The Biases we feed to Tinder algorithms (2023)

As the basis for one of the fastest growing social networking apps in the world, Tinder algorithms play an increasingly important role in the way people meet each other. As Tinder algorithms receive input from users' activity, theylearn, adapt, and act accordingly. In a way, the workings of an algorithm hold up a mirror to our societal practices, potentially reinforcingexisting racial biases.

Tinder Algorithms: Welcome to #swipelife

Tinder is one of the fastest growing social networking apps on a global scale. With users in 190 countries swiping1,6 billion pictures and generating around 20 billion matches every day, the location-based datingapplication plays a game-changing role in the dating world. (Liu, 2017)This article reflects on how the biases of Tinderalgorithms hold up a mirror to our societyby analyzing the human impact on their technological workings.

Online news outlets are cluttered with articles on how to win the Tinder game.In the realm of online forumssuch as Reddit, users collectively try and decode Tinderalgorithms by analyzing their personal experiences with it. In order to get more matches, people try and make sense of how the algorithm works, discuss which swiping behavior might be penalized or awarded, why certain profiles disappear from the ‘field’ or are being ‘chocked’ from new profiles to swipe on.

“Tinder is more than a dating app. It's a cultural movement. Welcome to #swipelife.” (

What materializes in both news articles and forums is frequent claims about Tinder algorithms being somewhat biased. They discuss how online dating is tricky, not because of people, but because of the algorithms involved. Both user experiences and experiments indicate that online dating applications seem to be reinforcing racial prejudices within the swiping community. (Sharma, 2016; Hutson, Taft, Barocas & Levy, 2018) “Although partner preferences are extremely personal, it is argued that culture shapes our preferences, and dating apps influence our decisions.” (Lefkowitz, 2018)

The public relevance of algorithms

According to Gillespie,algorithms shouldn’t be perceived as ‘cold mechanisms’, becausethey are just as much constituted by ‘warm human and institutional choices’ as they are based on technical achievements. (2014: 169) Depending on how an algorithm is programmed, the users' online behavior and the set of data it is given to process, certain cultural aspects will be highlighted while others are left out. Some information of a certain group is prioritized, which affords them greater visibility, while others are rendered invisible. Through this, algorithms play a crucial role in overall participation in public life.Scholars stress the importance of interrogating algorithms as a “key feature (...) of the cultural forms emerging in their shadows” (Gillespie, 2014: 169; Anderson, 2011 & Striphas, 2010).

Approaching algorithms from a sociological perspective, there are different dimensions to its public relevance. One of these is the promise of algorithmic objectivity.This refers to “the way the technical character of the algorithm is positioned as an assurance of impartiality, and how that claim is maintained in the face of controversy”. (Gillespie, 2014: 168)

Another dimension relates to the assumptions made by the algorithm's providers to know and predict their user's practices. Gillespie refers to these as ‘the circles of anticipation.’ (Gillespie, 2014: 168) This second dimension concerns the ways in which users reshape their online behavior to benefit from the algorithms they are dependent on. (Ibid.: 168).

An algorithm can only function when paired with a database, so in order to uncover possible biases of an algorithmic output, the human interference with algorithms needs to be included. This includes the input from both platform users andits developers. This is necessary because “Algorithms are made and remade in every instance of their use because every click, every query, changes the tool incrementally." (Gillespie, 2014: 173) So then, how are Tinder’s algorithmsprogrammed, how are the user and provider influencing their workings, and what data flows into their calculations?

Machine-learning Tinder algorithms

The very notion of algorithms is rather elusive, and the specific workings of underlying Tinder algorithms are not publicly revealed. This doesn't come as a surprise, as developers and platform providers in general rarely giveinsight into the coding of their underlying programs. They stressnot only that algorithms must not be tampered with as they are based on technological neutrality, but also the fact that they’d likely be copied and re-used by competing providers. (Gillespie, 2014: 176)

(Video) Tinder's algorithm: The secret reason you're still single

However, certain features of Tinder algorithms are ‘known’, either through practical evaluation of user experiences or through the app's providers themselves.

