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by Lucía Caballero
International Coordinator
June 08, 2026
from
TheConversation Website

DC Studio
When I talk to my son, an engineering student, and we have a
question or disagreement, he immediately turns to
ChatGPT as his primary source of
information and confirmation.
He is not alone in this.
The use of
generative A.I. tools has
exploded across different demographic groups. For many people, these
tools can be entertaining, informative and beneficial.
However, they also have a dark side.
Generative A.I.
is not formally recognized as addictive right now - the
medical evidence is still being gathered.
But there is a significant amount of data showing
heavy use of chatbots and other systems that produce text,
images and video leads to neural patterns
and behaviour that are associated
with addiction.
In light of Meta's and YouTube's
recent legal defeat in a landmark
social media addiction trial, I
believe it's time to ask whether a similar logic applies to
generative A.I. - and how it could be addressed.
The starting point would be to identify who
carries responsibility for overuse of generative A.I..
The science on this is not settled, and there are some
who counsel caution when using the
term addiction. They propose the use of other expressions
such as "problematic use".
However, in
a recent paper, our team of
researchers suggest there is strong evidence to suggest that
generative A.I. has addictive properties.
Much-discussed examples include,
emotional dependency on chatbot companions,
compulsive engagement with them, and
the loss of real-world
acquaintances and friends.
A key factor here is that, as in all cases of
addiction, the behavior has negative consequences for the user which
may affect both their personal and professional lives.
If we follow the argument that generative A.I. is a candidate for
addictive behavior, then we also need to look at responsibility.
Societies tend to find ways to deal with harm
by holding people or groups responsible for fixing it.
Those who could be accountable include
legislators, regulators, industry and health systems.
Historical Examples
Historical precedents such as smoking might offer
insights into how the area of generative A.I. addiction could
evolve.
Older readers may remember when the Marlboro Man would appear
before any feature movie in their local cinemas. It eventually
transpired that not only was smoking addictive and bad for your
health, but that tobacco companies
knew this.
Nevertheless, it was
publicly denied.
This led to lengthy and high-profile litigation, eventually
resulting in large-scale financial payouts and
changes to the industry.
These changes included the plain packaging of
tobacco products and gruesome warning labels on them.
Gambling could be following a
similar trajectory - and now social media companies may be taking
their first steps into a similar process.
A key question is,
whether the makers of a product - be it
tobacco, gambling or social media - are aware of its
addictive properties...
Another important factor being considered is
whether certain companies may even use the allegedly addictive
properties of their products for corporate advantage.
A.I. is not tobacco, of course, but there may be parallels to be
studied.
In
our research, we have identified
four groups of stakeholders that are now being called
upon to address the challenges linked to the possibility of
addiction to generative A.I.
- The first is governments and regulators.
These have a key role to play in highlighting
the problems, setting the rules of engagement, and creating
incentives for other parties to engage with the topic.
They can do this by requiring labeling, restricting advertising,
applying liability law and providing research funding - along
with many other mechanisms.
- But the most important role in addressing potential addictive
behavior associated with generative A.I. would be held by big
tech companies that develop and own these technologies - and
stand to benefit financially from them.
These companies own and have access to user data, which would be
needed to ascertain the features that support or alleviate
addiction.
They are also the parties that would benefit
financially from addiction by increasing user numbers and
engagement, the main currency of the digital age.
- In addition to these two groups, academic researchers
have an important role in collecting and interpreting data, and
providing the evidence needed to recognize addiction and
addictive features - in ways that allow for evidence-based
political or legal debate.
- Finally, civil society organizations such as user or
patient groups can help by providing support, advocating for
members' interests, and establishing early-warning structures.
The point is that none of these interested
parties can address the problem on their own.
They need to collaborate...
Someone else's Problem
A key problem at the moment is the lack of structured debate about
responsibilities - everybody assumes it is someone else's problem.
But there is ample precedent showing how greater
engagement from those involved with the issue may be achieved.
With tobacco, the World Health Organization (WHO)
formed
the Framework Convention on Tobacco Control
- a treaty-based mechanism that brought together governments, public
health bodies, researchers and civil society to evaluate evidence
and draw up common rules.
The International A.I. Safety Report
shows comparable international consensus-building activities are
already happening in other aspects of A.I.
Some responsibility also falls on the users of A.I., who
should try to avoid or control their own potentially harmful
behavior.
But appeals to individual moderation or
mindfulness have been shown with other addictions to be
insufficient.
While the harms associated with smoking or alcohol misuse are
well known, society still relies on age limits,
packaging rules and advertising restrictions.
Generative A.I. is being integrated into
the everyday fabric of our society.
The choices we now make will determine what
acceptable use looks like for years to come...
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