Chang, V. (2021). An ethical framework for big data and smart cities. Technological Forecasting and Social Change, 165, 1-11.
The central thesis of this paper is that the widespread use of big open data in the urban industry poses ethical risks. From these, Chang (2021) identifies problems of sampling bias, discrimination against particular social cohorts, opacity, and low data privacy. As a result, urban businesses can create solutions that turn out to be strategically flawed because they need more social justice. The article aligns well with Gibilisco and Steinberg (2022) and Harrison et al. (2019), who also explore the ethics of government data within the same ethical issues. This article can be used to argue that any business should ensure a high ethical standard when deciding to use open data.
Gibilisco, M., & Steinberg, J. (2022). Strategic reporting: A formal model of biases in conflict data. American Political Science Review, 1-17.
In this article, Gibilisco and Steinberg (2022) explore the conflict of interest as a critical ethical issue in the use of big data. Chang (2021) and Harrison et al. (2019) discuss the big picture of ethical issues, while Gibilisco and Steinberg (2022) focus on the problem of dishonesty and inconsistency, which threaten the quality of formulated business decisions. Governments may deliberately withhold some sensitive information for propaganda purposes. Hence, the authors call for an in-depth examination of the potential conflict of interest in using government data, which can be used as an argument in the paper. The value of this paper also lies in the authors’ proposed model for identifying distorted and false data.
Gottfried, A., Hartmann, C., & Yates, D. (2021). Mining open government data for business intelligence using data visualization: A two-industry case study. Journal of Theoretical and Applied Electronic Commerce Research, 16(4), 1042-1065.
In this paper, the authors describe using a visualization model to analyze large datasets. Gottfried et al. (2021), like Loukis et al. (2020), point out that it is the scale and width of government data that poses a threat to the quality of interpretation, so Gottfried et al. (2021) propose a Dirichlet latent distribution model to identify key themes in the array without data loss. This fits well with the results of Harrison et al. (2019), who propose using AI for big data processing. The value of the paper lies in proposing a working methodology for analyzing.
Harrison, T., F. Luna-Reyes, L., Pardo, T., De Paula, N., Najafabadi, M., & Palmer, J. (2019). The data firehose and AI in government: Why data management is a key to value and ethics.
In this paper, Harrison et al. (2019) describe the ethical challenges of government data, noting, as did Chang (2021) and Gibilisco and Steinberg (2022), the potential for bias and opacity. However, like Gottfried et al. (2021) and Loukis et al. (2020), they propose a methodology for using AI to objectively examine data without bias. The article’s critical value, expressed in a future argument, is that effective data management is essential to realizing the value and ethical potential of technology in government data use. In addition, this article can be seen as an integration of the ideas of the four previously discussed works.
Loukis, E., Kyriakou, N., & Maragoudakis, M. (Eds.) (2020). Electronic government: 19th IFIP WG 8.5 international conference, EGOV 2020, Proceedings 19. Springer International Publishing.
Loukis et al. (2020) describe a machine learning algorithm methodology to analyze big data and create predictions in this article. In particular, publicly available government data can be used to train neural networks to make further predictions about organizational sustainability and competitiveness. This article can be complemented by the findings of Gottfried et al. (2021), which can be used for candlestick analysis and pattern detection. Overall, the article’s practical value lies in proposing a new and relevant methodology that can serve as an argument for the diversity and progressiveness of government data analysis.