Tinder is based on a collection of algorithms that augments their processes to solve problems on a bigger scale. In other words:each of the Tinder algorithms is programmed to collect a set of data that aretabulated accordingly to contribute a relevant output. These results then work together to improve the overall user-experience, which is achieved when there is a notable increase of matches and messages. Since each user has individual preferences, it also needs personalized recommendation systems, which are obtained through collaborative filtering and algorithmic calculations. (Liu, 2017)

If you are losing the Tinder game more often than not, you will likely never get to swipe on profiles clustered in the upper ranks

One part of this collective is the Elo-score, also referred to as the ‘algorithm of desire’. Thisis, as confirmed by Tinder’s founder Sean Rad, a scoring system that ranks people according to their ‘desirability’.The term itself is derived from the chess world, where it is used to rank a player’s skill levels. Accordingly, this score is set up to compare users and match people who have similar levels of desirability – if you are losing the Tinder game more often than not, you will likely never get to swipe on profiles clustered in the upper ranks.(Carr, 2016)

Desire, though, depends on various factors that are based on personal preferences, which aren’t universal. These are most definitely not objective, but very much subjective in nature. So how canTinder algorithms objectively calculate a person’s desirability?

Tinder algorithms detects a user’s swiping patterns and uses those for future recommendations. (Carr, 2016) Basically, people who are on a same level of giving and receiving when it comes to right ("like") and left ("pass") swipes, are understood by Tinder algorithms to be equally often desired by other users. This makes it likely that their profiles are rendered visible to one another. Although, Rad argues: “It is not just how many people swipe right on you… its very complicated. It took us two and a half months just to build the algorithm because a lot of factors go into it.” (Cited in Carr, 2016) Nonetheless, details of those factors are not revealed, just like the score itself is not publicly accessible to users.

Being rejected is something that people will try to avoid as much as possible. “The beauty of Tinder, after all, is that rejection has been removed entirely from the process, since you have no idea who dismissed your profile.” (Cited in Carr, 2016) This process is kept hidden from the users, even though it might be consideredknowledge about the self that one is entitled to in order to know one's position in the ‘playing field’.

Surprisingly though, it is not only the process of rejection, the number of left swipes, that is kept from the user. The same goes forthe reception of right swipes. (Bowles, 2016) Tinder algorithms can actively decideto deny you a match, or several matches, simply by not showing them to you. Tinder programmed this ‘behavior’ into the algorithm to slow down the upper percentages of most ‘desirable’ people, by rendering their profiles less visible to other users in order to give people with lower rankings a chance.

Jonathan Badeen, Tinder’s senior vice president of product, sees it as their moral obligation to program certain ‘interventions’ into the algorithms.“It’s scary to know how much it’ll affect people. […] I try to ignore some of it, or I’ll go insane. We’re getting to the point where we have a social responsibility to the world because we have this power to influenceit.” (Bowles, 2016)

Swipes and swipers

As we are shifting from the information age into the era of augmentation, human interaction is increasingly intertwined with computational systems. (Conti, 2017) We are constantly encountering personalized recommendationsbased on our online behavior and data sharing on social networks such as Facebook, eCommerce platforms such as Amazon, andentertainment services such asSpotify and Netflix. (Liu, 2017)

(Video) Chapter 1: The science behind Tinder

As a tool to generate personalized recommendations, Tinder implemented VecTec:a machine-learning algorithm that is partly paired with artificial intelligence (AI). (Liu, 2017) Algorithms are designed to develop in an evolutionary manner, meaning that the human process of learning (seeing, remembering, and creating a pattern in one’s mind) aligns with that of a machine-learning algorithm, or that of an AI-paired one. An AI-paired algorithm can even develop its own point of view on things, or in Tinder’s case, on people.Programmers themselves will eventually not even be able to understand why the AI is doing what it is doing, for it can developa form of strategic thinking that resembles human intuition. (Conti, 2017)

A study released by OKCupid confirmed that there is a racial bias in our society that shows in the dating preferences and behavior of users

At the 2017 machine learning conference (MLconf) in San Francisco, Chief scientist of Tinder Steve Liu gave an insight into the mechanics of the TinVec approach. For the system, Tinder users are defined as 'Swipers' and 'Swipes'. Each swipe made is mapped to an embedded vector in an embedding space. The vectors implicitly represent possible characteristics of the Swipe, such as activities (sport), interests (whether you like pets), environment (indoors vs outdoors), educational level, and chosen career path. If the tool detects a close proximity of two embedded vectors, meaning the users share similar characteristics, it will recommend them to another. Whether it’s a match or not, the process helps Tinder algorithms learn and identify more users whom you are likely to swipe right on.

