Wids 2019

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Talk given for Women in Data Science event in Zurich on 5th April 2019 about using crowdsourced data and interpreting the biases within

Dados e análise

The role of innovative data collection
methods in advancing understanding, and
the importance of considering the biases
within.
Dr Reka Solymosi
@r_solymosi
Reka.Solymosi@Manchester.ac.uk

Lecturer in quantitative methods
Centre for Criminology
University of Manchester
“New” forms
of data
@r_solymosi

• Recall
• Interviewer bias
• Spatial resolution
@r_solymosi

Harnessing
information and
skills from large
crowds into one
collaborative
project.
@r_solymosi

@r_solymosi

@r_solymosi

@r_solymosi

@r_solymosi

@r_solymosi

@r_solymosi

@r_solymosi

What’s in a name?
@r_solymosi

Gender
Anon: 66.8%
Male: 24.4%
Female: 8.6%
@r_solymosi

@r_solymosi

@r_solymosi

@r_solymosi

Neighbourhood(?) guardianship
@r_solymosi

@r_solymosi

@r_solymosi

@r_solymosi

@r_solymosi

@r_solymosi

@r_solymosi

@r_solymosi

CAUTION!
@r_solymosi

Mode of production
• Involved/ interested citizens
• Pareto principle (++)
@r_solymosi

@r_solymosi

@r_solymosi

Ethical consideration
• Privacy/ surveillance
• Data ownership
• Data linkage
• Implications of findings (service slanting)
@r_solymosi

Possible solutions?
Weighting?
Small area estimation?
Creative ways to make use of biases
@r_solymosi

Thank you!
Questions?
@r_solymosi
reka.solymosi@manchester.ac.uk

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怎样办理圣地亚哥州立大学毕业证（SDSU毕业证书）成绩单学校原版复制

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Wids 2019

1. The role of innovative data collection methods in advancing understanding, and the importance of considering the biases within. Dr Reka Solymosi @r_solymosi Reka.Solymosi@Manchester.ac.uk

2. Lecturer in quantitative methods Centre for Criminology University of Manchester “New” forms of data @r_solymosi

3. • Recall • Interviewer bias • Spatial resolution @r_solymosi

4. Harnessing information and skills from large crowds into one collaborative project. @r_solymosi

10. @r_solymosi

11. @r_solymosi

12. What’s in a name? @r_solymosi

13. Gender Anon: 66.8% Male: 24.4% Female: 8.6% @r_solymosi

14. @r_solymosi

15. @r_solymosi

16. @r_solymosi

17. Neighbourhood(?) guardianship @r_solymosi

18. @r_solymosi

19. @r_solymosi

20. @r_solymosi

21. @r_solymosi

22. @r_solymosi

23. @r_solymosi

24. @r_solymosi

25. CAUTION! @r_solymosi

26. Mode of production • Involved/ interested citizens • Pareto principle (++) @r_solymosi

27. @r_solymosi

28. @r_solymosi

29. Ethical consideration • Privacy/ surveillance • Data ownership • Data linkage • Implications of findings (service slanting) @r_solymosi

30. Possible solutions? Weighting? Small area estimation? Creative ways to make use of biases @r_solymosi

31. Thank you! Questions? @r_solymosi reka.solymosi@manchester.ac.uk

Notas do Editor

Howe, Jeff. Crowdsourcing: How the power of the crowd is driving the future of business. Random House, 2008.
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*within Greater London Area
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