• How Ex Machina Abuses Women of Color and Nobody Cares Cause It's Smart

    How Ex Machina Abuses Women of Color and Nobody Cares Cause It's Smart

    Alex Garland, dir. & writer, Ex Machina (A24/Universal Films, 2015)a review of Alex Garland, dir. & writer, Ex Machina (A24/Universal Films, 2015)
    by Sharon Chang
    ~

    In April of this year British science fiction thriller Ex Machina opened in the US to almost unanimous rave reviews. The film was written and directed by Alex Garland, author of bestselling 1996 novel The Beach (also made into a movie) and screenwriter of 28 Days Later (2002) and Never Let Me Go (2010). Ex Machina is Garland’s directorial debut. It’s about a young white coder named Caleb who gets the opportunity to visit the secluded mountain home of his employer Nathan, pioneering programmer of the world’s most powerful search engine (Nathan’s appearance is ambiguous but he reads non-white and the actor who plays him is Guatemalan). Caleb believes the trip innocuous but quickly learns that Nathan’s home is actually a secret research facility in which the brilliant but egocentric and obnoxious genius has been developing sophisticated artificial intelligence. Caleb is immediately introduced to Nathan’s most upgraded construct–a gorgeous white fembot named Ava. And the mind games ensue.

    As the week unfolds the only things we know for sure are (a) imprisoned Ava wants to be free, and, (b) Caleb becomes completely enamored and wants to “rescue” her. Other than that, nothing is clear. What are Ava’s true intentions? Does she like Caleb back or is she just using him to get out? Is Nathan really as much an asshole as he seems or is he putting on a show to manipulate everyone? Who should we feel sorry for? Who should we empathize with? Who should we hate? Who’s the hero? Reviewers and viewers alike are melting in intellectual ecstasy over this brain-twisty movie. The Guardian calls it “accomplished, cerebral film-making”; Wired calls it “one of the year’s most intelligent and thought-provoking films”; Indiewire calls it “gripping, brilliant and sensational”. Alex Garland apparently is the smartest, coolest new director on the block. “Garland understands what he’s talking about,” says RogerEbert.com, and goes “to the trouble to explain more abstract concepts in plain language.”

    Right.

    I like sci-fi and am a fan of Garland’s previous work so I was excited to see his new flick. But let me tell you, my experience was FAR from “brilliant” and “heady” like the multitudes of moonstruck reviewers claimed it would be. Actually, I was livid. And weeks later–I’m STILL pissed. Here’s why…

    *** Spoiler Alert ***

    You wouldn’t know it from the plethora of glowing reviews out there cause she’s hardly mentioned (telling in and of itself) but there’s another prominent fembot in the film. Maybe fifteen minutes into the story we’re introduced to Kyoko, an Asian servant sex slave played by mixed-race Japanese/British actress Sonoya Mizuno. Though bound by abusive servitude, Kyoko isn’t physically imprisoned in a room like Ava because she’s compliant, obedient, willing.

    I recognized the trope of servile Asian woman right away and, how quickly Asian/whites are treated as non-white when they look ethnic in any way.

    Kyoko first appears on screen demure and silent, bringing a surprised Caleb breakfast in his room. Of course I recognized the trope of servile Asian woman right away and, as I wrote in February, how quickly Asian/whites are treated as non-white when they look ethnic in any way. I was instantly uncomfortable. Maybe there’s a point, I thought to myself. But soon after we see Kyoko serving sushi to the men. She accidentally spills food on Caleb. Nathan loses his temper, yells at her, and then explains to Caleb she can’t understand which makes her incompetence even more infuriating. This is how we learn Kyoko is mute and can’t speak. Yep. Nathan didn’t give her a voice. He further programmed her, purportedly, not to understand English.

    kyoko
    Sex slave “Kyoko” played by Japanese/British actress Sonoya Mizuno (image source: i09.com)

    I started to get upset. If there was a point, Garland had better get to it fast.

