Foresight Friday 19.01.2018

1. Kari Hiekkanen, Aalto Yliopisto
@ Foresight Friday, 19.1.2018
Digital Ethics ?

2. “Dislocations in labor markets,
with old industries
and jobs disappearing.”
(Robotic Jobocalypse, …)
“The destruction of privacy
in an unprecedented
and irrevocable manner.”
(Big Brother Society, …)
The danger of
growing social inequality.
(Digital Divide, …)
“What will happen
to democracy?”
(1-9-90 Rule, Social Mobbing, …)
(Don Tapscott, After 20 Years, It’s Harder to Ignore the Digital Economy’s Dark Side, Harvard Business Review 2016 )
(Tapscott, 1995)

3. (Gartner, Jun 2017)
Technological developments have in history always at some point
during their implementation into society forced us to revisit laws,
but in particular also ethical value systems and limits.
Time and again we are faced with the fact that technology
is in fact not neutral,
but contain in their very design ethical implications.
(Hasselbalch, 2016)

4. (© TheDailyBeast, 2015)

5. (© BusinessInsider, 2012)

6. (© ZDNet, 2013)

7. (© Independent, DailyMail 2017)

8. “We can build these models, but we don’t know how they work.”
(MIT Technology Review, 2017)

9. “We can build these models, but we don’t know how they work.”
(MIT Technology Review, 2017)

10. “We can build these models, but we don’t know how they work.”
(MIT Technology Review, 2017)

13. (Miltenburg: Stereotyping and Bias in the Flickr30K Dataset, Multimodal Corpora: Computer vision and language processing (MMC 2016))
1. A blond girl and a bald man with his arms crossed
are standing inside looking at each other.
2. A worker is being scolded by her boss in a stern lecture.
3. A manager talks to an employee about job performance.
4. A hot, blond girl getting criticized by her boss.
5. Sonic employees talking about work.
(© Flickr30K)

14. (
(Mika Mäntylä @ Twitter, 8-Oct-2017)

15. (

17. (Engadget, 2017-12-21)

18. The Irony?
Automation bias:
Belief that automated computer systems
and decisions based on data
are both objective and predictable.
Even if contradictionary evidence is present.
(c.f. Goddard, & al.. "Automation bias: a systematic review of frequency, effect mediators, and mitigators.”, Journal of the American Medical Informatics Association 19.1)

19. (Nguyen, Yosinski, Clune: Deep Neural Networks are Easily Fooled: …., In Computer Vision and Pattern Recognition, IEEE, 2015 )

20. (Sharif,et al. "Accessorize to a crime: …." Proceedings of 2016 Conference on Computer and Communications Security. ACM, 2016)

22. An inconvenient truth

23. An inconvenient truth
(Clicking Clean: Who is Winning The Race to Build a Green Internet?, Greenpeace 2017)

24. An inconvenient truth
(Andrae & Edler: On Global Electricity Usage of Communication Technology: Trend to 2030, Challenges 2015)
Expected Case
*Finnish Energy Consuption 2016: 312 TWh; Olkiluoto 1&2: 1.76 TWh

25. – The End –