Can we trust synthetic data? Key insights from the June data.europa
academy webinar
Can we trust data that was never directly collected from the real
world? This was the central question explored during the data.europa
academy [https://data.europa.eu/en/academy] webinar on Friday, 5 June
2026 titled ‘Responsible use of AI‑generated and synthetic data in
research'. The session, part of the series “Open data, academia, and
ethics”, introduced participants to the growing role of
AI‑generated and synthetic data in research. For those less familiar
with the topic, synthetic data refers to data that is artificially
created, often to overcome challenges such as limited availability or
privacy concerns, while still aiming to resemble real-world
patterns. 

The webinar provided both a technical and a critical perspective on
this evolving field. In the first presentation, Thomas Lampert
explained how machine learning models are trained on data, ranging
from images to text, and how these models can generate new, synthetic
outputs. Through simple examples, such as distinguishing between
apples and bananas, he illustrated how algorithms learn patterns from
existing data and reproduce results rather than the underlying human
reasoning process. The session also explored how generative models,
including systems like ChatGPT, are capable of creating entirely new
content based on learned patterns, raising important questions about
how such data should be interpreted and used.  

The second presentation shifted the focus to the broader implications
for research and society. Ella Hafermalz highlighted that data has
always been “constructed” to some extent, encouraging participants
to reflect on where the boundary lies between real and synthetic data.
She introduced the idea of a ‘synthetic threshold’, the point at
which data can no longer be considered purely real and discussed three
forms of synthetic data use: legitimate and transparent applications,
fraudulent or misleading uses, and cases where human and AI-generated
data become intertwined. This perspective underscored the importance
of transparency, clear documentation, and ethical responsibility when
working with AI-generated content.  

Ultimately, the webinar demonstrated that while synthetic and
AI-generated data open up new possibilities for innovation, such as in
fields like medical imaging, they also come with risks, including
bias, misinterpretation, and reduced trust if not handled carefully.
As research practices continue to evolve, ensuring responsible and
transparent use of these technologies will be key to maintaining
credibility and public trust. Watch the full recording of the webinar
here
[https://www.youtube.com/watch?v=1Qka-OiViqM&list=PLT5rARDev_rknM6hDfssgSCS6aUxE70eF].
Interested in exploring more topics like this? The data.europa academy
[https://data.europa.eu/en/academy] organises monthly webinars
covering topics such as open data, AI, and digital transformation.
Visit the events page [https://data.europa.eu/en/news-events/events]
to stay up to date with upcoming sessions and access past recordings.

Publication Date/Time
2026-07-03T08:00:00+00:00
Exploring how AI‑generated and synthetic data are reshaping
research, ethics, and trust