Presentation discussing an experiment with computer vision tagging of collection images. It looks at the appeal of AI for museums with limited resources, and how the frequent errors produced by this technology suggest that we may be better placed to consider AI a provocation rather than a solution. It concludes by noting that hybrid human-AI approaches to documentation could be used to offset some of the cognitive biases bring to museum cataloguing, but only if we move beyond the solutionist rhetoric through which AI is frequently promoted.

1. Error or
Serendipity?
Kevin Bacon,
Digital Manager
A small experiment with AI
at Royal Pavilion & Museums
Digital Collecting Practices 2019

2. Royal Pavilion & Museums is…
• Royal Pavilion
• Preston Manor
• Brighton Museum
• Hove Museum
• Booth Museum of
Natural History

4. Royal Pavilion & Museums is…
Collections include
World Art, Decorative Art, Natural Sciences, Fine Art,
Local History, Archaeology, Film & Media, Costume, Toys,
Musical Instruments, Royal Pavilion Archive

5. We are a Victorian
accident.
Wild eclecticism of our
collections is not
commensurate with our
resources.
How can we sustainably
make our collections
relevant to the digital
age?
The problem?

6. Collection Management Pressures
1. There will never be enough staff
2. Accreditation requirements prioritise basic cataloguing over
digitisation
3. Intellectual accessibility becomes marginalised

7. Is AI (computer vision) the solution?
1. Boost documentation output without threatening staff posts
2. Supports value of digitisation as an activity
3. Enables knowledge mining and improved intellectual
accessibility

8. AI & RPM: DAMS
• RPM uses Asset Bank
– both internal access
and online collections
• Sometimes used to
harvest data about
our collections that
can’t easily be
accommodated in our
Collection
Management System
• Auto-tagging tool
introduced in 2018
using Amazon
Rekognition

9. Brighton Town Hall as Parthenon in Ancient Egypt
It’s only 50% accurate…
… but is there serendipity in the error?
Tags: Art, Building, Architecture, Temple, Worship, Column,
Pillar, Parthenon, Shrine, Ancient Egypt, Ruins

10. RPM & AI: Gift
EU funded project led by
IT University of
Copenhagen
RPM one of 10 partner
museums in action
research module
Projects have focused on
playful and personalised
hybrid experiences
gifting.digital
Gift Experience by Blast Theory

11. The Experiment: Question
If AI provides an unreliable solution, might it
provide better use as a provocation?

12. Test whether AI driven
keyword tagging can inspire
better documentation.
Can an AI’s mistakes prove
helpful to humans who can
respond to the errors?
Instructions to participants:
1.Write description
2.Check and correct AI tags
3.Revisit and improve the description after correcting the tag
The Experiment: What we did

13. Discovery 1: Errors can lead to Enrichment
Tags: Animal, Mammal, Rodent, Rat
Mistakes can
prompt corrections:
rodent not
removed, but
replaced by
mustelid
Error begets new
data that may not
have been present
had the error not
existed

14. Discovery 2: Refocus Attention
Tags: Animal, Mammal, Rodent, Rat
Town Hall is not just
a Brighton
landmark, but an
example of an
architectural style
Identifies the small
detail: humans are
an important
element of this
print
Tags: Human, Person, Art, Building, Architecture, Drawing

15. Scientists
----------
Researchers,
like data
Artists
----------
Browsers,
looking for
inspiration
Discovery 3: Inspires Conversation
Experiment lead to
discussion about
multiplicity of
audiences for
different digital
assets
Possible tool for
driving audience
centred description

16. WYSIATI
‘What You See Is All
There Is’: a focus illusion
that blinds people to the
surrounding context of a
problem
Cognitive bias that
means humans have a
tendency to draw on
very limited information
when solving a problem
More likely to occur
when conducting
repetitive tasks like
documentation
Thinking systems:
• System 1: quick, without conscious control, often
relying on heuristics
• System 2: slow, conscious, using deliberative
reasoning

17. Conclusions / Provocations
Unwise to conclude much from a small and wilfully
unacademic experiment. But:
1. The rhetoric of AI in cultural heritage is overwhelmingly
solutionist, and this may be obscuring its short-term value
as a prompt or correction to human practice.
2. If the limitations of AI and human cognition can be
checked against each other, does this mean that museum
documentation can become a meaningful act of playful
dialogue?
3. How can we create workflows to encourage this hybrid
approach?

18. kevin.bacon@brighton-
hove.gov.uk
@fauxtoegrafik
brightonmuseums.org.uk
Thank you