The document discusses the relationship between philosophy of science and philosophy of technology. While traditionally viewed as separate disciplines, they may actually be investigating the same technoscientific object. New fields like exposomics that rely heavily on technology require reexamining philosophical assumptions. The document argues that technology has a "poietic character" in that it enables the production of data, phenomena, and knowledge, not just augmenting our capacities. This poietic view helps explain how causal knowledge is constructed in technoscience.

1. On the poietic character
of technology
Federica Russo
Universiteit van Amsterdam
russofederica.wordpress.com | @federicarusso

2. Overview
Phil Sci and Phil Tech
Parallel tracks to investigate the same object?
The case of EXPOsOMICS
How technology leads us to reformulating questions
Revisiting the relation between science and technology
Subordination
Poiesis
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3. PHIL SCI AND PHIL TECH:
ONE OBJECT, TWO DISCIPLINES?
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4. Two disciplines?
Different professional societies
Low permeable conferences
Specialised journals and publication venues
Few joint job descriptions and study programmes
…
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5. How many ‘objects’?
Discipline-specific questions for science and technology?
Techno-science: can we still separate them?
Examples abound
Data intensive science; lab experiments; even social science
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6. Towards a multifaceted discipline?
Society for Philosophy of Science in Practice
Journal: Philosophy and Technology
Symposia on Philosophy of Interdisciplinarity in various
conferences
…
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7. EXPOsOMICS
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8. The ‘exposome’
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9. From GWAS to EWAS
The limits of genomics
An expanded notion of exposure
Internal and external exposure – the exposome
Better understanding of disease mechanisms
The missing link: how environmental exposure and
disease are connected
Go down to the molecular level
Better prediction and health policy planning
Earlier and more accurate prediction
Identification of areas for public health intervention
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10. Environmental
exposure and disease
Traditional epidemiology
Establish correlation between
classes of environmental factors
and classes of disease
Molecular epidemiology
Measurement at molecular level
Identify biomarkers of exposure,
of early clinical changes, of
disease
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11. Measure chemicals in water, air, etc
Identify biomarkers of exposure
Detect biomarkers of early clinical changes
Match with
biomarkers of disease
11
Make
categories of
environmental
factors
Match with
categories of
disease
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12. Goals
Identify biomarkers at key stages of disease evolution
Match biomarkers to trace evolution of disease
(meeting-in-the-middle methodology)
Challenges
Understand link <environmental exposure—disease>
Handle unknowns of the system
Torture (big) data sets produced
Identify causal relations in spite of large interaction
effects
Reinterpret micro-links at macro-level
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13. Selected epistemological and
metaphysical questions
What evidence?
Mechanisms? Difference-making?
What account of causality?
Information? Capacities?
What ‘levels’?
Generic, single case? Marco, micro? Biological, social?
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14. Technology in
exposomics
Sensors, smartphones, GPS
Omic technologies
Liquid chromatography
Mass spectometry
Nuclear magnetic
resonance spectroscopy
…
Statistics softwares
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15. Technology—built in—science
Technology is present at every stage of the
scientific process
Data generation > Data collection > Data analysis
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16. Reformulating questions
How does technology change the
conceptualisation of
Data / Observation / Experience
Evidence
Causality
Knowledge
…
To answer (some of) these, we need to
revisit the relation between science
and technology
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17. SCIENCE⇋TECHNOLOGY
Subordination?
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18. Received views
Technology ancillary to science
A heir of the Greek distinction between techne and episteme
A demarcation problem
Science and technology are distinct, and
Science instrumental to creation of technology
[Arageorgis & Baltas, Agassi in the ‘80s]
Technology instrumental to science
Embodiment of science in technology
Instrumental realism
[Hacking, Ihde]
Shift object! From reality to knowledge
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19. SCIENCE⥊TECHNOLOGY
Poiesis
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20. Detecting signal
Data-driven research
First: produce massive data sets
Then: detect signals in the data
Detect signal to
Establishing meaningful correlations in the data
Tracing information (evolution of biomarkers)
Establishing causal links (between environmental factors and
diseases)
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21. Traditional epistemological categories
don’t hold out
Beyond observable / unobservable
We produce data
Beyond traditional (and single-faceted) accounts of causality
Difference-making, production, …, say what causality is, in abstraction of
how know about
Constructionist epistemology
Beyond ‘science-centric’ accounts of (causal) knowledge
Perspectivism
Distributed nature of understanding
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22. Technology has poietic character
Technology enables production
Of data, phenomena, knowledge
Not merely augmenting our capacities
Seeing farther away, seeing the smaller
Technologies change the scientific ‘(info)sphere’
The object is partly created
No gap between object – subject (in-betweeness)
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23. Still pondering
Poiesis vs augmentation
Floridi: digital technologies change the e-nvironment
Is it the same for science? Is poiesis dependent on the type
of technology?
The nature of the technoscientific object
But is this new? Or does it come already with Scientific
revolution, Baconian science, instruments as power into
nature?
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24. SUM UP AND CONCLUDE
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25. Phil Tech and Phil Sci run on parallel tracks of inquiry
Yet, there seems to be one technoscientific object of study
Technology urges a reformulation of traditional phil sci questions
Traditional phil sci categories don’t hold up the challenges of
technoscience
Poiesis helps understand how knowledge (specifically, causal) is
produced
Not merely scientific nor merely technical, but technoscientific
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