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Poietic character of technology
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Poietic character of technology
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 ©FedericaRusso 2
3.
PHIL SCI AND
PHIL TECH: ONE OBJECT, TWO DISCIPLINES? ©FedericaRusso 3
4.
Two disciplines? Different professional
societies Low permeable conferences Specialised journals and publication venues Few joint job descriptions and study programmes … ©FedericaRusso 4
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 ©FedericaRusso 5
6.
Towards a multifaceted
discipline? Society for Philosophy of Science in Practice Journal: Philosophy and Technology Symposia on Philosophy of Interdisciplinarity in various conferences … ©FedericaRusso 6
7.
EXPOsOMICS ©FedericaRusso 7
8.
The ‘exposome’ 8©FedericaRusso
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 9©FedericaRusso
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 10©FedericaRusso
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 ©FedericaRusso
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 ©FedericaRusso 12
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? ©FedericaRusso 13
14.
Technology in exposomics Sensors, smartphones,
GPS Omic technologies Liquid chromatography Mass spectometry Nuclear magnetic resonance spectroscopy … Statistics softwares ©FedericaRusso 14
15.
Technology—built in—science Technology is
present at every stage of the scientific process Data generation > Data collection > Data analysis ©FedericaRusso 15
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 ©FedericaRusso 16
17.
SCIENCE⇋TECHNOLOGY Subordination? ©FedericaRusso 17
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 ©FedericaRusso 18
19.
SCIENCE⥊TECHNOLOGY Poiesis ©FedericaRusso 19
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) ©FedericaRusso 20
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 ©FedericaRusso 21
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) ©FedericaRusso 22
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? ©FedericaRusso 23
24.
SUM UP AND
CONCLUDE ©FedericaRusso 24
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 ©FedericaRusso 25