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Data Justice

Data justice adopts a social justice perspective to the collection, analysis and dissemination of data. A social justice lens evaluates how a society’s structure and values impact an individual’s experiences.

The increasing popularity of big data has raised concerns regarding the social implications they pose. Data justice attempts to evaluate the justness of data-use and to establish data standards and practices which maintain and advance social equality.

Arguments for data justice

Inherent biases in big data

Inherent biases can occur in both the development and interpretation of datasets. Some argue that all data are subject to biases because the collected data is reflective of social privilege and injustices. Information technologies are not biased entities in themselves. Rather, it is human influence that embeds social privilege and power into the information they contain.

Algorithms

Algorithms are founded by big data. It is argued that algorithms cannot be subjective. This argument is founded on the basis that whoever developed the algorithm, intentionally or not, incorporated some degree of personal biases. Algorithms are used to target specific audiences and personalize the results for each consumer. This can result in a "filter bubble" that prevents individuals from being exposed to certain information. As a result, individuals are not exposed to perspectives which challenge their current beliefs and attitudes.

Solutions

A variety of solutions have been offered by data experts.

Education and awareness

Data are complex and often vulnerable to misinterpretation. This concern would be mitigated if individuals had the necessary knowledge and education that is required to analyze and interpret big data. Increased knowledge would enable consumers to critically reflect upon how the collection, construction and interpretation of big data are susceptible to socially unjust biases.

Mixed methodology

Mixed methodologies, which contain qualitative and quantitative research methods, are necessary elements to challenge social injustice. The qualitative approach provides insight regarding community values, perspectives and attitudes, while the quantitative data is used to reinforce the study's reliability.

Moral guidelines and standards

Data administrators should adhere to ethical guidelines and standards to ensure the justness of data. Data would be subject to evaluation and revision to ensure social justness is pursued.

Topic Selection: Data Justice

For the purpose of this assignment I have chosen the topic of Data Justice. Data can pose dramatic social and political implications for both individuals and communities. For this reason, various scholars demand that rules and ethical standards are established around data to ensure the progression of social justice.

This article will illustrate how recent developments in data, particularly big data and datafication, has lead to the creation of data justice. Furthermore, the article will examine potentially harmful outcomes of misused data. To conclude, suggested solutions and policies from experts in the field will be highlighted.

Bibliography

• Dencik, L., Hintz, A., & Cable, J. (2016). Towards data justice? The ambiguity of anti-surveillance resistance in political activism. Big Data & Society, 3(2), 2053951716679678.
• Taylor, L. (2017). What Is Data Justice? The Case for Connecting Digital Rights and Freedoms on the Global Level.
• Holtzhausen, D. (2016). Datafication: threat or opportunity for communication in the public sphere?. Journal of Communication Management, 20(1), 21-36.
• O’Hara, K., Nguyen, M. C., & Haynes, P. (Eds.). (2014). Digital Enlightenment Yearbook 2014: Social Networks and Social Machines, Surveillance and Empowerment. IOS Press.
• Hintz, A., Dencik, L., & Wahl-Jorgensen, K. (2017). Digital Citizenship and Surveillance | Digital Citizenship and Surveillance Society — Introduction. International Journal Of Communication, 11, 9. Retrieved from http://ijoc.org/index.php/ijoc/article/view/5521
• Elmer, G., Langlois, G., & Redden, J. (Eds.). (2015). Compromised data: From social media to Big Data. Bloomsbury Publishing USA.
• Milan, S. & Velden, L. (2016). The Alternative Epistemologies of Data Activism. Digital Culture & Society, 2(2), pp. 57-74. Retrieved 4 Oct. 2017, from doi:10.14361/dcs-2016-0205

Article Evaluation

For the purpose of this assignment, I examined Wikipedia's article Open Data.

Is everything in the article relevant to the article topic? Is there anything that distracted you?

Everything in the article pertained to the topic. The article was divided into two major topics; open data in science and open data in government. Although this provided different perspectives on open data, the two subjects were not very well correlated and this caused the flow of the article to suffer.

Is the article neutral? Are there any claims, or frames, that appear heavily biased toward a particular position?

Although this article outlines arguments both for and against open data, the article provides considerably more examples of the benefits of open data. That being said, the article states that the advantages and disadvantages generally depend on its use. For example, the benefits for scientific use focused on the accessibility of data and to decrease the risk of data loss. Government open data is supported for its ability to engage citizens and enhance transparency. Comparatively, it states that individual institutions are generally the challenging force to open data and largely relies on funds. The advantages were examined in great detail, whereas the cons were few.

Check a few citations. Do the links work? Is there any close paraphrasing or plagiarism in the article?

After reviewing the citations, no links were found to be broken. No cases of paraphrasing, plagiarism or copyright violations were found, but four statements still require a citation.

Is each fact referenced with an appropriate, reliable reference?

For the majority of the article, facts were accurately referenced. Published journal articles provided the majority of the data for the page. The article also included various external webpages for additional information.

Is any information out of date? Is anything missing that could be added?

Open data is a relatively new concept and therefore the information is up-to-date. The earliest reference was published in 2005, ensuring the relevance of the data.

How is the article rated? Is it a part of any WikiProjects?

The Talk page of this article indicates that it is rated as C-Class. It is a part of WikiProject Open and WikiProject Computing.

How does the way Wikipedia discusses this topic differ from the way we've talked about it in class?

Although we have not yet discussed open data in great detail, I suspect that our in-class discussions will not focus so much on the scientific use of open data. Instead, how open data effects society and changes the way people think about data. In our first class we talked about what we thought data is and there was an array of answers. Open data is disseminated in many different forms and as a result, challenges how society imagines data.