    Unfortunately the treatment of Kyoko’s character just keeps spiraling. We continue to learn more and more about her horrible existence in a way that feels gross only for shock value rather than for any sort of deconstruction, empowerment, or liberation of Asian women. She is always at Nathan’s side, ready and available, for anything he wants. Eventually Nathan shows Caleb something else special about her. He’s coded Kyoko to love dancing (“I told you you’re wasting your time talking to her. However you would not be wasting your time–if you were dancing with her”). When Nathan flips a wall switch that washes the room in red lights and music then joins a scantily-clad gyrating Kyoko on the dance floor, I was overcome by disgust:

    [youtube https://www.youtube.com/watch?v=hGY44DIQb-A?feature=player_embedded]

    I recently also wrote about Western exploitation of women’s bodies in Asia (incidentally also in February), in particular noting it was US imperialistic conquest that jump-started Thailand’s sex industry. By the 1990s several million tourists from Europe and the U.S. were visiting Thailand annually, many specifically for sex and entertainment. Writer Deena Guzder points out in “The Economics of Commercial Sexual Exploitation” for the Pulitzer Center on Crisis Reporting that Thailand’s sex tourism industry is driven by acute poverty. Women and girls from poor rural families make up the majority of sex workers. “Once lost in Thailand’s seedy underbelly, these women are further robbed of their individual agency, economic independence, and bargaining power.” Guzder gloomily predicts, “If history repeats itself, the situation for poor Southeast Asian women will only further deteriorate with the global economic downturn.”

    caption
    Red Light District, Phuket (image source: phuket.com)

    You know who wouldn’t be a stranger to any of this? Alex Garland. His first novel, The Beach, is set in Thailand and his second novel, The Tesseract, is set in the Philippines, both developing nations where Asian women continue to be used and abused for Western gain. In a 1999 interview with journalist Ron Gluckman, Garland said he made his first trip to Asia as a teenager in high school and had been back at least once or twice almost every year since. He also lived in the Philippines for 9 months. In a perhaps telling choice of words, Gluckman wrote that Garland had “been bitten by the Asian bug, early and deep.” At the time many Asian critics were criticizing The Beach as a shallow look at the region by an uniformed outsider but Garland protested in his interview:

    A lot of the criticism of The Beach is that it presents Thais as two dimensional, as part of the scenery. That’s because these people I’m writing about–backpackers–really only see them as part of the scenery. They don’t see them or the Thai culture. To them, it’s all part of a huge theme park, the scenery for their trip. That’s the point.

    I disagree severely with Garland. In insisting on his right to portray people of color one way while dismissing how those people see themselves, he not only centers his privileged perspective (i.e. white, male) but shows determined disinterest in representing oppressed people transformatively. Leads me to wonder how much he really knows or cares about inequity and uplifting marginalized voices. Indeed in Ex Machina the only point that Garland ever seems to make is that racist/sexist tropes exists, not that we’re going to do anything about them. And that kind of non-critical non-resistant attitude does more to reify and reinforce than anything else. Take for instance in a recent interview with Cinematic Essential (one of few where the interviewer asked about race), Garland had this to say about stereotypes in his new film:

    Sometimes you do things unconsciously, unwittingly, or stupidly, I guess, and the only embedded point that I knew I was making in regards to race centered around the tropes of Kyoko [Sonoya Mizuno], a mute, very complicit Asian robot, or Asian-appearing robot, because of course, she, as a robot, isn’t Asian. But, when Nathan treats the robot in the discriminatory way that he treats it, I think it should be ambivalent as to whether he actually behaves this way, or if it’s a very good opportunity to make him seem unpleasant to Caleb for his own advantage.

    First, approaching race “unconsciously” or “unwittingly” is never a good idea and moreover a classic symptom of white willful ignorance. Second, Kyoko isn’t Asian because she’s a robot? Race isn’t biological or written into human DNA. It’s socio-politically constructed and assigned usually by those in power. Kyoko is Asian because she ha been made that way not only by her oppressor, Nathan, but by Garland himself, the omniscient creator of all. Third, Kyoko represents the only embedded race point in the movie? False. There are two other women of color who play enslaved fembots in Ex Machina and their characters are abused just as badly. “Jasmine” is one of Nathan’s early fembots. She’s Black. We see her body twice. Once being instructed how to write and once being dragged lifeless across the floor. You will never recognize real-life Black model and actress Symara A. Templeman in the role however. Why? Because her always naked body is inexplicably headless when it appears. That’s right. One of the sole Black bodies/persons in the entire film does not have (per Garland’s writing and direction) a face, head, or brain.