Additionally, TinVec is assisted by Word2Vec. Whereas TinVec’s output is user embedding, Word2Vec embeds words. This means that the tool does not learn through large numbers of co-swipes, but rather through analyses of a large corpus of texts. It identifies languages, dialects, andforms of slang.Words that share a common context are closer in the vector space and indicate similarities between their users' communication styles. Through these results, similar swipes are clustered together and a user’s preference is represented through the embedded vectors of their likes. Again, users with close proximity to preference vectors will be recommended to each other. (Liu, 2017)

But the shine of this evolution-like growth of machine-learning-algorithms shows the shades of our cultural practices. As Gillespie puts it, we need to be aware of 'specific implications' when relying on algorithms “to select what is most relevant from a corpus of data composed of traces of our activities, preferences, and expressions.” (Gillespie, 2014: 168)

A study released by OKCupid (2014) confirmed that there is a racial bias in our society that shows in the dating preferences and behavior of users. It shows that Black women and Asian men, who are already societally marginalized, are additionally discriminated against in online dating environments.(Sharma, 2016)This has especially dire consequences on an app like Tinder, whose algorithms are running on a system of ranking and clustering people, that is literally keeping the 'lower ranked' profiles out of sight for the 'upper' ones.

Tinder Algorithms and human interaction

Algorithms are programmed to collect and categorize a vast amount of data points in order to identify patterns in a user’s online behavior. “Providers also take advantage of the increasingly participatory ethos of the web, where users are powerfully encouraged to volunteer all sorts of information about themselves, and encouraged to feel powerful doing so.” (Gillespie, 2014: 173)

Tinder can be logged onto via a user’s Facebook account and linked to Spotify and Instagram accounts. Thisgives the algorithms user information that can be rendered into their algorithmic identity. (Gillespie, 2014: 173) The algorithmic identity gets more complex with every social media interaction,the clicking or likewise ignoring of advertisements, and the financial status as derived from online payments. Besides the data points of a user’s geolocation(which are indispensable for a location-based dating app),gender and age are added by users and optionally supplemented through ‘smart profile’ features, such as educational level and chosen career path.

Gillespie reminds us how this reflects on our ‘real’ self: “To some degree, we are invited to formalize ourselves into these knowable categories. When we encounter these providers, we are encouraged to choose from the menus they offer, so as to be correctly anticipated by the system and provided the right information, the right recommendations, the right people.”(2014: 174)

“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”

(Video) Algorithms are Destroying Society

So, in a way, Tinder algorithms learns a user’s preferences based on their swiping habits and categorizes them within clusters of like-minded Swipes. A user’s swiping behavior in the past influencesin which cluster the future vector gets embedded. New users are evaluated and categorized through the criteria Tinder algorithms havelearned from the behavioral models of past users.

This raises a situation that asks for critical reflection.“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”. (Lefkowitz 2018) This may be harmful, for it reinforces societal norms: “If past users made discriminatory decisions, the algorithm will continue on the same, biased trajectory.” (Hutson, Taft, Barocas & Levy, 2018 in Lefkowitz, 2018)

In an interview with TechCrunch (Crook, 2015), Sean Rad remained rather vague on the topic of how the newly added data points that are derived from smart-pictures or profilesare ranked against each other, as well as on how that depends on the user. When asked if the pictures uploaded on Tinder are evaluated on things like eye, skin, and hair color, he simply stated: “I can’t reveal if we do this, but it’s something we think a lot about. I wouldn’t be surprised if people thought we did that.”

According to Cheney-Lippold (2011: 165), mathematical algorithms use “statistical commonality models to determine one’s gender, class, or race in an automatic manner”, as well as defining the very meaning of these categories. These features about a user can be inscribed in underlying Tinder algorithmsand used just like other data points to render people of similar characteristics visible to each other. So even if race is not conceptualized as a feature of matter to Tinder’s filtering system, it can be learned, analyzed and conceptualized by its algorithms.

