Dating Apps: The Coding of 'Liquid Love'

Dating Apps: The end of civilization, the glamorization of hook-up culture or the greatest social scam? If you have read anything about dating apps, you will probably agree with all of the above– yet the realization of the masked truth may shock you.

Dating apps have been on the rise in the past decade, and more recently have become the ‘norm’ for millennials to form romantic relationships. The increasing popularity of mobile dating apps has transformed ways in which these intimate relationships are formed, formally known as networked intimacy. A recent study, which was published in Journal of Gender Studies, points out that in the contemporary age, mediated intimacy requires “a medium through which intimate relations can be established between the subject and the other”. In this article, Hobbs coins the term “networked intimacy” as it not only relates to the mobility and mediation of the mobile phones but also the extensive connectivity supported by dating apps.

Times have changed, and since the explosion of dating platforms in 2012 the world has never been the same again. Hook-up culture has taken over the youth and how we view relationships; the labels, different stages of ‘exclusivity’ and whether things are going too fast or not at all, are just some of the confusions we are faced with due to commitment issues and new social standards brought by the dating app giants.

In past generations, when the platforms were yet to be invented, if a man was interested in a woman, he would straightforwardly approach his interest at a bar or a café and start a conversation. You would imagine a similar concept exists in the dating world online, however the reality is, despite common speculations, that the potential pool of matches you have available to you is an illusion. As a matter of fact, due to the algorithms embedded in each dating app, your matches are determined through each swipe to the right or left – building a code which presents you with potential partners that the algorithm ‘thinks’ you will like.

Alike Google image algorithm, the apps use image placement and collaborative filtering on their platforms, taking into the account the number of likes the profile receives and how recent a user was active on the platform, as well as location. Therefore, through the use of keywords attached to each user’s image the algorithm can determine your ‘desired’ match and present you with potential partners who consequently all look the same. This algorithm is used to filter out any profiles that are considered as ‘not compatible’ to each individual. It’s a similar way in which Netflix uses their algorithm to recommend what to watch; which are partly based on your personal preferences and partly based on what is popular with other subscribers. Over time, those algorithms reduce human choice and marginalise certain types of profiles, and even though collaborative filtering may generate recommendations, they definitely leave certain users at a disadvantage.

This coding of dating may be a blessing or a curse, depending on what you are looking for. If you are searching for a specific type of partner, whether it is due to religious or cultural reasons, or possibly a preference to personal taste, then the algorithm has succeeded in its matchmaking. However, if you do not have a preference and are attracted to people regardless of their looks or ethnicity or even gender, then you may struggle to find ‘the one’.

For one interviewed individual, the algorithms disguised itself as an asset to her search of potential matches. When asked about her experiences of looking for a long-term partner on Tinder, Priya stated that her matches lacked diversity, however this played to her advantage of matching with Indian males due to cultural values.

Priya: “When I was on Tinder [5 years ago], I never realised algorithms even existed because it was so long ago and I was much younger. I wasn’t in tune with what’s going on behind the scenes. I personally think it helped me, in a weird way, because I was very niche in my search”

On the contrary to Priya, Connor developed his own algorithm with swiping and matching with partners. He values the state of mind over appearance, and it is hard for him to find a match on a platform dominated with shallow images of an individual.

Connor: “I am indifferent [to the algorithm], as I am more attracted to the mind rather than looks. My matches are diverse yet they still fall under certain categories of ‘stereotypes’. I am running my own social experiment on the matters of swiping patterns, seeing who I match with and then after a few conversations on the app decide to meet if we have the same state of mind”

Taking the perspective of a bisexual African-American woman, living in a predominately white society, Dalina argues that no dating app is diverse enough…

Dalina: “[On Hinge] I noticed a pattern starting to form after the first week. Algorithms in dating apps have their pros and cons. I see the positives in filtering out people I probably wouldn’t like in person based off my preferences, but I also don’t have the best judgement of men or women on dating apps. That’s where the negatives come to play. Sometimes people fall for those that aren’t their norm.”

The majority of internet dating platforms are based on the Gale-Shapley Algorithms; formally known as the “Stable Marriage Problem”. The two Nobel prize-winning mathematicians and economists created a series of mathematical patterns that can be applied to relationships. The theory states that when a set of men propose to their favourite woman, each woman rejects all but her favourite man. However, she doesn’t instantly accept the proposal due to the possibility of someone better coming along in the future, and keeps him on a string. Sound familiar?

