It’s Not You, It’s The Algorithm

Editor’s Note:

Heichi Magazine is glad to announce a new collaboration with the Hyundai Motorstudio Beijing, jointly presenting a collection of essays edited by curator Jenny Chen Jiaying. Under the rubric of her curated exhibition, AI: Love and Artificial Intelligence, currently on view in Hyundai Motorstudio Beijing, this special column presents three long-form essays by the participating artists.

Translator’s Note:

The game Monster Match sits at the entrance of the exhibition AI: Love and Artificial Intelligence and will be the last work addressed in this series, bridging from our magazine’s lineup in 2020 to forthcoming publications in 2021. Previously, we explored molecular symbiosis inspired by “quantum entanglement” in Johanna Bruckner’s essay, then re-examined a critique of dichotomy through Frank WANG Yefeng’s writing, both topics closely related to a condition of polarized discourse and proliferating identity politics. Similarly, this article by Benjamin Berman, one of the creators of Monster Match, also offers a concrete solution to a contemporary phenomenon. Through detailed data and research, Berman explains the logic of all the dating apps at our fingertips and shows how a technology that promises intimacy has actually sown division and nurtured inequality.

Can dating apps help you find love? Yes! An algorithm matching people is now the dominant way singles meet each other in the United States. But there’s one thing most dating app makers don’t want you to know. The particular algorithm they use can do the exact opposite of finding your true love. It can permanently separate you from your perfect someone and everyone likes them, due entirely to factors outside of your control.

State of Online Dating

Are dating apps a good part of the internet? If you ask the people who make them, you’ll hear a resounding “yes.” Match Group, the company that owns both Tinder and OkCupid, found in their 2018 survey that “singles met first dates on the internet more than through any other venue.” Those companies would like you to believe that everyone is using dating apps, and they have the numbers to prove it. I personally admire the creator of OkCupid, Christian Rudder, for his intense transparency and rigor in showing how dating apps work and how they’ve helped people meet each other.

But according to independent research, it’s hard to say how good dating apps really are. Despite so much online dating, Americans are feeling lonelier than ever. Some experts believe this is because technology divides people. In their view, smartphones interfere with intimacy, and social media halts courtship with unrealistic expectations of beauty and wealth. But other experts believe the difference in college education rates between men and women is really to blame for dating problems.

When looking at dating app data, Mr. Rudder once lamented that “there’s a bias against [black users]” on his website. Yet there’s also evidence that online dating is associated with higher rates of interracial marriage. There’s no consensus whether these apps made dating better or worse. However, one thing is certain: some people get much more from dating apps than others. As both a millennial and game developer, I can assure you that dating apps are a game, and there are winners and losers. But don’t take my word for it: read the online communities dedicated to online dating.

There, the people who get matches share strategies like what pickup lines to use, what times to start swiping, and even what species of pet you should pose with. The people who don’t get matches talk about past relationships, debate politics, or, more often than not, blame women. The discourse around dating focuses on tips at the expense of what is really special about online dating: the algorithm.

Algorithms have changed over the course of dating app history. When the internet consisted of “bulletin board systems,” or digital notice boards of sorts, the algorithm for matching was essentially random. Whoever showed up on the website is who we got matched with. Nowadays, there are still apps people use that work this way, like WeChat’s Shake, which matches two people as long as they’re shaking their phones at the same time.

Later, dating apps matched people with some “sober arithmetic,” as the creator of OkCupid put it. The idea was that people who matched in terms of answers to personality questions, appearance, location, or other aspects of their daily lives would also be a good match romantically. This algorithm is actually a form of counting, where every matching aspect between two people gives the match a point, and every disagreement takes a point away. The best matches are the ones with the highest scores—a good match in the sense of having a lot in common, essentially.

Dating App Algorithms

Nowadays, apps use collaborative filtering, a sophisticated algorithm famously used to make movie recommendations based on previous movies you’ve watched. Collaborative filtering tries to find groups of people with shared preferences, whatever they are, and then makes recommendations to the individual based on the preferences of the group. Instead of specifying what makes a good match  — age, hometown, taste in music, feelings on smoking, to name a few from OkCupid — collaborative filtering infers preferences from the data. It’s an unopinionated approach to something really subjective, and it works.