We are seen and treated as members of categories, but are oblivious as to what categories these are or what they mean. (Cheney-Lippold, 2011) The vector imposed on the user, as well as its cluster-embedment, depends on how the algorithms make sense of the data provided in the past, the traces we leave online. However invisible or uncontrollable by us, this identity does influence our behavior through shaping our online experience anddetermining the conditions of a user’s (online) possibilities, which ultimately reflects on offline behavior.

While it remains hidden which data points are incorporated or overridden, and how they are measured and weighed against one another, this may reinforce a user’s suspicions against algorithms. Ultimately, the criteria on which we are ranked is “open to user suspicion that their criteria skew to the provider’s commercial or political benefit, or incorporate embedded, unexamined assumptions that act below the level of awareness, even that of the designers.” (Gillespie, 2014: 176)

Tinder and the paradox of algorithmic objectivity

From a sociological perspective, the promise of algorithmic objectivity seems like a paradox. Both Tinder and itsusers are engaging and interfering with the underlying algorithms, which learn, adapt, and act accordingly. They follow changes in the program just like they adapt to social changes. In a way, the workings of an algorithm hold up a mirror to our societal practices, potentially reinforcingexisting racial biases.

However, the biases are there in the first place because they exist in society. How could that not be reflected in the output of a machine-learning algorithm?Especially in those algorithmsthat are built to detect personal preferences through behavioral patterns in order to recommend the right people. Can an algorithm be judged on treating people like categories, while people are objectifying each other by partaking on an app that operates on a ranking system?

We influencealgorithmic output just like the way an app works influences our decisions. In order to balance out the adopted societal biases, providers are actively interfering by programming ‘interventions’ into the algorithms. While this can be done with good intentions, those intentions too, could be socially biased.

The experienced biases of Tinderalgorithms are based on a threefold learning process between user, provider, and algorithms. And it’s not that easy to tell who has the biggest impact.

(Video) AI Governance & Risk Management | Kartik Hosanagar | Talks at Google


Bowles, N. (2016) Mr (swipe) right? After a year of tumult and scandal at Tinder, ousted founder Sean Rad is back in charge. Now can he – and his company – grow up?

Carr, A., (2016). Fast I Found Out My Secret Internal Tinder Rating and Now I Wish I Hadn’t.

Cheney-Lippold, J. (2011). A new algorithmic identity: Soft biopolitics and the modulation of control. Theory, Culture & Society 28 (6), 164-181.

Conti, M. (2017). Ted: The incredible inventions of intuitive AI.

Crook, J. (2015). Tinder introduces a new matching Algorithm.

Gillespie, T. (2014). The relevance of algorithms. In Gillespie, Tarleton, Pablo J. Boczkowski & Kirsten A. Foot (eds.) Media technologies: Essays on communication, materiality and society. MIT Scholarship Online, 167-193.

Hutson, J.A., Taft, J. G., Barocas, S. and Levy, K. (2018). “Debiasing Desire: Addressing Bias & Discrimination on Intimate Platforms” Proceedings of the ACM On Human-Computer Interaction (CSCW) 2: 1–18,

Lefkowitz, M. (2018) Redesign dating apps to lessen racial bias, study recommends.

Liu, S. (2017). In SlideShare: Personalized Recommendations at Tinder: The TinVec Approach.

MLconf (2017). MLCONF 2017 SAN FRANCISCO.

(Video) Congress Was Confused by the Internet During Hearing With Google CEO | NowThis

Sharma, M. (2016). Tinder has a race problem nobody wants to talk about.


What is wrong with Tinder algorithm? ›

We can also guess that the algorithm rewards pickiness and disincentivizes people to swipe right too much. You're limited to 100 right swipes per day in Tinder, to make sure you're actually looking at profiles and not just spamming everyone to rack up random matches.

How to crack the Tinder algorithm? ›

How to increase your score within Tinder's algorithm?
  1. Hack #1: Be exhaustive. ...
  2. Hack #2: Use all Tinder's features. ...
  3. Hack #3: Connect often. ...
  4. Hack #4: Travel. ...
  5. Hack #5: Be super selective. ...
  6. Hack #6: Write to your Tinder matches, fast. ...
  7. Hack #7: Use elaborate vocabulary. ...
  8. Hack #8: Use the features, once more.

What is bias in an algorithm? ›

Machine learning bias, also sometimes called algorithm bias or AI bias, is a phenomenon that occurs when an algorithm produces results that are systemically prejudiced due to erroneous assumptions in the machine learning process.

