This document outlines a research project exploring how digital technologies and an informational perspective can inform our understanding of science. It argues that technology now plays an active role in constructing scientific knowledge through technoscientific practices. The project will describe how science is changing with technology and revisit classic philosophy of science concepts through this lens. It uses the example of exposure science and biomarkers research to illustrate how an informational approach can provide new insights into ontological and epistemological questions about what entities exist and how knowledge is produced in technoscientific fields.

1. Towards an informational turn
in philosophy of science?
Federica Russo
Philosophy | Humanities | Amsterdam
russofederica.wordpress.com | @federicarusso

2. Overview
The pervasiveness of technology
Digital technologies and the philosophy of information
Philosophy of information and philosophy of science
Techno-scientific practices
An example: exposure science and biomarkers research
Ontological and epistemological questions
How to revisit classic philsci concepts with PI
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3. THE PERVASIVENESS
OF TECHNOLOGY
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4. 4

5. 5

6. Artefacts, instruments
From history of technology
Machines to help us domesticate nature,
Artefacts to boost / amplify our capacities
From history of science
The scientific revolution was also a technological revolution
The tradition of the workshop and the role of instruments in
scientific method
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7. DIGITAL TECHNOLOGIES
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8. The digital revolution
The place of human beings keeps changing:
We are not
at the center of the universe (Copernicus)
at the center of nature (Darwin)
transparent to ourselves (Freud)
the only intelligent being (Turing)
[Floridi, The 4th Revolution]
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9. An information
revolution
A revolution in the information
process
From pre- to hyper-history
Pre-history: information is not
recorded
History: information is
recorded, processed
ICTs transform the whole
information process
The amount of transmitted
information
How information is transmitted
9
[Pics: Floridi, The 4th Revolution]

10. Basic vocabulary of PI
Information as:
Resource: we use it, e.g.: to make decisions; Product: we use it to
generate further information, and then used to modify it … i.e.:
information is a target
[Formal definition of information does not interest us here]
Inforgs: informational organisms
We, intelligent humans; intelligent engineered artefacts
Infosphere: informational environment
The whole space of possible information, including Nature.
Physis: nature, reality
Techne: practical science and creation of artefacts
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11. Basic methodology of PI
The Method of Levels of Abstraction
A familiar idea to computer scientists.
A useful method to tackle conceptual problems too
How we handle information processes
A system
A model of the system
A collection of variables, each having a well-defined possible set of values
or outcomes
A LoA qualifies the level at which a system is considered
Specify what are the relevant variables
Formalise the model (qualitative or quantitative terms)
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12. Why going LoA in philosophy?
Clarify implicit assumptions
Facilitate comparison
Enhances rigour
Promotes resolution of possible conceptual
confusions
[Floridi, The ethics of information, ch.3]
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13. TECHNO-SCIENTIFIC PRACTICES
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14. The digital revolution:
from everyday life to scientific practice
The 4th revolution in Floridi’s philosophy:
We, humans, are inforgs in the infosphere
A change in the interaction with the external world and with ourselves
Tension between physis and techne is revitalised
Rethinking ethical agents as homo poieticus
We can now address ethical challenges in e.g. computer ethics, digital divide,
right to be forgotten, …
The 4th revolution in my project:
How does science change?
Describe scientific practices
How does our understanding of science change?
(Re)investigate ontology and epistemology of science
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15. Why practices
Traditional, Anglo-American PhilSci:
Attention to the ‘end-products’: theories, entities, …
Largely value-free, interested in context of justification
Philosophy of science in practice
Attention to how we make science
The making in the labs and institutions, value-laden,
including the context of discovery
The interactions between different sorts of epistemic agents
(human, artificial, hybrid)
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16. Why techno-scientific
The practice of science is highly technologised
The question is not who comes first
Technology first >< Science first
The interesting question is
How does our understanding of science changes? [Once the
technological component is appropriately examined]
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17. Applying LoA
Absolute questions
• What is science?
• What is knowledge?
• What is reality?
• Will technology take over
humans?
Questions at given LoAs
• How is knowledge production
to be characterised in techno-
scientific practices?
• What epistemic access to
reality is mediated / produced
by technology
• What epistemic role does
technology have in science?
• What practical role does
technology have in daily life?
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18. EXPOSURE SCIENCE AND
BIOMARKERS RESEARCH
An example
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19. From epidemiology to
molecular epidemiology
Environmental exposure and disease
(How) do {air, water, chemicals, …} cause {cancer, asthma, allergies, ...}?
Traditional epidemiology
Establish correlation between classes of environmental factors and of
disease
Molecular epidemiology
Measurements at molecular level
Identify biomarkers of exposure >> of early clinical changes >> of disease
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20. Exposure causes Disease
Measure chemicals in water, air, etc
Identify biomarkers of exposure
Detect biomarkers of early clinical
changes
Match with biomarkers of disease 20
Make categories
of environmental
factors
Match with
categories of
disease

21. Data- and technology-driven
Big data helps with the generation of evidence of
correlation (statistical analyses) and of mechanisms
(omics analyses)
Specifically, technology is central to: generating,
processing, storing / archiving, curating, analysing
data
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22. Data collection,
storage, generation
Sensors, smartphones, GPS
Biobanks
Omic technologies
Liquid chromatography
Mass spectometry
Nuclear magnetic
resonance spectroscopy
…
Statistics softwares
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23. Reformulated questions
How does technology change the
practice of epidemiology?
How does technology change the
conceptualisation of, e.g.:
Data / Observation / Experience
Evidence
Causality
Knowledge
…
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24. An example ad hoc?

