Vortrag von #55 Datengarten, von der Chaos Computer Club Berlin.

1. D A T E N U N D T E C H N O L O G I E I N
I N T E R N A T I O N A L E R
E N T W I C K L U N G S Z U S A M M E N A R B E I T
Z A R A R A H M A N
@ Z A R A R A H

2. K O N T E X T

3. M A C H T U N G L E I C H G E W I C H T E

4. V E R S C H I E D E N E P E R S P E K T I V E N

5. V E R S C H I E D E N E
W E R T V O R S T E L L U N G E N

6. B E N U T Z E R = G E FÄ H R D E T E
B E V Ö L K E R U N G S G R U P P E N

7. E I N PA A R T R E N D S …
• Privatsektorbeteiligung
• Identifizierung von Menschen
• Kommunikation

8. E I N PA A R T R E N D S …
• Privatsektorbeteiligung
• Identifizierung von Menschen
• Kommunikation

9. P R I VAT S E K T O R B E T E I L I G U N G
Read more: on Tech Companies' responses to Nepal Earthquake

10. C O R P O R AT E S O C I A L R E S P O N S I B I L I T Y

11. N E U E T R E N D S
• Privatsektorbeteiligung
• Identifizierung von Menschen
• Kommunikation

12. – J O H N N Y A P P L E S E E D
I D E N T I F I Z I E R U N G

13. – M A R C H 2 0 1 5
"UNHCR will deploy Accenture’s Biometric Identity
Management System (BIMS) across UNHCR
global operations. The system captures and
stores fingerprints, iris data and facial images of
individuals, providing those who are often
undocumented with their only personal identity
record."

15. N E U E T R E N D S
• Privatsektorbeteiligung
• Identifizierung von Menschen
• Kommunikation

16. T R A N S PA R E N Z I N D E R
E N T W I C K L U N G S H I L F E
H T T P : / / D E V T R A C K E R . D F I D . G O V. U K

17. D ATA P O R TA L S

18. B U B B L E S …

20. B E D E N K E N

21. H Y P E
H Y P E

22. N Ü T Z L I C H K E I T

23. – J O H N N Y A P P L E S E E D
Google Trends (captured June 4, 2015)

24. N Ü T Z L I C H K E I T

25. " D I S I N F O R M AT I O N V I S U A L I S AT I O N "
Read more: Disinformation Visualization: How to lie with datavis, by
Mushon Zer-Aviv

26. I D E N T I TÄT ≠ I D E N T I F I Z I E R U N G

27. I N T E R N E T. O R G
D O P P E L M O R A L

28. - R E P O R T: PAT I E N T P R I VA C Y I N A M O B I L E W O R L D 2 0 1 3 .
"most African countries have not implemented
comprehensive data privacy laws like those
existing in Europe"
E X P E R I M E N T E

29. P R O J E C T L O O N
E X P E R I M E N T E

30. F E R N G E S T E U E R T E V E R H Ü T U N G S -
I M P L A N TAT E
E X P E R I M E N T E

31. - A N K A R A , T U R K E Y - J A N 2 0 1 5 , R E U T E R S
“Since December, Syrians must be registered in
order to get access to free healthcare and
schooling outside of camps.”
Z U S T I M M U N G

32. - J A N 2 2 N D , 2 0 1 4 D I R E C TO R O F U N H C R ’ S D I V I S I O N
O F P R O G R A M M E S U P P O RT, M A L AW I .
"Improving the accuracy of registration
data is a priority for UNHCR in our efforts to
make a positive impact… it is also of
significant importance to host
governments."
Z W E C K E N T F R E M D U N G D E R D AT E N

33. – A L - A R A B I YA A R T I C L E , J A N 1 2 2 0 1 5
“Turkey has provided some 1.5 million
Syrian refugees with biometrics IDs...the IDs
will be used in the provision of aid, job
offers, education + social opportunities.
The database will also be used to
identify those who have been involved
in criminal activity”

34. - D I A L I N G D O W N R I S K S , B Y H I B A H H U S S A I N F O R
T H E N E W A M E R I C A F O U N D AT I O N ( A U G U S T 2 0 1 3 )
“a fifth (18%) of participants reported that
someone else had inadvertently received
their IVR or SMS containing AIDS and HIV
information”
T E C H N I S C H E F E H L E R

35. ● Personifizierung: Begünstigte = passive Datensubjekte
● Zustimmung: unterschiedliche Bedeutung und Wichtigkeit
● Datenschutz: Aufenthaltsort
● Technisches Verständnis
● Machtgefälle: durch Technologie noch stärker
● Technische Design-Probleme: Überprüfungsmöglichkeiten
I S S U E S

36. W I E S O L LT E E S W E I T E R
G E H E N ?

37. Verantwortlicher
Umgang mit Daten
http://responsibledata.io

38. Datensparsamkeit und Datenvermeidung

39. Ausbildung
everyone works with data!

40. Bessere
Datenkenntnisse und
technische Kenntnisse
http://schoolofdata.org

41. – J O H N N Y A P P L E S E E D
Dystopian theories of change
http://techtohuman.com, by Lucy Chambers

42. FA Z I T

43. WA S F E H LT ?

44. V E R S TÄ N D N I S

45. Kein ist neutral
daten
app
technologie

46. Danke für Eure Aufmerksamkeit!
mail@zararah.net
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