    caption
    Symara A. Templeman, who played “Jasmine” in Ex Machina (image source: Templeman on Google+)

    “Jade” played by Asian model and actress Gana Bayarsaikhan, is presumably also a less successful fembot predating Kyoko but perhaps succeeding Jasmine. She too is always shown naked but, unlike Jasmine, she has a head, and, unlike Kyoko, she speaks. We see her being questioned repeatedly by Nathan while trapped behind glass. Jade is resistant and angry. She doesn’t understand why Nathan won’t let her out and escalates to the point we are lead to believe she is decommissioned for her defiance.

    It’s significant that Kyoko, a mixed-race Asian/white woman, later becomes the “upgraded” Asian model. It’s also significant that at the movie’s end white Ava finds Jade’s decommissioned body in a closet in Nathan’s room and skins it to cover her own body. (Remember when Katy Perry joked in 2012 she was obsessed with Japanese people and wanted to skin one?). Ava has the option of white bodies but after examining them meticulously she deliberately chooses Jade. Despite having met Jasmine previously, her Black body is conspicuously missing from the closets full of bodies Nathan has stored for his pleasure and use. And though Kyoko does help Ava kill Nathan in the end, she herself is “killed” in the process (i.e. never free) and Ava doesn’t care at all. What does all this show? A very blatant standard of beauty/desire that is not only male-designed but clearly a light, white, and violently assimilative one.

    caption
    Gana Bayarsaikhan, who played “Jade” in Ex Machina (image source: profile-models.com)

    I can’t even being to tell you how offended and disturbed I was by the treatment of women of color in this movie. I slept restlessly the night after I saw Ex Machina, woke up muddled at 2:45 AM and–still clinging to the hope that there must have been a reason for treating women of color this way (Garland’s brilliant right?)–furiously went to work reading interviews and critiques. Aside from a few brief mentions of race/gender, I found barely anything addressing the film’s obvious deployment of racialized gender stereotypes for its own benefit. For me this movie will be joining the long list of many so-called film classics I will never be able to admire. Movies where supposed artistry and brilliance are acceptable excuses for “unconscious” “unwitting” racism and sexism. Ex Machina may be smart in some ways, but it damn sure isn’t in others.

    Correction (8/1/2015): An earlier version of this post incorrectly stated that actress Symara A. Templeman was the only Black person in the film. The post has been updated to indicate that the movie also featured at least one other Black actress, Deborah Rosan, in an uncredited role as Office Manager.

    _____

    Sharon H. Chang is an author, scholar, sociologist and activist. She writes primarily on racism, social justice and the Asian American diaspora with a feminist lens. Her pieces have appeared in Hyphen Magazine, ParentMap Magazine, The Seattle Globalist, on AAPI Voices and Racism Review. Her debut book, Raising Mixed Race: Multiracial Asian Children in a Post-Racial World, is forthcoming through Paradigm Publishers as part of Joe R. Feagin’s series “New Critical Viewpoints on Society.” She also sits on the board for Families of Color Seattle and is on the planning committee for the biennial Critical Mixed Race Studies Conference. She blogs regularly at Multiracial Asian Families, where an earlier version of this post first appeared.

    The editors thank Dorothy Kim for referring us to this essay.

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  • Who Big Data Thinks We Are (When It Thinks We're Not Looking)

    Who Big Data Thinks We Are (When It Thinks We're Not Looking)

    Dataclysm: Who We Are (When We Think No One's Looking) (Crown, 2014)a review of Christian Rudder, Dataclysm: Who We Are (When We Think No One’s Looking) (Crown, 2014)
    by Cathy O’Neil
    ~
    Here’s what I’ve spent the last couple of days doing: alternatively reading Christian Rudder’s new book Dataclysm and proofreading a report by AAPOR which discusses the benefits, dangers, and ethics of using big data, which is mostly “found” data originally meant for some other purpose, as a replacement for public surveys, with their carefully constructed data collection processes and informed consent. The AAPOR folk have asked me to provide tangible examples of the dangers of using big data to infer things about public opinion, and I am tempted to simply ask them all to read Dataclysm as exhibit A.