Why do I suddenly get no Tinder matches? ›

If only one or even a few of your matches have disappeared, they've most likely ended the match or deleted their Tinder account. If they deleted their account and decide to come back to Tinder, you may see that person reappear in your card stack.

Why I don't get any matches on Tinder? ›

Look over your profile

Tinder will tell you if your profile has been deactivated but make sure it's also set up correctly. Check that your photos and bio text are still there. For so many who ask “why am I not getting matches on Tinder?”, the answer is somewhere in their profile setup.

Does Tinder have an attractiveness algorithm? ›

Users are then sorted into tiers based on their desirability score, and that was, in essence, the algorithm: you get presented with people approximately your level of attractiveness when you swipe.

Does Tinder punish you for resetting? ›

Resetting your Tinder account can get you shadowbanned, especially if you delete it then try to recreate it a few minutes later. (Classic bot behavior.) When you're shadowbanned, the app seems normal to you, but no one else can see you. So you won't be getting any matches.

Why am I shadowbanned on Tinder? ›

A Tinder Shadowban happens when someone breaks Tinder's community guidelines. A shadowbanned user can still access the app and everything will seem normal.

What causes algorithmic bias? ›

The short answer: People write the algorithms, people choose the data used by algorithms and people decide how to apply the results of the algorithms. Without diverse teams and rigorous testing, it can be too easy for people to let subtle, unconscious biases enter, which AI then automates and perpetuates.

What are the types of algorithmic bias? ›

There are two types of bias in AI. One is algorithmic AI bias or “data bias,” where algorithms are trained using biased data. The other kind of bias in AI is societal AI bias. That's where our assumptions and norms as a society cause us to have blind spots or certain expectations in our thinking.

What are some causes of algorithmic bias? ›

There are three main causes of algorithmic bias: input bias, training bias, and programming bias. 20 On the other hand, algorithmic outcomes oftentimes labeled as “biased” may simply reflect unpleasant facts based on causal relationships derived from reliable representative data.

What is an example of algorithms bias? ›

The term algorithmic bias describes systematic and repeatable errors that create unfair outcomes, such as privileging one arbitrary group of users over others. For example, a credit score algorithm may deny a loan without being unfair, if it is consistently weighing relevant financial criteria.

What are five common types of bias? ›

Reduce your unconscious bias by learning more about the five largest types of bias:
  • Similarity Bias. Similarity bias means that we often prefer things that are like us over things that are different than us. ...
  • Expedience Bias. ...
  • Experience Bias. ...
  • Distance Bias. ...
  • Safety Bias.
Feb 25, 2021

What are the dangers of algorithmic bias? ›

Algorithmic biases could intensify the inequalities among people and affect their lives. Another risk is that a person's chances of landing a job that they deserve could be reduced, simply because they belong to a group that the algorithm is biased against.

How many likes does the average girl get on Tinder? ›

Average Tinder likes for a girl

Tinder typically allows girls over 50 likes per day. Why do women come out on top? They're more selective. Tinder's algorithm determines the like limit on users.

Is Hinge better than Tinder? ›

Overall, Tinder may be the better option for those looking for a casual fling or one-night stand, whereas Hinge is the better choice for those seeking something more serious and long-term.

Why is it harder to get matches on Tinder? ›

When you first open an account, Tinder shows you to a very wide audience for about a day. They use this time to calculate and determine whom your audience should be. This is why you might get a bunch of matches your first few days, but then it drops considerably in a week.

How many matches does the average guy get on Tinder? ›

The average match rate for guys on Tinder is about 1 in 10. So, if a guy swipes right 100 times, he'd typically get 10 matches. However, this isn't just a matter of blindly swiping right - you also need to be selective and not too picky.

Is it normal for guys to get no matches on Tinder? ›

You're not alone. For guys, it's extremely common to get no matches on Tinder — despite swiping right on pretty much everyone. You might start to think that you're ugly or not successful enough to impress Tinder matches.

Is Bumble better than Tinder? ›

Bumble is better than Tinder if you're looking for a serious relationship. The matches are of higher quality overall, and many of the women you'll find on the app are “meet-the-parents-ready” marriage material. Bumble was started by one of Tinder's founders who wanted to create a more “women-friendly” app.

