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Journal Of The Association For Information Science And Technology Add in library

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Question:

Describe about a Journal of the Association for Information Science and Technology?
 
 

Answer:

Big data have enthralled the whole world with the magnitude of its capacity and the ability to handle bulks of data. The recent interest have made the debates evolve regarding ethical and disciplinary contexts in some of the particular domains of practical applications of big data. The need of the hour is to synthesize and evaluate some conceptual dilemmas building around Big Data. There are some attributes of big data that require more critical insight and attention such as opacity, disparity, autonomy, generativity and futurity (Ekbia, 2015).

The whole concept of Big Data is unambiguous therefore, there have been three dimensions of definitions proposed including a product oriented approach that includes the scale and size of data; process oriented approach pertaining to the inclusion of technological frontier (Fiesler, 2015); and cognition focused approach oriented towards the relation with human beings.

The current technology is deemed to be incompetent and insufficient to analyze the big data because of economic concerns or limitations of machinery and applications. In terms of ethical withering of brain, on one hand big data is a catalyst to solve the macro scale problems of the real world but its complexity on the other hand and the massive interactions with real world is not the ability of human mind to comprehend. The ethical dilemma of complexity have made the utter need of statistical analysis, technological infrastructures, tans disciplinary work and visualization techniques to be embedded in the concept of big data to reduce its complexity and let the human brain comprehend it for effective application.

The scientists and advocates of data driven intensive science of big data have been trapped under the debate of distinction between relation and correlation many of it being like a debate between theory driven science and data driven science (Shin, 2015). The crux of big data and the hallmark of its data mining lies in prediction which might sometimes lead to drastic ethical dilemmas which can further worsen the situation. Another upcoming ethical debate and a strike question is dichotomy between qualitative and quantitative methods. There have been minute distinctions leading to blurring of difference between interpretation and analysis of Big Data. The data making is a process of multiple social agents having variety of diverse interests.

 

Big data undergoes stages of collection, management, storage and to bring it into usable form for the analysts to dwell deeper into its meaning, it is essential that it is cleaned with scripting languages like Python or Perl or with the help of automated tools like Beautiful Soap (Alagidede, 2015). This stage includes human intervention and his skills of judgment and interpretation to call their subjective opinions which might sometimes spoil or deteriorate the data. Sometimes even data liquidity involving personal data and de-identification of it further makes the situation critical. Variety of areas using big data like social media, location tagged payments, medicine and geo locating devices face the ethical dilemma between risks to data privacy and anonymity through re-identification and ethos of transparency, liquidity and data sharing.

With the debate of what to count and what not to consider on the input side of big data also extends to the output side with the dilemma being what to select and what to leave. In small samples of data, the issues of normality, volatility and validation concerns the analysts of big data. Moreover, a small change in even a single unit can totally reverse the effect of results and significance of the output. Though there have been remedial actions proposed for improving the reliance on the statistical significance like use of Bayesian analysis, correcting of degrees of freedom and selection of a smaller p value but they are not enough to address the concern of sampling and selection bias in the analysis of big data (Watson, 2015).

Inclusion of big data in the analysis and research purposes have further aggravated and amplified the long lived ethical dilemmas in the science and humanities study. There are plethora of gaps between the articulated visions and the reality in practical scenario pertaining to big data. It includes analysis of a huge amount of heterogeneous data without having the knowledge of those affected. They are generated silently and are put to unforeseen applications (Lake, 2015) as they are collected by many of the seen and unforeseen sources incriminating the privacy and also leading up to secondary disadvantages pertaining to tracking, profiling, exclusion, discrimination and loss of total control. 

Big data though have captured enormous data under its realm but have also bought bigger ethical tensions for the company using this technology to analyze into the social world having unmeasurable data kept to be analyzed.

 

References

Ekbia, H., Mattioli, M., Kouper, I., Arave, G., Ghazinejad, A., Bowman, T., ... & Sugimoto, C. R. (2015). Big data, bigger dilemmas: A critical review.Journal of the Association for Information Science and Technology.

Fiesler, C., Young, A., Peyton, T., Bruckman, A. S., Gray, M., Hancock, J., & Lutters, W. (2015, February). Ethics for Studying Online Sociotechnical Systems in a Big Data World. InProceedings of the 18th ACM Conference Companion on Computer Supported Cooperative Work & Social Computing (pp. 289-292). ACM.

Shin, D. H., & Choi, M. J. (2015). Ecological views of big data: Perspectives and issues.Telematics and Informatics, 32(2), 311-320.

Alagidede, P. (2015). Book Review: Development dilemmas: The methods and political ethics of growth policy.African Review of Economics and Finance,6(2), 144-149.

Watson, D. (2015). Research ethics and integrity for social scientists.International Journal of Research & Method in Education, (ahead-of-print), 1-2.

Lake, P., & Drake, R. (2015). Information Systems Management in the Big Data Era.

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