planetary health, data science, ethics, research strategy, ICT

1. Planetary Health and Data Science:
Prolegomenon to a research
strategy (or a meander through some questions)
Christopher Brewster
25 February 2021

2. Outline
• Motivation
• What is planetary health? The ill-health
of our planet
• What is the relevance to data science?
• Unintended impacts and digital
agriculture
• An invitation for further exploration

3. Motivation
• To make sense of the variety of
research projects I am involved
in.
• To develop a story (or a vision,
or strategy), some account of
why and how.
• A conceptual model
• A set of criteria

4. Planetary health as a concept
• Term popularized by initiative of The Lancet journal with
Rockefeller Institute 2014-2015.
- “human health and human civilisation depend on flourishing
natural systems and the wise stewardship of those natural
systems. However, natural systems are being degraded to an
extent unprecedented in human history.
• Spawned lots of initiatives including a journal.
• Convenient term for bringing together the dependence
of human wellbeing on a broad set of environmental and
social issues.
• The term “planetary health” come from the medical
world but there is no reason not redefine them in a less
anthropocentric manner

5. Planetary health vs. One Health vs. Ecohealth
• In fact there are several such top level
concepts -
• One Health – stressed connection
between human, animal and
environmental health. Especially
focused on zoonotic diseases.
• Ecohealth – supposedly focused on
biodiversity
• Planetary health – more
anthropocentric, ecosystems in terms of
human health.
• These are only issues of emphasis
Lerner
and
Berg
https://www.frontiersin.org/articles/10.3389/fvets.2017.00163/full
Rabinovitz
et
al.
https://gh.bmj.com/content/3/5/e001137

6. The Ill-Health of the Planet
• All trends (nearly) for
planetary health are
catastrophic
• Population increases
• GHG emissions and mean
temperatures
• Biodiversity collapse
• … and many more
Global surface temperature records 1850-2019s
European grassland butterflies

7. Relevance to Data Science
• Data science is an interdisciplinary
field, combining parts of computer
science (especially AI), parts of
mathematics (especially statistics),
and various social sciences and
humanities.
• Data science and its tools are key to
understanding the world
- From satellite-based monitoring of oceans,
arctic regions, and forests to
- Counting butterflies and birds to
- To climate modelling (all the way back to the
“Limits to growth” model of 1972)
• BUT it is also part of the problem

8. Unintended Impacts

9. Problematic aspects of DS and ICT - 1
• Tangible and obvious:
- Energy usage of ICT manufacture and usage.
- Slavery and child labour in the supply chain
(conflict minerals – tungsten, tin, tantalum and
gold)
- E-waste and toxic chemicals in devices
- Used to go to China, now moving to Africa
- Energy usage of data centres (205 TWh in
2018, or 1% of global electricity)
- Technological innovation driving ICT
replacement – including new data science
demands and capabilities

10. Problematic aspects of DS and ICT - 2
• More intangible: Social, ethical and
philosophical impacts (some examples)
- Amazon – using recommender systems and Amazon
prime for instant gratification
- Google – enabling total “information awareness” via
internet search, Google Mail, and Youtube (see Edward
Snowden). Privatisation of personal activity.
- Facebook – “surveillance capitalism” at its finest.
- The filter bubble effect and the enabling/provocation
social divisions and hatreds (cf. Rohinga crisis in
Myanmar and Facebook)
"The best minds of my
generation are
thinking about how to
make people click
ads."
-Jeff Hammerbacher

11. Example: Digital Agriculture
• My research concerns extending,
improving, designing systems for
digital agriculture
- Precision agriculture
- Supply chain tracking and tracing
- Data sharing in the agrifood system
• How does this impact Planetary
Health?
- Very hard to predict … or is it?

12. Digital Agriculture - 2
• Deskilling:
- Automation
• Reskilling:
- Education
- Sharing knowledge and
experience
• Efficiency
- Optimisation/JIT
- Resilience
https://www.robotics.org/blog-article.cfm/Agricultural-Robots-Understanding-Professional-Service-Robots-in-Agriculture/151
https://www.bloomberg.com/news/articles/2020-11-27/brexit-britain-s-food-supply-is-imperiled-by-christmas-and-covid

13. “It is difficult to get a man to understand
something, when his salary depends on his not
understanding it.”
- Upton Sinclair

14. Possible negative consequences of Digital Agrifood
• Instrumentation of farms leads to extensive available data
• Access, ownership, control of that data becomes more valuable than
food produced
• This leads either:
- “Enslavement” of farmers to digital control (see Uber etc.), or
- Far greater farm consolidation (in an already consolidating process)
• Consequently
- More monoculture – optimization of production for remote owners
- More loss of rural employment
- Greater loss of biodiversity – who cares, we have it all on Youtube or video
games

15. Need for Criteria: SDGs
https://sdgs.un.org/goals

16. Can we model potential impacts of our technological
innovations?
• Can we construct complex models that allow us
to explore the impact (intended and
unintended) of changes and innovations?
• Knowledge graphs? Agent based simulations?
• For example: Can we model the impact of
eating a shrimp here on a mangrove forest in
Thailand?
• Long distance impacts in time and space, across
society and the environment.
• Can we or should we use such tools to make
strategic decisions? To create or not to create?
Talos – Hephaistos’s robot

18. Conclusions and an Invitation
• Planetary Health provides a “way of seeing”.
• Planetary Health provides important technical
challenges in terms of modelling the world.
• Planetary Health provides potentially a framework for
considering the ethical, social and above all
environmental implications of our technological
initiatives.
• For IDS, an open invitation:
- Perhaps it is another way of telling our story.
- If people are interested we could explore further to
develop a “Planetary health and Data Science vision and
toolkit”

19. Questions/Reactions

20. References
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