    Rudder is a co-founder of OKCupid, an online dating site. His book mainly pertains to how people search for love and sex online, and how they represent themselves in their profiles.

    Here’s something that I will mention for context into his data explorations: Rudder likes to crudely provoke, as he displayed when he wrote this recent post explaining how OKCupid experiments on users. He enjoys playing the part of the somewhat creepy detective, peering into what OKCupid users thought was a somewhat private place to prepare themselves for the dating world. It’s the online equivalent of a video camera in a changing booth at a department store, which he defended not-so-subtly on a recent NPR show called On The Media, and which was written up here.

    I won’t dwell on that aspect of the story because I think it’s a good and timely conversation, and I’m glad the public is finally waking up to what I’ve known for years is going on. I’m actually happy Rudder is so nonchalant about it because there’s no pretense.

    Even so, I’m less happy with his actual data work. Let me tell you why I say that with a few examples.

    Who Are OKCupid Users?

    I spent a lot of time with my students this summer saying that a standalone number wouldn’t be interesting, that you have to compare that number to some baseline that people can understand. So if I told you how many black kids have been stopped and frisked this year in NYC, I’d also need to tell you how many black kids live in NYC for you to get an idea of the scope of the issue. It’s a basic fact about data analysis and reporting.

    When you’re dealing with populations on dating sites and you want to conclude things about the larger culture, the relevant “baseline comparison” is how well the members of the dating site represent the population as a whole. Rudder doesn’t do this. Instead he just says there are lots of OKCupid users for the first few chapters, and then later on after he’s made a few spectacularly broad statements, on page 104 he compares the users of OKCupid to the wider internet users, but not to the general population.

    It’s an inappropriate baseline, made too late. Because I’m not sure about you but I don’t have a keen sense of the population of internet users. I’m pretty sure very young kids and old people are not well represented, but that’s about it. My students would have known to compare a population to the census. It needs to happen.

    How Do You Collect Your Data?

    Let me back up to the very beginning of the book, where Rudder startles us by showing us that the men that women rate “most attractive” are about their age whereas the women that men rate “most attractive” are consistently 20 years old, no matter how old the men are.

    Actually, I am projecting. Rudder never actually specifically tells us what the rating is, how it’s exactly worded, and how the profiles are presented to the different groups. And that’s a problem, which he ignores completely until much later in the book when he mentions that how survey questions are worded can have a profound effect on how people respond, but his target is someone else’s survey, not his OKCupid environment.

    Words matter, and they matter differently for men and women. So for example, if there were a button for “eye candy,” we might expect women to choose more young men. If my guess is correct, and the term in use is “most attractive”, then for men it might well trigger a sexual concept whereas for women it might trigger a different social construct; indeed I would assume it does.

    Since this isn’t a porn site, it’s a dating site, we are not filtering for purely visual appeal; we are looking for relationships. We are thinking beyond what turns us on physically and asking ourselves, who would we want to spend time with? Who would our family like us to be with? Who would make us be attractive to ourselves? Those are different questions and provoke different answers. And they are culturally interesting questions, which Rudder never explores. A lost opportunity.

    Next, how does the recommendation engine work? I can well imagine that, once you’ve rated Profile A high, there is an algorithm that finds Profile B such that “people who liked Profile A also liked Profile B”. If so, then there’s yet another reason to worry that such results as Rudder described are produced in part as a result of the feedback loop engendered by the recommendation engine. But he doesn’t explain how his data is collected, how it is prompted, or the exact words that are used.

    Here’s a clue that Rudder is confused by his own facile interpretations: men and women both state that they are looking for relationships with people around their own age or slightly younger, and that they end up messaging people slightly younger than they are but not many many years younger. So forty year old men do not message twenty year old women.

    Is this sad sexual frustration? Is this, in Rudder’s words, the difference between what they claim they want and what they really want behind closed doors? Not at all. This is more likely the difference between how we live our fantasies and how we actually realistically see our future.

    Need to Control for Population

    Here’s another frustrating bit from the book: Rudder talks about how hard it is for older people to get a date but he doesn’t correct for population. And since he never tells us how many OKCupid users are older, nor does he compare his users to the census, I cannot infer this.