This is the basis of every dating app created to day; Hinge, Tinder, Bumble. Projecting the rationality of ‘always someone better out there’, therefore the individuals searching for matches and potential partners are never completely satisfied and keep their matched partners on a string. Nowadays, there is so much variety and ease to form romantic relationships, with a swift swipe to the right you have the potential to form an intimate relationship with someone. However, when you have the same option with an X number of partners, along with the realisation that your match is most likely talking to X number of other matches, you are forced to ‘keep your options open’ in order to avoid heartbreak.

Statistics show that there is an imbalance of women to men ratio on dating apps, with an average of 7:10 on Tinder. However, the harsh reality is that women are more judgemental when it comes to rating men on scale of ‘attractiveness’. Studies have shown that a man is 6.2 times more likely to swipe right for a woman he finds attractive, compared to vice versa, which may be adding to the algorithm of matching partners. Since there are more men available to a single woman, men are forced to compete and women judge more brutally on their appearances, and accept some proposals with the idea of someone better coming along. Consequently, dating apps have become the perfect place to ‘shop around’ without responsibilities, risk or emotional pain.

Based on the answers provided by dating individuals and personal experiences of dating apps on diversity I created a social experiment that will test the algorithms in these platforms and how they present potential partners to me.

Tinder, Bumble and Hinge hold the crowns in the matchmaking business. According to 2020 statistics, Tinder has 50 million monthly users across the world, with 1 billion swipes a day. Therefore, I decided to create a profile on each of those platforms, with identical pictures and tag lines to test whether algorithms build upon my swiping patters and determine if I have been my own saboteur of my dating life.

After three weeks of observations and daily right-swiping I have manipulated the algorithm into showing me matches it recognises as ‘my type’. Subsequently, on Hinge, the algorithm developed within 3 days, showing me continuous profiles of males that the algorithm classified as to my desired preference. Throughout time I was beginning to see more consecutive suggestions of matches, as well as repetition of profiles. The other platforms, Bumble and Tinder, took more time to develop an algorithm, yet it still succeeded in results. Despite the main feature in Tinder being location-based, the pattern of my right-swiping and keywords embedded in profile photos allowed me to manipulate the algorithm into showing me males similar in their appearances.

Towards the end of the social experiment amongst the 10 profiles I would right-swipe, on average 8 profiles were consecutive on Bumble, and 6-7 on Tinder, with a staggering 10/10 on Hinge. This shows that in just under a month I have built 80% of the comprehensive algorithm and diminished my chances of matching with males who may qualify as ‘outside my norm’.

This experiment proves that algorithms play an important role in presenting profiles and therefore influencing your decisions in dating. Whether done attentively or subconsciously, the algorithms in dating apps will learn the patterns of your swiping habits and present you with matches that it recognises as your desired preference. Therefore, keeping you in a bubble of matches similar in appearances. For someone like Priya, the algorithms allow her to filter out the matches who are not to her inclination, presenting her with a niche assortment from which she has the ability to form intimate relations. However, for someone like Dalina and Connor who do not have a niche preference in partners, the matching becomes too precise and limited – filtering out other possibilities that they are open to yet have no access.

While these apps are designed to improve personal lives and form romantic relationships with ease, they also produce an over-abundance of connections at the point of establishing relationships brought forth by dominance of profiles and categorisation. I acknowledge that what I have found cannot represent dating app users at large, as users of different gender identities, sexual orientations and cultural origins may be presented with different results.

Therefore, if you’re thinking of creating a dating profile on any platform, pay close attention to the algorithm you create and whether it reflects your preferences to the online or offline world.

May the swipes be ever in your favour.

Towards a Feminist Internet: Combatting Algorithmic Gender Biases

Words: Ellisha Walkden-Williams and Viktoria Bielawa

The Internet has often acted as a stage by which gender biases have been acted out. Just looking at the gender parity in the tech industry, algorithms automating sexist datasets, or the blatant gender stereotyping in subservient female chatbots and home artificial intelligence assistants, gender bias is prevalent in different aspects of the Internet. Humans tend to see sexism as a social problem that needs to be addressed, but AI is not programmed in the same way. 

