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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.

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.