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The Love Machine

Written for Ours, Scientifica for the NUS Science Journalism Club

Babe There's something tragic about you Something so magic about you Don't you agree?
From Eden, Hozier

“Let me not to the marriage of true minds admit impediments”

This would be without a doubt a great opening line on Tinder. And for something so widespread, we perhaps fall victim to the swipe first question later trope. And a question we ought to ask is how these apps frame our view of love in terms of tradeable quantities. Here, I hope to present a case as to how dating apps and the companies behind them are participants in the worrying commodification of love.

The notion of using the internet to search for romantic partners is almost as old as the internet itself, with Operation Match (a questionnaire) operating since 1964. In fact the online dating market is so huge, it is the second most profitable Internet based economy (one can only imagine what the first is). Tinder for example netted 1.6 billion dollars in 2021, a 17% increase from the previous year. And something to note is that consumers are paying for these apps not only with their money but also with their deeply private and intimate data. Professor of Information Technology at Carnegie Mellon, Allesandro Acquisti says “Tinder knows much more about you when studying your behaviour on the app. It knows how often you connect and at which times; the percentage of white men, black men, Asian men you have matched. Personal data is the fuel of the economy. Consumers’ data is being traded and transacted for the purpose of advertising.” Now this is not necessarily a bad thing, in fact in many cases this is life saving as people can screen out potentially harmful individuals. Increase in online dating has made blind dates less of a necessity and as such, people go into dates with more background knowledge about the other party.

Yet it is my position that online dating has taken the actual act of dating and commodified it immensely since. As noted by Heino et al. in Relationshopping, dating sites are usually designed to facilitate the sensation of window shopping and lightning fast (read: compulsive) decision making based on a few central attributes. To quote the research, “the functional and design of online dating sites encouraged participants to adopt a marketplace orientation towards the online dating experience.” In this sense, people are dehumanised and viewed more as commodities. Again, I would like to stress that none of this is inherently evil. One could argue all of this leads to a better experience for the consumers. Yet what I would like to draw the reader’s attention to is how these sites may not have aligned goals with you and when the stakes are as high as who we want to spend our lives with, we should at the very least be aware of these issues.

And now, we must consider the role that machine learning plays in all of this. Before proceeding much further, we should define what we mean by machine learning (ml). A more palatable definition would be that ml is applied mathematics to make predictions and model systems. It should not surprise people that technologies like this are being leveraged in the dating sphere. What should surprise and worry people is how widespread and impenetrable most of these technologies are. Tinder uses a ml software called VecTec and mines our personal information from sites like Facebook, Instagram and Spotify to build a stronger dossier to make predictions. Sounds great? Well not so much given how much it places importance in past decisions. To quote Hutson, Taft, Barocas and Lev “If a user had several good Caucasian matches in the past, the algorithm is more likely to suggest Caucasian people as ‘good matches’ in the future.” Considering how messy and multivariate relationships are, reducing things solely on a racial paradigm seems myopic, at least to this writer. This is even before we enter the topic of racist AI’s such as how systems trained on tainted data sets now disproportionately recommend for black people to be arrested. Given most machine learning models are black boxes, we cannot effectively pinpoint when a model would make a biassed prediction and when it's unbiased.

Having explained the technical difficulties facing us, I would like to offer a more philosophical and introspective view. It appears that there has been a serious epistemological shift of how we currently view the topic of love and romance. An increased sanitisation of the whole process of falling in love is becoming increasingly facilitated by major corporations. Apps like Tinder serve as a middle-man in this pursuit. There is an increasing push to view love as something transactional, economic and tradeable. Something to note is that in the English language, the action best used to describe being enamoured with someone is to ‘fall in love’. Key word here being fall. Hence, as pointed out by Slovenian philosopher Slavoj Zizek, there is an unexpected, uncontrollable, catastrophic element to love. Yet, we are becoming increasingly averse to this part of love simply because we cannot control it. Dating sites, much like mediaeval matchmakers, stand in between our fragile hearts and the cruel cold world. To quote Zizek, “we want beer without alcohol, coffee without caffeine and love without the danger.” This isn't to say one cannot find love through these apps, in fact many do. But as pointed out above, oftentimes these huge conglomerates have a vested-interest in keeping us continually engaged within their insular app ecosystem. And we should be cognisant of this whilst on their sites.

So, what is it I am trying to say? We have to make the best of what we have. These sites are enormously powerful. At the same time, there are huge structures in place that often look to exploit us. I’m hopelessly hopeful you're just hopeless enough to believe that a romantic notion of love is still possible, if that is what you are looking for. Love can be both tragic and magic. Love does not have to be transactional. And if this be an error and upon me proved, I have never written nor no man has ever loved.

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