Environment & Energy (May 10th 2019) Data Driven Innovation 2019. Dipartimento di ingegneria, Università degli Studi Roma Tre.

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Making citizens the eyes of policy makers:
a sweet spot for hybrid AI?
Daniele Miorandi
U-Hopper

2. © All rights reserved, U-Hopper 2019
Environmental governance
• Policy- and decision-makers
need data to take informed
actions
• Authoritative sources may be
expensive to obtain (and hard
of reach in developing
countries)
• Limited awareness and
engagement of citizens hinder
the impact of the policies/
actions
The context

3. Can we involve citizens to provide data
useful for environmental governance?

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Tell me, I’ll forget
Show me, I’ll remember
Involve me, I’ll understand
Why involving citizens?

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Use (better: leverage) citizens as sensors
• Many potential sensors!
• Sensors that move (as people do) - finer
spatial granularity
• Finer temporal granularity
• Free (well, kind of)
Side benefits

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• Crowdsourcing/crowdsensing as an established
research field
• Rather large body of literature
• “Crowdsensing” returns 11,400 results on Google
Scholar
• Some well-known success stories
• Related keyword: citizens science
Is this really new?

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Citizens' Observatories are community-based
environmental monitoring and information systems.
They build on innovative and novel Earth
observation applications embedded in portable or
mobile personal devices. This means that citizens
can help and be engaged in observing our
environment.
An EU perspective

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Laypeople using geiger meters
to measure radioactivity in the
Fukushima aftermath
Over 50 million data points
collected to date
Example: Safecast

9. Citizens
Portable sensors
Mobile personal
devices
Citizens
Observatory
Citizen-generated
data

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Citizen-generated data has
quality issues
• Noisy (low-cost
sensors, non
professional people,
etc.)
• Unreliable (no control
on people, connectivity
issues etc.)
Data quality

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Traditionally, policy and
decision-maker in
environment have been
working with authoritative
data sources
• Fully trusted data of high
quality
• High availability
Working with noisy, unreliable data

12. can AI help?

13. NO, not this AI

14. The ‘humble’ AI

15. Citizens
Portable sensors
Mobile personal
devices
Citizens
Observatory
Citizen-generated
data
AI
AI-annotated data

16. Is AI fit for purpose?

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Nothing can (yet) replace human intelligence
at some tasks
Can we use people to help AI when AI is not
good enough?
The missing piece: you

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“Science which assists the
needs and concerns of
citizens” and “a form of science
developed and enacted by the
citizens themselves”. (Alan
Irwin)
Citizen science

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A large community of volunteers supporting various citizen science
projects across very diverse fields, such as space (astronomy),
biology (biodiversity), geography etc,
Typical task: image annotation
Data:
• 1,765,830 registered volunteers
• 422,416,250 annotations generated
Example: Zooniverse

20. Citizens
(in situ)
Portable sensors
Mobile personal devices
Citizens
Observatory
Citizen-generated
data
AI
Data Quality
Engine
AI-annotated data
Citizens
(remote/online)
Data to
annotate
Annotations
Citizen-generated data
with validated annotations

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A large-scale initiative to demonstrate the viability of using citizen-
generated information in river monitoring and flood prevention.
SCENT engages citizens in environmental monitoring and enables them to
become the ‘eyes’ of the policy makers.
Focus on monitoring land-cover/use changes using citizens smartphones
and tablets.
Funded by EC within the H2020 framework
The SCENT project
@SCENT_EU
scent-project.eu

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Two large-scale pilots (Danube delta in Romania and
Kifisos basin in Greece)
Collected so far:
• 13,939 citizen-generated images
• 2,132 citizen-generated videos
SCENT: pilots

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SCENT context
challenging for AI
Need fine-grained classifications
River bank with bare soil? With low grass? With high grass?
Trees vs fruit trees?
…

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The creator signalled the presence of a water level meter
The AI identified a bank river with low grass
Both got invalidated in the final human intelligence stage
Into the wild…

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What AI said:
• Fruit tree (99% confidence —> Passed)
• Tree (92% confidence —> Passed)
• River bank.Bare soil (74% confidence —> Validated)
• Pasture/Grassland (49% confidence —> Invalidated)
• River bank.Low grass (48% confidence —> Invalidated)
More from the wild

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Image creator signalled the presence of a water level
indicator
Confirmed by the AI
But.. AI computed 2.0m depth :(
Even more from the wild

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Next step: use citizen-generated data to enrich data
from Earth observations (e.g., Sentinel)

28. Citizens
(in situ)
Portable sensors
Mobile personal devices
Citizens
Observatory
Citizen-generated
data
AI
Data Quality
Engine
AI-annotated data
Citizens
(remote/online)
Data to
annotate
Annotations
Citizen-generated data
with validated
annotations
Harmonisation
platform EOs data
Data for
policy- and
decision-makers

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Hybrid AI: artificial intelligence + collective intelligence as an
approach for solving complex, wicked problems
A lot of space for application in environmental monitoring/
environmental governance
Do not reinvent the wheel
• Protocols for user engagement
• Software components (open source/Apache v.2)
• Existing citizen science communities to tap in
Summing it up

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www.u-hopper.com
Via Sanseverino 95
38122 - Trento, Italy
Contacts
Daniele Miorandi
daniele.miorandi@u-hopper.com
CEO
+39 347 5865883
Get in touch if you want to run a project
We can help!

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Additional material

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Extremely hard for AI
Teams better than individuals at
tackling them
The level of diversity correlates with
the effectiveness of solutions
Wicked problems