December 16, 2022

Data and Society: quiz

Data & Society: Quiz

This quiz have questions regarding data and the society, there are 13 questions with correct answers.

These questions is designed to open the readers mind and think about the data that are stored, generated and used today.

A full reference list is provided at the end of the quiz for further reading…

Questions

Qusestion 1: What is data?

  • Facts that we can measure quantitatively
  • Knowledge that we can acquire from reliable sources
  • Facts and statistics collected for further analysis
  • Information collected from reliable sources

Question 2: According to Kitchin, data is not a “given” but a “taken? What does he mean?

  • Data is not a natural resource
  • It takes a lot energy to collect data
  • Data is out there for us to collect if we need
  • Data are construed, recorded, and collected as the result of human decisions

Question 3: Why do we need to clean data?

  • Data might have outliers.
  • Data might be invalid.
  • There might be a loss of data.
  • All of the other answers.

Question 4: What makes data objectivity challenging?

  • Measuring and collecting data involves interpretation and value judgements.
  • Not using direct observation properly during data collection.
  • Not using proper data collection tools.
  • Our inclination to use judgements of value.

Question 5: Can we equate data neutrality and data objectivity?

  • No, they are different concepts.
  • Partly, because neutrality depends on objectivity.
  • Yes, because both presuppose human detachment.
  • Partly, because data objectivity can result in neutrality

Correct. Neutrality too is a value judgment and not always the most objective one. Sometimes, it is neutrality that represents bias.

Question 6: Which of the following characterizes the notion of data as discussed in this course?

  • Data is a stable object ready for use.
  • Given a lot of data, useful and reliable discoveries would follow with no need for interpretation.
  • Data moves from sites of creation to sites of analysis and interpretation.
  • Data practices are the same regardless of the sector and area.

Question 7: Which of the following could improve facial recognition?

  • The training data should be more varied for the AI system but we cannot generate enough data about women and people of color.
  • Better representation of women and people of color in the data that trains AI.
  • Machine will always discriminate no matter the data used for training.
  • Humans intervene to rectify faulty facial recognition matches.

Question 8: Which of the following explains why datafication is not only about data?

  • It is composed of four layers, including data in different forms.uding data,
  • It requires regulation to intervene in specific sectors.
  • It requires human and technical capacity to handle data.
  • It focuses also on the individual rights of the data subjects.

Question 9: Why should we challenge the reification of datafied identities?

  • We should challenge non-objective classifications.
  • Because we all have multiple identities and we cannot fit a single classification
  • When constructing and categorise data, system developers can treat socially constructed and negotiated categories of identity as fixed.
  • We should not challenge reification because categorising identities is a useful and easy way to sort data.

Question 10: Which of the following is considered a form of unintentional data pollution?

  • Encoding societal biases and prejudices in a dataset
  • Including data pollutants in a training set by mistake.
  • Not including greener data in a training dataset.
  • Not taking an ethical stance on data

Question 11: What are important aspects for platforms to work/ to ‘platform’?

  • Their role as intermediaries, application programming interfaces and a business model
  • Their role as standalone, diconnected actors that benefit from users’ interaction with the GUI to generate data points.
  • The creation and provision of content, the use of application programming interfaces and means for revenue

Question 12: What is platformization?

  • The technical re-configuration of websites into platforms.
  • Using a platform as a base to build or use software or another platform on top of it
  • The omnipresence always changing platforms in most spheres of social life and the re-organization of social life according to platform logics.

Question 13: What characterizes the digitalization of the 21th century, according to the ‘digital trinity’?

  • A stronger stance taken by national, supra-national and global actors to adress Artificial Intelligence
  • A spiral of interconnected platformization, datafication and algorithmization
  • The increasingly distributed role of individuals, the community and governments. when it comes to questions and concerns about digital data use.

References

  • Beaulieu, A., & Leonelli, S. (2021). Data and society. A critical introduction. Sage. Available as a preprint at https://ore.exeter.ac.uk/repository/bitstream/handle/10871/127993/Data%20and%20Society_Pre print.pdf?sequence=2 (selected chapters).

  • Bowker, G.C., & Star, S.L. (2000). Sorting things out: Classification and its consequences. Cambridge, MA: MIT Press (Chapter 6 “The Case of Race Classification and Reclassification Under Apartheid, pp. 195-225).

  • Brown, S. (2021). Machine learning, explained. MIT Management Sloan School. https://mitsloan.mit.edu/ideas-made-to-matter/machine-learning-explained

  • Couldry, N., & Yu, J. (2018). Deconstructing datafication’s brave new world. New Media & Society, 20(12), 4473–91. https://doi.org/10.1177/1461444818775968.

  • Cui, W. (2019). Visual analytics: a comprehensive review. IEEE Access, 1-19.

  • D’Ignazio. C., & Klein, L.F. (2020). Data feminism (Chapter 3 “What gets counted counts). Cambridge, MA: MIT Press.

  • Dourish, P., & Gómez Cruz, E. (2018). Datafication and data fiction: narrating data and narrating with data. Big Data & Society, 5(2), 1-10. https://doi.org/10.1177/2053951718784083.

  • Hasselbalch, G. (2022). Data pollution & power – white paper for a global sustainable agenda on AI (pp.18-35). The Sustainable AI Lab, Bonn University.

  • Micheli, M., Ponti, M., Craglia, M. and Berti Suman, A. (2020). Emerging models of data governance in the age of datafication. Big Data and Society, 7(2), 1-15. https://doi.org/10.1177/2053951720948087

  • Redman, T.C. (2018). If your data is bad, your machine learning tools are useless. Harvard Business Review. https://hbr.org/2018/04/if-your-data-is-bad-your-machine-learning-tools-are-useless

  • Venturini, T., & Munk, A.C. (2022). Controversy mapping: A field guide. Cambridge, UK; Polity (selected chapters). Available at the Göteborgs universitetsbibliotek Samhällsvetenskapliga biblioteket

  • Winner, L. (1999). Do artifacts have politics? Daedalus, 109(1), 121-136 (Winter 1980).