Collaborative filtering is so effective it’s used by almost all apps; this is the important thing to understand about online dating today. It powers your Facebook and Twitter feeds, your Google searches, and your Netflix and Amazon recommendations. It’s not that complicated. You’ve seen this a million times: “You might also like…” How does Amazon know what you might also like, and why does it use the word “also”? Because you’re not the only person on Earth buying tortilla chips. Amazon looks up what else tortilla chip buyers have bought: salsa. So it knows “you might also like” salsa without really understanding anything about the innate relationship between tortilla chips and salsa. The same exact thing is going on with dating, except the thing that’s on offer is people.

Collaborative filtering in dating means that the earliest and most numerous users of the app have outsize influence on the profiles later users see. Some early user says she likes (by swiping right on) some other active dating app user. Then that same early user says she doesn’t like (by swiping left on) a Jewish user’s profile, for whatever reason. As soon as some new person also swipes right on that active dating app user, the algorithm assumes the new person “also” dislikes the Jewish user’s profile. Similar users have similar tastes, according to collaborative filtering. So the new person never sees the Jewish profile.

A recent look at this phenomenon is going to change the way you think about online dating. Users of dating apps make “yes” or “no” decisions on other users, one by one, and that data is counted to figure out whose preferences that user most resembles. Then, data from the older user is borrowed to make recommendations to the newer user: that’s how the algorithm determines which profile to show next. While many legacy dating applications let you browse, most people prefer being given a single profile to look at and making a yes-or-no decision. Apps have arrived at collaborative filtering both because it’s effective in terms of matching and also because its user interface design is preferred over browsing.

There are side effects to collaborative filtering: users with uncommon preferences are poorly served by the algorithm. So app makers have made niche apps, each catering to a group of users, to make collaborative filtering more effective. There are dozens of special interest dating apps, like Amo Latina for Latino users, Jdate for Jewish users, and Grindr for LGBT users. Most of these apps are owned by the same company (Match Group, a subsidiary of IAC). They’re not really different apps: they function by the same logic and even have the same interface.

But for a variety of reasons, they may attract users who have bad luck with collaborative filtering in apps with larger user bases like Tinder. A large user base puts money in the company’s bank account. But a large user base also makes uncommon preferences seem more uncommon, while common preferences become ever more dominant.

There’s a tension between the effectiveness of collaborative filtering and a tech company’s objective to have as many users as possible. The result is many apps, owned by the same people, that divide users into religious, ethnic, sexual orientation, and geographic groups. That is the state of online dating today.

New Results

A brand new simulation has quantified our gut feelings about dating apps: that a feedback loop in collaborative filtering gives majority users better matches at the expense of minority users. There’s something innate about collaborative filtering that disadvantages people who are underrepresented in the data. Without malicious intention, collaborative filtering reproduced the underlying causes of inequality in offline life.

This is all besides the point because there is no perfect dating algorithm, only compromises. There is an imbalance between what people want and what people give in dating. All preferences cannot be satisfied for everyone.

A simple fix: dating apps can give you a “reset button” to clear your history of likes and reset how the algorithm sees you. Or today, you can delete and recreate your dating app account. Both fixes take control of the algorithm in one important way: by not helping it. But we’re not in the business of proposing better alternatives to collaborative filtering. And people who suggest we try are missing the point.

Society ought to be able to inspect how algorithms work, in the sense of looking at the code. Facebook CEO Mark Zuckerberg withstood hours of congressional grandstanding to answer questions about Facebook’s newsfeed algorithms. He never explained how the algorithm worked at a fundamental level, which was the question many congresspeople asked because they couldn’t answer it themselves.

The answer is “collaborative filtering,” but we only know that because of a zeitgeist in the software industry, not because anyone outside of Facebook looked at the code. So let’s just look at the code!

This game I developed shows how the typical dating app algorithm works. You won’t actually have to go on any dates. The game is a simulation. You’ll still build a profile though: a monster profile. It’s called MonsterMatch, and it uses collaborative filtering to decide which monsters you’ll get to swipe left and right on — and which monsters you’ll never get a chance to see.

We’re also sharing all the code because laypeople’s explanations are often co-opted to tell you the story the algorithm owner wants you to hear. If you want to see exactly how collaborative filtering works in a dating app, read the algorithm here. Tech companies that deploy collaborative filtering, least of all dating apps, never do this. But they ought to. Sharing the code is the only defense against people telling you one thing and actually doing another with software.

