This document discusses the ethics of increasing automation and the evolving knowledge infrastructure. It notes challenges around trusting automated analysis with no human in the loop, and understanding complex and evolving data sources. Breakout groups are proposed to discuss interventions for improving research quality, issues around reproducibility with different data types, and the impact of large online data on reproducibility in social sciences. The document references challenges around safety vs security, and tradeoffs between hardening systems and adaptive response.

1. David De Roure
@dder
The Ethics of Automation
A dystopian view of our
evolving knowledge infrastructure
DIRECTOR, UNIVERSITY OF OXFORD E-RESEARCH CENTRE

2. http://zubitubi.blogspot.co.uk/2016/03/school-funny-picture-prank-memories.html
Christine Borgman

3. Engineering
Cyber
Linguis.cs
English
Oxford
Mar.n
School
Saïd
ARC IT Services
ECI Geography SKA
CUDA
Physics
Computer
Science
Maths
History
Oxford
Internet
Ins.tute
Music
Archaeology
Classics
Zoology
Museums
Wolfson
Law
Bodleian
Libraries
Pharmacology
Biochemistry
TORCH DH
Papers contain proofs
Reviewers check proofs
This is not like soNware
MIREX community
Annual challenges
Shared music corpus
Non-consump.ve
Annual conference
Sociotechnical infrastructure
Music industry is
digital end-to-end (nearly)
More so than research?
Digital Music Objects:
Crea.on, Produc.on,
Distribu.on, Consump.on,
Reuse, Archiving
When data cannot be shared
How do we build research quality?

4. Energy Efficient
Computing
Infrastructure
(STFC)
De-identified admin
(inc. health) data
Business
data
Open data
(public sector)
Social media
data
Research
data
Longitudinal
survey data
Open data
Securely held data
Environment
data
Business and LG Data
Research Centres
(ESRC)
Admin Data Research
Centres (ESRC)
High Performance
Data Environment
(NERC)
Clinical
data
Medical Bioinformatics (MRC)
Understanding Populations
(ESRC)
Clinical Practice Datalink
(MHRA, NIHR)
100,000 Genome Project NHS)
Research Data Facility (EPSRC)
European Bioinformatics
Institute (EMBL)
Bioscience E-Infrastructure
(BBSRC)
Square Kilometre Array (STFC)
Digital Transformations
(AHRC)
Archive
data
Open Data
Institute
Commercial
Research
Understanding Populations
(ESRC)

5. https://twitter.com/CR_UK/status/446223117841494016/
Some people's smartphones
had autocorrected the word
"BEAT" to instead read
"BEAR".
"Thank you for choosing an
adorable polar bear," the
reply from the WWF said.
"We will call you today to set
up your adoption."
http://www.bbc.com/news/technology-26723457

6. The Macroscope

7. Social Media Triangle
social media
data and
analytics
social media for
engagement with
research
social media
as a subject
of research
Sam McGregor

8. New Forms of Data
▶ Internet data, derived from social
media and other online interactions
(including data gathered by
connected people and devices, eg
mobile devices, wearable
technology, Internet of Things)
▶ Tracking data, monitoring the
movement of people and objects
(including GPS/geolocation data,
traffic and other transport sensor
data, CCTV images etc)
▶ Satellite and aerial imagery (eg
Google Earth, Landsat, infrared,
radar mapping etc) http://www.oecd.org/sti/sci-tech/new-data-for-
understanding-the-human-condition.htm

9. Vonu Thakuriah

10. Blackett Review of IoT
▶ The Internet of Things
describes a world in which
everyday objects are
connected to a network so that
data can be shared
▶ But it is really as much about
people as the inanimate object
▶ It is impossible to anticipate
all the social changes that
could be created by connecting
billions of devices
https://www.gov.uk/government/publications/internet-of-things-blackett-review

11. PETRAS Privacy, Ethics, Trust, Reliability, Acceptability, and Security
for the Internet of Things
• The fusion of the cyber, physical and human elements
• Scale: from 1mm3 devices to large infrastructure systems
• Managing devices throughout their (decades long) lifetimes
• New and evolving threat landscape
• Continue to operate when partially compromised
The Challenges are numerous
• Safety vs Security
• Security vs Efficiency
• Hardening vs Adaptive Response
Tradeoffs
Emil Lupu

12. More people
More machines
Data Deluge
HPC
Conven.onal
Computa.on
Social
Machines
Social
Networks
Science 2.0
e-Science
Internet of Things
Social Machines
Web Science
Big Data Machine learning AI

14. "
http://blog.zooniverse.org/2015/06/29/a-whole-new-zooniverse/
http://monsterspedia.wikia.com/wiki/File:Argus-Panoptes.jpg
Panoptes
Citizens at scale!
Citizens creating citizen
science projects
Empowered
A social machine for
creating social machines

15. Pip Willcox

16. UK Winter 2014 Floods
• 39726 simula.ons (~30TB
data)
• 2014 flooding described as
a 1 in 100 year event in
terms of rainfall volume
• Return .me plot shows this
has become a 1 in 80 year in
terms of risk
• Risk of a very wet winter
has increased by 25%
(Schaller et al, Jan 16, NCC)
David Wallom

17. Data Detect Store AnalyticsFilter Analysts

18. Human Digital Physical Triangle
human
digital physical
social
media
IoT
automation
and scale

19. Edwards, P. N., et al. (2013) Knowledge Infrastructures: Intellectual Frameworks and Research
Challenges. Ann Arbor: Deep Blue. http://hdl.handle.net/2027.42/97552

20. A computationally-enabled
sense-making network of
expertise, data, software,
models and narratives
Big Data, in a
Big Data Centre

21. Research in the Wild (West)
Imagine you are a conference chair… or responsible for urban
planning, or security. Confidence in results is getting harder:
What interventions should we make to improve confidence
and quality? What (socio-)technology can we adopt?
Trusting the analysis that
is occurring
Automation of workflows,
crowd-sourced data reduction,
software vulnerabilities, increasing
adoption of machine learning,
and no critical human in the loop
Knowing what the data is,
where it has come from,
and what we can do with it
Multiple and partial data sources,
at speed and scale, in an evolving
ecosystem of data processing
intermediaries, with complex
permissions for data use

22. Breakout topics
Group A – Lecture Theatre
Chair: Prof David de Roure
What interventions can we make to
improve the quality of research in
our increasingly automated
research communication
ecosystem?
Group C – Ho Tim Seminar Room
Chair: Prof Eric Meyer
Repurposed data (e.g. Twitter,
Wikipedia), data exhaust (e.g.
mobile phones, Loyalty cards),
proprietary data, data that comes
with non-sharing or non-disclosure
agreements (e.g. Twitter), other sorts
of commercial data, data limited in
distribution because of differing
national legal frameworks, etc.
What does reproducibility mean in
these contexts?
Group B – Louey Seminar Room
Chair: Dr Suzy Moat
Reproducibility in the social
sciences: does the arrival of large
online data sources make the
outlook better or worse?”

23. David De Roure
david.deroure@oerc.ox.ac.uk
0000-0001-9074-3016
Thanks to Christine Borgman, Chris Lintott, Emil Lupu,
Sam McGregor, Vonu Thakuriah, David Wallom, Pip Willcox.
http://www.slideshare.net/davidderoure/ethics-of-automation-60574240