Does Tinder show the hottest people first? ›

Hot people come first (allegedly)

Those within your gender, age and distance restrictions who have the highest attractiveness score will be the first 10-15 users you see when you first open the app, because this makes you think that Tinder has LOADS of fit people on it.

What algorithm gets you more likes on Tinder? ›

The more “things you do” the more the algorithm likes you.
  1. Changing your profile bio regularly to show you're active.
  2. Messaging people daily to confirm you actually engage with your matches.
  3. Opening the app every day to swipe a few times.
  4. Using your free daily “super” like (or 5 if you pay for Tinder Gold).
Oct 25, 2022

Which gender uses Tinder the most? ›

Tinder gender demographics

Tinder has a gender imbalance, with 75% of users identifying as male.

What is the 3 month rule on Tinder? ›

Open Tinder and create your new account. You'll need a new phone number to sign up, unless you wait 3 months in between deleting your Tinder and creating a new one. If you don't wait, it will need to be a number you've never used for Tinder before and that you can access to get the SMS verification codes.

What are the unspoken rules of Tinder? ›

8 Unwritten Rules Of Engagement On Tinder
  • DO NOT. ...
  • Get on Tinder to Make Friends.
  • Have Inanimate Objects in Your Profile Picture.
  • Put No Solo Pictures of Yourself.
  • Put Your BBM Pin/ Snapchat ID in Your Description.
  • ***** Grammar.
  • Tell Your Life Story Within the First Three Minutes of Conversation.
  • Give One Word Responses.
Jan 24, 2015

Does Tinder unban you after a while? ›

Their official ruling on bans is that they're not reversable, but there's a slight chance they'll remove the ban if you appeal. You'll know you're banned if you log in to the app or website and you get a message you've violated their terms of use.

How long does shadowban last? ›

According to creators who've been shadow banned, a TikTok shadow ban typically lasts around two weeks.

Is Tinder shadowban forever? ›

Just like a regular Tinder ban the shadowban lasts forever. Yes, you heard me right. Once your account has been shadowbanned there is no escape. Except resetting your account, and I explain to you how to do it here.

Does Tinder shadowban ever go away? ›

Tinder shadowban is Tinder penalizing you for doing something wrong (i.e breaking the Tinder community guidelines) without you knowing you're being penalized. Your profile becomes almost invisible, and it'll take you some time until you realize something's actually wrong. The shadowban lasts forever.

Which algorithm has higher bias? ›

Generally, a linear algorithm has a high bias, as it makes them learn fast. The simpler the algorithm, the higher the bias it has likely to be introduced. Whereas a nonlinear algorithm often has low bias.

Why do algorithms make mistakes? ›

Algorithms tend to fail when they have the wrong data or focus on the wrong outcomes.

How do you avoid bias errors? ›

One of the most effective methods that can be used by researchers to avoid sampling bias is simple random sampling, in which samples are chosen strictly by chance. This provides equal odds for every member of the population to be chosen as a participant in the study at hand.

What are the 4 types of algorithm? ›

There are four types of machine learning algorithms: supervised, semi-supervised, unsupervised and reinforcement.

What are the 7 forms of bias? ›

  • Seven Forms of Bias.
  • Invisibility:
  • Stereotyping:
  • Imbalance and Selectivity:
  • Unreality:
  • Fragmentation and Isolation:
  • Linguistic Bias:
  • Cosmetic Bias:

What is the most common type of bias? ›

Confirmation Bias

One of the most common cognitive biases is confirmation bias. Confirmation bias is when a person looks for and interprets information (be it news stories, statistical data or the opinions of others) that backs up an assumption or theory they already have.

What are algorithmic problems? ›

What is an algorithmic problem? Simplest and robust definition: An algorithmic problem is simply to compute a function f : Σ∗ → Σ∗ over strings of a finite alphabet. Algorithm A solves f if for all input strings w, A outputs f (w).

How can algorithmic bias negatively impact business performance? ›

Algorithmic bias not only carries risks of legal exposure, but it can also cause sub-optimal performance. For example, bias in lending practices can lead to both financial losses when bias favors bad credit risks, and missed financial gain when bias unfairly discriminates against good credit risks.