25. Think of
High-energy physics
Space research
Social media analysis
Digital humanities
…
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26. ONTOLOGICAL QUESTIONS
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27. Ontology, but not armchair
What there is:
not an abstract, absolute question
Here:
Do ICTs bear any changes to ‘what there is’?
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28. Re-ontologising the real: a PI perspective
ICTs create new ontological spaces
Compare:
Dishwashers vs smartphones
Mimeograph vs smartprinting
Onlife presence vs locality
In a techno-scientific context
New understanding of old concepts
What is an entity?
Do entities really exist??
It depends on the LoA
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29. Again on biomarkers research
Are biomarkers tiny tiny entities?

30. 30
Traditions
of
Entity
Hunting
Aetiological
standpoint /
infectious disease
model / search
for pathogens
Mechanistic
explanation /
causally relevant
entities
Analytic
metaphysics /
Language and
ontology / focus
on particulars

31. Picking up entities?
While classical statistical models to analyzing -omics data serve the
purpose of identifying signals and separating them
from noise, little has been done in chronic diseases to model
time into the exposure-biomarker-disease continuum.
[Vineis and Chadeau-Hyam 2011]
From these two parallel analyses [statistical analyses], we obtained
lists of putative markers of (i) the disease outcome, and (ii)
exposure. These were compared in a second step in order to
identify possible intersecting signals, therefore defining
potential intermediate biomarkers.
[Chadeau-Hyam et al 2011]
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We pick up signal!

32. Early theorisations of biomarkers
A must read
Molecular epidemiologist Paul Schulte (1993)
Biomarkers
Biological markers: ‘’technologically powerful measures of
biological variables’’
Indicate events in the continuum [E----D] at different levels
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33. What do biomarkers stand for?
It is vital to understand the nature of the relationship between the
mark and the event
“Does the marker represent an event, is it an event itself, is it a
correlate of the event, or is it a predictor of the event?
The answers to these questions may affect who is sampled, how
and when they are sampled, and what confounders or effect
modifiers are considered. […] Thus, a biologic marker often
refers to the use made of a piece of biologic
information rather than to a specific type of
information.”
Schulte, 1993, p.14-15.
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34. Process-based disease causation?
Biomarkers are not causes
They are not even markers for causes, per se
Biomarkers mark salient points of a process (=
disease) to be understood in causal terms
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35. Process ontologies
Latest insights from phil biology
To say what an individual is we must look at processes,
rather than entities
Dupré, Pradeau (and Guay), Seibt, …
We can conceive as disease as a (causal) process
Sharp distinction between normal and pathological
collapses
[Yes, we may also create new ‘normal’ with biomarkers
of health …]
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36. Undecided after ontological
arguments?
Look at the epistemology

37. EPISTEMOLOGICAL QUESTIONS
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38. Epistemology, from science in practice
Again, start from the practice of science, then
raise epistemological questions.
Here: How do we know? How do we produce
knowledge?
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39. Techno-science
What is the technology doing?
Not just allowing us to
See the smaller or see the bigger
Analyse / store / transmit data
BUT mainly:
Taking active part in constructing knowledge
We and the machines, together
Technology has a poietic charachter
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40. Why poiesis?
Knowledge is not just representational
Mimesis: we re-produce what there is
[Admittedly, we also express it in natural language, but it is not to be
reduced to it]
Knowledge is produced
Who/What produces knowledge?
Epistemic Agents: human and artificial
Knowledge becomes embodied, distributed, connected, …
Both human and artificial epistemic agents
have poietic characther
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41. TO SUM UP AND CONCLUDE
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42. Hard facts
Technology is pervasive
In everyday life
In science
Much has been written on how technology
affects the personal, societal, and political
sphere
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43. A research project
How does technology change science?
How does technology change our understanding
of science?
Take the epistemic role of technology seriously
Develop a concept of techno-scientific practices
Describe the changes in science
Revisit classic PhilSci concepts
Explore new conceptual spaces
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44. Why going informational?
A methodology – LoA
Justify ‘chopping up’ the big, absolute phil questions
A conceptual framework
Infosphere, inforg, poiesis, ..
A vocabulary to
account for techno-scientific practices
(re)design concepts
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45. Will I succeed? Watch my space!
http://russofederica.wordpress.com | @federicarusso
Techno-Scientific Practices:
An Informational Approach
Under contract with Rowman & Littlefield