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Inside the algorithms of dating apps

In the ever-evolving landscape of modern romance, dating apps have become the gatekeepers of our romantic destiny. What once relied on serendipity and face-to-face encounters has now been replaced by algorithms, quietly working behind the scenes to curate our potential matches. While success stories often make headlines, the intricate workings of these algorithms and their potential pitfalls are seldom discussed. 

The foundation of dating app algorithms lies in data, a vast pool sourced from various channels. As this article by Rewire suggests, users willingly contribute by filling out questionnaires, providing feedback, and even syncing their social media profiles. The apps meticulously track every click, interaction, and in-app message, creating a comprehensive profile of individual preferences. 

The hidden algorithm driving these apps, though shrouded in secrecy, is believed to employ collaborative filtering. Much like Netflix or Facebook, it predicts preferences based on user behavior and the majority’s opinion. If you express a dislike for blonde men, the algorithm adapts by showing you fewer or no blonde profiles. It tailors recommendations based on your past choices, creating a personalized experience. 

However, this personalization comes at a cost – the reinforcement of biases. Dating apps, unintentionally or not, perpetuate racial, physical, and other biases by relying on collaborative filtering. The echo chamber effect occurs when the algorithm assumes shared preferences among users with similar tastes, limiting exposure to diverse individuals. 

Moreover, dating app algorithms struggle to adapt to the evolving nature of human preferences. As users’ tastes and priorities change over time, the algorithm, rooted in historical data, may lag in reflecting these shifts. This poses a challenge for individuals seeking different experiences or relationships than initially anticipated. 

In essence, the dating app algorithm is a work in progress. While it excels at recognizing logical patterns, it falls short in capturing the complexity of human dynamics. For a more accurate reflection of the human experience, algorithms must evolve to consider diverse and changing tastes. As we continue entrusting our romantic fate to algorithms, these need to become more transparent and responsible, and that comes from (we know you know): Algorithmic audits. 

Also called AI audits, these mechanisms scrutinize data to shed light on the potential impacts of these algorithms. This way, we can see if an algorithm is being effective by detecting patterns or simply relying on outdated, biased data that leads to discrimination and inefficiencies.  

Want to know more about our work decrypting the algorithms of YouTube, TikTok, or Uber? Check out our website and don’t forget to ask your favorite apps to get audited!