What do algorithms affect? ›

In a world filled with more content than we could ever possibly consume, algorithms are a necessary part of the internet. But algorithms can also have unintended consequences, like creating filter bubbles, perpetuating bias, and undermining our creativity, choices, and opportunities.

What are 3 examples of algorithms? ›

Common examples include: the recipe for baking a cake, the method we use to solve a long division problem, the process of doing laundry, and the functionality of a search engine are all examples of an algorithm.

What are three bias examples? ›

Confirmation bias, sampling bias, and brilliance bias are three examples that can affect our ability to critically engage with information. Jono Hey of Sketchplanations walks us through these cognitive bias examples, to help us better understand how they influence our day-to-day lives.

What is a simple example of bias? ›

Biases are beliefs that are not founded by known facts about someone or about a particular group of individuals. For example, one common bias is that women are weak (despite many being very strong). Another is that blacks are dishonest (when most aren't).

What are popular biases? ›

Pessimism Bias: We sometimes overestimate the likelihood of bad outcomes. Optimism Bias: We sometimes are over-optimistic about good outcomes. Blind Spot Bias: We don't think we have bias, and we see it others more than ourselves.

What is bias and why should it be avoided? ›

In academic writing, it is important to avoid using language that can be seen as biased. Bias happens when writers choose language that is either not specific or not sensitive to labels. Section 5 of the APA Manual covers the many ways that bias can appear in writing and how to avoid those.

What is the problem with bias? ›

Bias distorts truth. It is wrapped up with our humanity, a bug in the system that we don't often recognize. Bias can be as straightforward as unconscious preference and it can be simple or sinister. But, however they manifest, the biases we harbor interfere with our ability to understand the real environment around us.

Is Tinder getting worse? ›

How are Tinder App Ratings Changing Over Time? Since its peak of 4.05 in October 2014 Tinder ratings have been steadily declining to its current position of 2.35 as of February 2022. This is 42% drop in average rating over the app's 8-year life span.

Why are there so many bots on Tinder? ›

Why are there bots on Tinder? Tinder bots give scammers a way to obtain personal information, trick people out of money, or infect devices with malware. Experts estimate that bots make up a quarter of all web traffic, and Tinder is no exception.

How do you know if your Tinder is shadowbanned? ›

  1. You open up your phone, scroll through your apps and find the Tinder app. ...
  2. Once the 12 hours cooldown period ends, you go back to swiping and the same story like before transpires. ...
  3. When you're shadowbanned, that symbol won't even show. ...
  4. So yeah, you'll still be able to login.

What is the male to female ratio on Tinder? ›

Tinder gender demographics

Tinder has a gender imbalance, with 75% of users identifying as male.

Does Tinder work for average guys? ›

“While most women can get matches easily even when they are highly selective, the majority of men must temper their expectations. The data also tells us that some men will likely struggle to get a date on Tinder even after thousands of swipes and many months trying,” Gerrard and Bø say.

Is Tinder all about hooking up? ›

Culturally, Tinder is known as a hook up app and it's really just that, DO NOT EXPECT TO FIND LOVE.” Since its inception in 2012, Tinder has been known as a hookup app, but this may be changing. Like Bumble, some users have met spouses or long-term partners on the app, while others use it strictly for casual flings.

How do you confuse a Tinder bot? ›

Try asking something unexpected like “What color is the sky” or type a few random letters and see whether the responses make sense; Conversations turn into a request to click on a link to continue chatting outside of Tinder, verify your profile, or check some photos.

What percentage of Tinder is bots? ›

We discovered that 10% of all new dating profiles created were fake. We also found that: Male profiles are 21% more likely to be fake than female profiles.

How do you tell if a girl is a bot on Tinder? ›

How to detect you are talking to a bot in a dating app
  1. Be creative when choosing your conversation topics. ...
  2. Look for repetitive patterns. ...
  3. Ask about recent events. ...
  4. Talk in any language except for English. ...
  5. Malicious chatbots don't really want to chat. ...
  6. Trust the chat, not the image. ...
  7. Common sense.
Mar 7, 2021


1. Virtual Workshop - Algorithmic Biases on dating apps and beyond
(Consumer Reports Advocacy)
2. Types of Men and Women that use Tinder - Jordan Peterson
(Dose of Truth)
3. Scientific Matchmaking
(Psychology In Seattle)
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