Algorithmic bias is the result of structural limitations in its design. The imagined objectivity is often conflated with pure, mathematical logic. These AI programs utilise algorithms to function. Algorithms are a procedure for solving a problem by using machine learning and artificial intelligence, typically for the purpose of search engine optimisation. AI is designed to measure, analyse, represent and predict human behaviour. The people who code these AI systems insert their own societal norms and values. Only 22% of these creators are women, which is why popular forms of misogyny may at times be automated within these systems and reflected back to users as unequivocal fact. The problem is language. Cultural biases are constituted through language, then recorded, and finally transferred into ideological meaning. This is coded into the very interfaces of algorithmically designed technologies — exposing and consummating existing social prejudices and identities. 

The tech world is starting to realise these problems, and The Feminist Internet launched in 2017 by Charlotte Webb to challenge systematic gender bias that was circulating on the Internet. It has programs like Hollabot, a prototype app that tackles online abuse, or F’xa, a chatbox that educates users about AI bias, to help eliminate gender bias online. The team has introduced ‘Designing a Feminist Alexa’ workshops, allowing students to prototype and code alternatives to Alexa using feminist standards. Then there’s the configuration of a Feminist Alexa, which fights for a more equitable Internet for all users. 

As part of Our Algorithmic Lives, Feminist Internet will be presenting ‘In Your Shoes’ Chatbot in collaboration with UAL MA students. The chatbot has been designed as a futuristic job-hunting tool that purposefully discriminates against marginalised subgroups. Imbued with humanistic qualities, users are able to interact with the bot within the exhibition space — making you think about your own attitudes and the need for algorithmic accountability for the sake of future generations. We spoke to The Feminist Internet’s founder to discuss how the movement is changing the conversation around AI and bias. 

What urged you to create Feminist Internet?

I had just finished my practice-based PhD looking at how the Internet changes the way we understand creative authorship, and was increasingly frustrated with inequalities I saw both in the digital creative industries and academia. I was invited to propose a research project for UAL looking at tech and gender, and it evolved into a 10-day course for UAL students, where we co-created the Feminist Internet Manifesto and prototyped creative responses to gender inequality relating to the Internet. Many of the participants wanted to keep our work going, and so Feminist Internet has evolved organically ever since then.  

What are Feminist Internet’s key principals? 

Our mission is to make the internet more equal through critical, creative practice. We address issues such as AI bias, online abuse and the environmental impact of technologies. Our approach is always to engage audiences with the complexities of these topics in a clear, accessible and playful way. As well as the points our manifesto, we focus on bringing feminist approaches to technology development, encouraging the next generation of talent to foreground equality and inclusion in their practices so they can be a force for good in the digital (and wider) world.   

What do you hope to achieve through this movement? 

We hope to raise awareness amongst young people, particularly those interested in becoming designers, developers, social entrepreneurs, and equip them with the practical, technical and conceptual tools to promote equality in techno-social systems. We also hope to contribute to the growing field of activists, academics, and developers who together are calling for tech companies to be more accountable for the negative social impacts their platforms and services can cause.  

Is it the technology or the programmed algorithms of the platform that are non-inclusive, or is it the users shaping them? 

There is a feedback loop between existing social biases, data sets, machine learning models, and people’s behaviours. We can’t separate these from each other since they all co-exist and tend to reflect each other. For example, take a healthcare algorithm that prioritises white patients over sicker black patients. The algorithm is designed to direct medical care to those most in need, but it makes its predictions based on data about people’s past health costs. Black patients historically receive less health care than white patients, so the system flags white patients more often because they have spent more on health care in the past. In this case, there is a problem with the design of the system - using cost as a proxy for health status, but there is also a problem with society! The reason black people spend less on healthcare is due to systemic social and economic inequalities.  

Due to patriarchal and capitalist values still being so rooted in our society, do you think the world needs to change before the internet does? 

The world and the internet can’t be separated - they are part of one ecosystem. As long as racism, sexism, white supremacy, capitalism and other oppressive systems exist, the internet will reproduce them. That’s not to say there isn’t great potential for the internet to be a liberating force, but that it reflects society, as society reflects it.