By
Stuart Kerr, Technology Correspondent, LiveAIWire

Human beings have been attempting to engineer romantic connection
since at least the invention of the matchmaker, and the structural logic of
the endeavour has not changed much since then: gather information about
individuals, identify compatible pairs, facilitate introduction. What has
changed, dramatically, is the scale at which this can now be attempted and
the degree to which the process has been automated. The global online dating
market generates revenues exceeding 9 billion dollars annually, and
artificial intelligence is central to how every major platform in the sector
functions.

How Matching Algorithms Actually Work

Dating platform algorithms are proprietary and their precise
mechanisms are not publicly disclosed, but the research literature on
recommender systems allows a reasonably informed account of their general
structure. Modern dating algorithm systems use collaborative filtering
techniques related to those used in Netflix recommendations and Spotify’s
Discover Weekly, identifying patterns of expressed preference in large user
populations to predict which profiles a given user is likely to engage with
positively. Behavioural signals play a central role: how long you spend
looking at a profile, whether you read the full bio or just swipe on the
photograph, how quickly you respond to messages, and what kinds of messages
you send all influence how the algorithm treats your profile and shapes your
feed. Tinder’s internal documentation, disclosed in litigation, revealed the
existence of a desirability scoring system that influenced match quality and
profile visibility.

The Gamification of Human Connection

Dating platforms are designed to maximise engagement, and this
design objective is not identical to the objective of helping users find
meaningful relationships. A platform that successfully connected all its
users into lasting relationships would rapidly eliminate its own user base.
Swipe mechanics borrowed from slot machine psychology, variable reward
schedules, and notification patterns designed to pull users back to the app
are features whose design reflects conversion rate optimisation rather than
user wellbeing. Research published in the Proceedings of the National Academy
of Sciences found that couples who met through online dating reported
relationship quality similar to those who met through traditional channels,
but the search experience itself is often experienced as dispiriting,
addictive, and disconnected from how people actually present themselves and
what they want from relationships.

What This Means for You

If you use dating apps, the experience you are having is shaped by
algorithms whose objectives are not fully aligned with yours. The profiles
you see are not a random or comprehensive sample of the available population;
they are a curated selection shaped by your prior behaviour and the
platform’s commercial incentives. If online dating is not working as well as
you hoped, the experience is partly a data point about algorithm design and
platform incentives, not necessarily an accurate reflection of your
desirability or the compatibility of available partners. As LiveAIWire has
covered in analysis of AI
and romance fraud, the dating app environment is also a vector for
sophisticated fraud operations that exploit the same engagement mechanics
platforms use to retain genuine users.

AI Companions: Beyond Matching to Relationship

A distinct and rapidly growing category goes beyond matching to
provide the relationship itself: AI companion applications including Replika,
Character.AI, and Pi have accumulated tens of millions of users, many of whom
report forming genuine emotional attachments to their AI companions. The
therapeutic potential for people experiencing severe isolation, social
anxiety, or grief has attracted genuine interest from mental health
researchers. The risks of fostering dependency, replacing rather than
supplementing human connection, and exposing vulnerable users to platforms
whose commercial interests may conflict with their psychological wellbeing
are equally real. As LiveAIWire has examined in coverage of the
labour behind AI systems, AI companion products depend on data
annotation and safety review infrastructure whose privacy implications for
intimate user data are significant and not always adequately
disclosed.

Compatibility Science: What the Research Actually
Says

Research by psychologists including Eli Finkel at Northwestern
University has examined whether algorithmic matching on the basis of
personality traits and stated preferences improves relationship outcomes. The
meta-analysis
published in Psychological Science in the Public Interest concluded
that while online dating has real advantages in enabling access to a large
pool of potential partners, the claim that algorithmic matching improves
relationship quality relative to self-selection is not well-supported by evidence.
Self-reported preference data is a poor predictor of actual romantic
attraction, which is heavily influenced by factors that emerge from
interaction rather than from profile characteristics.

The Future of AI-Mediated Romance

The trajectory of AI in romantic matching and companionship is
toward greater personalisation, more sophisticated behavioural modelling, and
increasingly convincing simulations of human emotional response. Newer
entrants including Hinge have explicitly incorporated relationship success
metrics into their product design philosophy, arguing that a reputation for
producing good relationships is a durable competitive advantage. Whether this
philosophy survives scale and investor pressure remains to be seen. As
LiveAIWire has explored in coverage of AI
systems that shape consequential life decisions, the transparency and
accountability questions that apply to AI in professional domains apply
equally to AI that mediates our most personal ones. The black-box nature of
matching algorithms, and the absence of meaningful user agency over systems
that govern romantic prospects, is a governance gap attracting increasing
regulatory attention in the EU and UK.

Transparency and User Agency: The Regulatory
Opportunity

The most tractable near-term regulatory intervention in dating
platform AI is transparency: requiring platforms to disclose the key factors
that influence profile visibility and match presentation, and to provide
users with meaningful information about how their data is used to shape their
experience. This falls short of full algorithmic disclosure, which platforms
would resist on commercial confidentiality grounds and which would in any
case be of limited utility to most users, but it establishes a baseline of
informed consent that currently does not exist.

The EU Digital Services Act, which applies to large online
platforms including major dating services, includes transparency requirements
for recommender systems that will require platforms to explain the main
parameters determining which content is presented to which users, and to
offer users the option of a recommender system not based on profiling. The
practical implementation of this requirement in the dating context is still
being developed, but it represents a meaningful shift in the regulatory
approach to algorithmic transparency.

User agency is the complementary concern. Even well-informed users
have limited ability to influence the algorithmic systems that govern their
dating experience, beyond the behavioural signals they generate through app
use. Providing users with genuine control over the parameters that shape
their match presentation, beyond the blunt instruments of stated preferences
that most platforms offer, would require platforms to make design choices
that potentially conflict with engagement maximisation. The regulatory and
competitive pressures that might produce those choices are developing slowly,
but the direction of travel in both European and UK digital regulation is
toward greater user rights over algorithmic systems, which may eventually
create meaningful leverage for dating app users seeking more transparent and
controllable matching experiences.

Can Machines Understand the Heart? An Honest Assessment

The question posed in the title of this article deserves a direct
answer. Current AI systems do not understand the heart in any meaningful
sense. They can model patterns of human romantic behaviour at scale, identify
statistical regularities in expressed preferences and engagement signals, and
optimise for outcomes that are measurable at population level. What they
cannot do is grasp the particular chemistry of two specific people, the
significance of a shared moment, the way that attraction develops through
accumulated small interactions, or the difference between a relationship that
works on paper and one that works in life.

The honest answer is that AI can improve the logistics of meeting
potential partners and can, at the margins, filter out the most obvious
mismatches. It cannot substitute for the irreducibly human process of getting
to know another person, and platforms that present algorithmic matching as a
more fundamental solution than it is are, in a meaningful sense, misleading
their users. The American
Psychological Association’s research on attraction and
compatibility consistently finds that the factors that matter most
in successful long-term relationships are not well-captured by the profile
data that algorithms work with.

This is not an argument against online dating, which has genuine
advantages in expanding the pool of potential partners accessible to people
whose social networks would not otherwise expose them to compatible matches.
It is an argument for realistic expectations about what algorithms can and
cannot contribute to the deeply human project of finding
love.

About the Author

Stuart Kerr is the Technology Correspondent at LiveAIWire,
covering artificial intelligence across society, policy, and industry. About
LiveAIWire.