In our opinion, if an algorithm’s code penalizes some people somehow, it doesn’t have to be illegal: people just ought to know how it works. This should assuage big tech companies who resist regulation of core intellectual property like recommendation algorithms. An informed consumer base will improve digital inclusion and allow minority groups to be treated more equitably online. But sharing code is sometimes not enough. Some code, like collaborative filtering, lacks “interpretability.” It’s hard to know why the code does what it does, even when we know what it does. Interpretability crops up whenever the algorithm does counting on lots of data, like counting swipes. So if you look for a piece of code that says, “Score Jewish users worse,” you’ll never find it. That’s not how it works.

In the case where an algorithm can’t be interpreted, we ought to demand data on its consequences. That means if the consequence of an algorithm is discrimination, even if there is no piece of code that says “discriminate,” the algorithm is discriminatory. For example, if there’s something in common about first-time users of dating apps who immediately quit using the app shortly afterwards, we ought to know what that thing is.

We regulate and inspect medicine, energy, finance, agriculture, transportation safety, and education based on outcomes. The key feature of those regulations is sharing information for consumer choice and protection. Those industries still innovate and make money. We ought to apply the same standards to algorithms.

Unlike those other industries, dating apps already collect comprehensive data about users and their behavior. There’s little cost burden to answering outcomes questions. While it’s difficult to know exactly which questions to ask, it would take an afternoon and a database connection to answer them.

Today, we already know one thing: dating apps are effectively segregated. Jdate and JSwipe for Jewish users; Amo Latina for Latino users; Tinder for coastal users. Dozens exist, each app their own community. Users report more satisfaction by using these segregated apps. Dating app creators will write letters to the editor saying as much. But surely there’s a cost to segregation.

Being funneled into a smaller, segregated experience often gives you fewer opportunities. There isn’t any evidence for this in segregated dating apps specifically. The data are not open to independent research. But history has shown segregation to disfavor the segregated. Anti-miscegenation laws reinforced inequalities on future generations. Because it affects who’s having kids with whom, a segregated dating app could be a high-tech version of that shameful past.

In light of this uncertainty, we ought to equip the public with enough information to make an informed choice about what dating apps to use. So share the algorithm’s code, and let the user decide if she’ll get a fair shake in the dating app game.

Our prediction: If people really knew how much these apps screwed them, they’d stop using those apps. For a giant internet company, that’s the scariest thing of all.

Benjamin Berman is an artist and developer in San Francisco, California. After leaving MIT, where he researched how gaming influences society, he now directs a community-authored e-sports game called Spellsource. His professional and artistic work touches on near-future sci-fi (Virtual High, App the Movie), a data-driven society (Workpop, Hear All Ye People), and computer history (Did My Brother Invent E-Mail?) as shown at the Tribeca Film Festival, on the Disney Channel, and in the New York Times.

Jenney Chen Jiaying is a writer and curator. She is now a PhD candidate in Western philosophy at Eastern China Normal University. She holds a Bachelor of Arts from the Department of Art History of China Academy of Art and received her Master of Arts degree from Lancaster University in the UK. She has contributed to media such as Artforum (CN), Artshard, NOWNESS. Recent Projects include: AI: Love and Artificial Intelligence, Hyundai Motorstudio, Beijing, China (2020); Copernicus, E.M.Bannister Gallery of Rhode Island College, Providence, U.S.A (2019); Li Hanwei: Liquid Health, Goethe Space, Shanghai (2019); First edition of the Shanghai Curators Lab, Shanghai Academy of Fine Arts, Shanghai (2018). Jenny’s other academic activities include the First Annual Conference of Network Society “Forces of Reticulation” roundtable and Huayu Forum of Art, etc. Her article “Post-Internet Art Inside and Outside the Chinternet” was included in the collection of essays Forces of Reticulation published by China Academy of Art Press. Shanghai Contemporary Art Archival Project 1998-2010, which she co-wrote and edited, was published by MOUSSE in 2017.

Benjamin Berman & Miguel Perez, Monster Match, 2020, Video Games

Figure 1: How men rated women on OkCupid, 2014

OkCupid publishes data about its users on its blog, OkTrends, where it measures how users’ ratings correlate to their race. Black women are consistently rated lower by all men, on average.

Figure 2: Trends in preferences on OkCupid, 2009-2014

Racial preferences in dating seemed to persist between 2009 and 2014. However, since then, users have been reporting greater comfort with dating people different than them over time. Something about the app’s design might play a role in the persistence of these race-correlated ratings despite real changes in the user's preferences.

Figure 3: Audience size of dating apps, 2017

In December 2017,  industry data service Statista made these estimates of dating app audience sizes based on how many users interacted with the specified app at least once that year. With 8.2 million users, Tinder was the largest dating app.


Published: 2021.01.07