This document discusses a techno-scientific approach to understanding how users come to trust AI-generated deepfake content. It proposes studying deepfakes from a system perspective, looking at the network of relationships between users, AI systems, and their environments, rather than focusing only on the technical aspects of deepfakes or user characteristics. This approach sees knowledge as produced through the partnership between humans and technologies. It aims to provide a more holistic understanding of deepfakes in order to inform policy responses that address their sociotechnical dimensions rather than just their technical aspects.

1. Trusting AI-
generated
content: a
techno-scientific
approach
Federica Russo
Philosophy & ILLC, UvA | SOLARIS – Project Coordinator
@federicarusso | @solaris_eu

2. New wine and
new bottles?
• Good news spread fast, but deepfakes spread even faster
• The power of generative AI
• (re)produce video, audio, and textual contents
• Lots of questions on the table:
• Threatening democratic processes?
• Strengthening democratic engagement?
• Change education objectives and assessment?
• Challenge authorship regulation in art and academia
• …
2

3. Understanding
deepfakes
• Why users ‘trust’ deepfakes, from a system
perspective
• How do deepfakes ‘travel’ on social media?
• What is the appropriate level of intervention to
diminish the effects of infodemics?
• What is the good use of deepfakes?
3

4. Methodology and approach
4

5. A system
perspective
on
deepfakes

6. Deepfakes are not all alike …

7. The
network

8. What makes this
approach ‘techno-
scientific’?

9. Poiêsis:
the
partnership
between
humans and
artefacts
• Philosophy of techno-science: a space to
reflect on how humans and artefacts act
together, and in given environments
• In techno-scientific contexts proper:
• How the production of knowledge is not exclusive
prerogative of human epistemic agents.
• We produce knowledge together with instruments
(analogue and digital)
• In the study of deepfakes:
• How trust in AI-generated content depends not on
the quality of the deepfake only or on characteristics
of users only but on a network of relations between
different actants

10. Why does it matter?

11. From philosophy to policy
• Deepfakes are not just a technological
problem
• This is a pernicious reduction that will make
policy narrowly focus on the technicalities of
deepfakes production and spreading
• A system-level perspective may indicate
different leverage points for different types
of policies, or regulation, or campaigns, or
other types of action

12. Trusting AI-
generated
content: a
techno-scientific
approach
Federica Russo
Philosophy & ILLC, UvA | SOLARIS – Project Coordinator
@federicarusso | @solaris_eu
Thanks to Powerpoint Designer for some design ideas 
And to my SOLARIS colleagues for taking poiêsis from theory to practice