    Here’s a graph from Rudder’s book showing the age of men who respond to women’s profiles of various ages:

    dataclysm chart 1

    We’re meant to be impressed with Rudder’s line, “for every 100 men interested in that twenty year old, there are only 9 looking for someone thirty years older.” But here’s the thing, maybe there are 20 times as many 20-year-olds as there are 50-year-olds on the site? In which case, yay for the 50-year-old chicks? After all, those histograms look pretty healthy in shape, and they might be differently sized because the population size itself is drastically different for different ages.

    Confounding

    One of the worst examples of statistical mistakes is his experiment in turning off pictures. Rudder ignores the concept of confounders altogether, which he again miraculously is aware of in the next chapter on race.

    To be more precise, Rudder talks about the experiment when OKCupid turned off pictures. Most people went away when this happened but certain people did not:

    dataclysm chart 2

    Some of the people who stayed on went on a “blind date.” Those people, which Rudder called the “intrepid few,” had a good time with people no matter how unattractive they were deemed to be based on OKCupid’s system of attractiveness. His conclusion: people are preselecting for attractiveness, which is actually unimportant to them.

    But here’s the thing, that’s only true for people who were willing to go on blind dates. What he’s done is select for people who are not superficial about looks, and then collect data that suggests they are not superficial about looks. That doesn’t mean that OKCupid users as a whole are not superficial about looks. The ones that are just got the hell out when the pictures went dark.

    Race

    This brings me to the most interesting part of the book, where Rudder explores race. Again, it ends up being too blunt by far.

    Here’s the thing. Race is a big deal in this country, and racism is a heavy criticism to be firing at people, so you need to be careful, and that’s a good thing, because it’s important. The way Rudder throws it around is careless, and he risks rendering the term meaningless by not having a careful discussion. The frustrating part is that I think he actually has the data to have a very good discussion, but he just doesn’t make the case the way it’s written.

    Rudder pulls together stats on how men of all races rate women of all races on an attractiveness scale of 1-5. It shows that non-black men find their own race attractive and non-black men find black women, in general, less attractive. Interesting, especially when you immediately follow that up with similar stats from other U.S. dating sites and – most importantly – with the fact that outside the U.S., we do not see this pattern. Unfortunately that crucial fact is buried at the end of the chapter, and instead we get this embarrassing quote right after the opening stats:

    And an unintentionally hilarious 84 percent of users answered this match question:

    Would you consider dating someone who has vocalized a strong negative bias toward a certain race of people?

    in the absolute negative (choosing “No” over “Yes” and “It depends”). In light of the previous data, that means 84 percent of people on OKCupid would not consider dating someone on OKCupid.

    Here Rudder just completely loses me. Am I “vocalizing” a strong negative bias towards black women if I am a white man who finds white women and Asian women hot?

    Especially if you consider that, as consumers of social platforms and sites like OKCupid, we are trained to rank all the products we come across to ultimately get better offerings, it is a step too far for the detective on the other side of the camera to turn around and point fingers at us for doing what we’re told. Indeed, this sentence plunges Rudder’s narrative deeply into the creepy and provocative territory, and he never fully returns, nor does he seem to want to. Rudder seems to confuse provocation for thoughtfulness.

    This is, again, a shame. A careful conversation about the issues of what we are attracted to, what we can imagine doing, and how we might imagine that will look to our wider audience, and how our culture informs those imaginings, are all in play here, and could have been drawn out in a non-accusatory and much more useful way.


    _____

    Cathy O’Neil is a data scientist and mathematician with experience in academia and the online ad and finance industries. She is one of the most prominent and outspoken women working in data science today, and was one of the guiding voices behind Occupy Finance, a book produced by the Occupy Wall Street Alt Banking group. She is the author of “On Being a Data Skeptic” (Amazon Kindle, 2013), and co-author with Rachel Schutt of Doing Data Science: Straight Talk from the Frontline (O’Reilly, 2013). Her Weapons of Math Destruction is forthcoming from Random House. She appears on the weekly Slate Money podcast hosted by Felix Salmon. She maintains the widely-read mathbabe blog, on which this review first appeared.

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