen to be women. It would be great if more people in general took an interest to the field.

But please let's stop assuming that industries are discriminating based on little more than enrolment