Samenvatting
Summary Data Driven Dreams
I created summaries for myself for Health Care Management by writing down all the slides of the presentations with the notes of the teachers
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Data-DrivenDreams
GW4032MV
May 19, 2023 – June 29th, 2023
Thomas van Waesberge
537735tw
,Week 1
Trends in healthcare
- Datafication
o Health into data sleep, steps
- Digitization
o For computational analyses
- Computational abilities (ML/AI)
o Enhanced models the last years to analyze the data
- High expectations but also many concerns
o Legal
o Ethical
o Societal how does society change positive/negative
o Professional medical professionals how does AI change professional routines
and how they work
Data Driven Dreams
- Societal expectations dreams what will they enable us to do
- Also focus on socio-technical perspective how do technology shape societal expectations
and discussion and the other way around
- Interplay between:
o Technology
Maximizing computation power and algorithmic accuracy to gather, analyze,
link, and compare large data sets
o Analysis
Drawing on large data sets to identify patterns in order to make economic,
social, technical, and legal claims
o Mythology
The widespread belief that large data sets offer a higher form of intelligence
and knowledge that can generate insight that were previously impossible, with
the aura of truth, objectivity, and accuracy
Algorithmic walk
Algorithm
- What is an algorithm?
o Can be thought of as the basic steps in Big Data as they are the a basis of machine
learning
o Technical:
A set of instructions for solving a problem step-by-step, typically execute by a
computer
Cookbook for example
o Socio-technical:
Powerful (often obscure) entities that somehow govern, shape and control
infrastructures, practices and our daily lives
Personalized suggestions on Netflix, YouTube or news for example
can create polarization on ideas when only seeing certain information
filter bubbles, not seeing other information
, - What does an algorithm look like
- How can we use an algorithm
Reflect on algorithmic reasoning
- Navigator: determines the route
- Referee: makes sure that the rules are followed correctly
- The photographer: takes photos of critical or illustrative situations
- Map maker: draws a map of the groups path
- Not taker: takes notes of critical situations
- Collector: identifies/collects objects that illustrate
Literature week 1
Schonberger & Cukier (2014) – Big Data: a revolution
What are the possibilities of Big Data according to the authors?
- Data will help us make sense of our world in ways we are just starting to appreciate
o Decoding human genome
o Trading on markets by computer algorithms
- Increase the scale of data, we can do new things that weren’t possible when we worked with
smaller amounts
o Solution to global problems climate change, economic development and good
governance
- Many aspects of the world will be augmented or replaced by computer systems that today are
the sole purview of human judgement
o Big data is predictions applying math what is the chance increasing the
amount of data, the more accurate the prediction
- Source of new economic value and innovation, and the way we analyze information that
transform how we understand and organize society
o Using all data lets us details we could never see with smaller (samples) quantities. Big
data gives us an especially clear view of the granular: subcategories and submarkets
that samples can’t assess
- Datafication transforming information about anything and turn it into data to make it
quantified allows predictive analysis and unlocks implicit, latent value of the information
- Specific area expertise matters less force an adjustment to traditional ideas of
management, decision-making, human resources and education
What are (new) characteristics of Big Data mentioned in the chapter?
- The new quantity of information that surrounds us and how fast it grows 1200 exabytes, of
which 2% non-digital
- It is more digital than analog 7% of data in 2007 was analog (paper, books, photos etc)
2000 25% was digital
- By changing the amount, the essence is changed a photo capturing 24 frames per
second quantitative change produced a qualitative change increase the scale of data,
we can do new things that weren’t possible when we worked with smaller amounts
- Big data about predictions
- Improved efficiency, information collection and analysis one took years, now in days or less
What are downsides of Big Data according to the authors?
- We rely on it daily
, o Autocorrect, dating sites, spam filter etc. will become more with cars automatically
braking
- Undermine privacy algorithms predict individual likelihoods of heart attack or commit a
crime, and adjust health insurance or deny loans ethical consideration
Does this chapter fit better with the technical or socio-technical approach?
- Socio-technical, because the chapter talks about how technology will shape our social lives
Vincent (2014) – Politics of buzzwords at the interface of technoscience
What does the author mean with her description of buzzwords as 'linguistic technologies'?
- Stereotyped phrases
- Buzzwords omnipresent and used ad libitum (used as often as necessary or desired)
The author describes three main performances in how buzzwords shape techno-scientific
developments. What are these three performances? Can you think of examples?
- Buzzwords as linguistic technologies more than just empty, meaningless terms used for
marketing and communication purposes like signposts, pointing to a direction and inviting
us to move in this direction. As such, they can be analyzed as powerful linguistic technologies:
o Generate matters of concern and play an important role in trying to build consensus
(Re)shaping practices
Guide research and innovation, and public use of technology.
Create concern metaphors: ‘green technology’ suggests possible
alliance between nature (the color green) and technology.
o Set attractive goals and agendas
Attract people point to a goal
Ideal goal (out of reach) ‘zero emission’ inspire guidelines for action
Provide direction help actors of innovation make sense of their action in a
global historical context of innovation.
o Create unstable collectives through noise
Create a movement, a mainstream, it is not because of their meanings: they
just make noise creating agitation.
Instead of transmitting a signal detached from noise they seek to transmit a
signal by increasing noise.
Ambiguities and various actors retain their language come together and
rely on misunderstandings to achieve something
Buzzwords are alive as long as they manage to gather people around a
matter of concern.
How could you describe Big Data as a buzzword based on this analysis? Can you think of examples of
how Big Data as a buzzword shapes techno-scientific developments?
- Big data has the power of prediction these prediction can generate concern, set attractive
goals and agendas and could create a movement
Dalton & Thatcher (2014) – Critical data studies. Society and space
What is Big Data according to the authors?
- Data that is dominant in our lives, but it is receding into the banality of the every-day
Why is a critical approach needed and what should it study?
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