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Advances in Computer and Communication

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Frequency: quarterly ISSN Online: 2767-2875 CODEN: ACCDC3
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Article Open Access http://dx.doi.org/10.26855/acc.2023.10.007

Can Data Discriminate?

Fangbin Li

The University of Sheffield, Sheffield, UK.

*Corresponding author: Fangbin Li

Published: November 22,2023

Abstract

This paper explores the consequences of data discrimination in the digital era, where data-driven systems and algorithms play a crucial role. It highlights how data discrimination manifests through biased behavior in data-driven systems, perpetuating societal inequalities and biases. Focusing on social media and big data, the analysis highlights the potential for algorithmic bias, targeted marketing practices, and the amplification of extremist content. The paper argues that data discrimination poses ethical challenges and emphasizes the need for increased transparency, fairness, and user awareness. It also addresses limitations in governing algorithmic discrimination and emphasizes the importance of interdisciplinary collaboration to tackle this pressing issue.

References

[1] Dencik, L., Hintz, A. and Cable, J. (2016). ‘Towards data justice? The ambiguity of anti-surveillance resistance in political activism’, Big data & society, 3(2), p. 205395171667967. doi: 10.1177/2053951716679678.

[2] Gangadharan, S. P., & Niklas, J. (2018). Between antidiscrimination and data: Understanding human rights discourse on automated discrimination in Europe. Available at: 

https://eprints.lse.ac.uk/88053/13/Gangadharan_Between-antidiscrimination_Published.pdf. (Accessed: 28 May 2023). 

[3] Gangadharan, S.P., Eubanks, V. and Barocas, S. (2014). Data and discrimination: Collected essays. Open Technology.

[4] The Guardian. (2020). The Guardian view on A-level algorithms: failing the test of fairness. Available at: 

https://www.theguardian.com/commentisfree/2020/aug/11/the-guardian-view-on-a-level-algorithms-failing-the-test-of-fairness. (Accessed: 29 May 2023).  

[5] Sandvig, C. et al. (2016). ‘When the algorithm itself is a racist: Diagnosing ethical harm in the basic Components of Software’. International journal of communication, 10, pp. 4972-4990.

[6] Dencik, L., Jansen, F., & Metcalfe, P. (2018). ‘A conceptual framework for approaching social justice in an age of datafication’, DATAJUSTICE project, 30. Available at: 

https://datajusticeproject.net/wp-content/uploads/sites/30/2018/11/wp-conceptual-framework-datajustice.pdf. (Accessed: 29 May 2023). 

[7] Noble, S. U. (2018). Algorithms of oppression: how search engines reinforce racism. New York: New York University Press.

[8] Weltevrede, E. and Jansen, F. (2019). Infrastructures of intimate data: Mapping the inbound and outbound data flows of dating apps. Computational Culture, (7).

[9] Zelenkauskaite, A. & Bucy, E. P. (2016). ‘A scholarly divide: Social media, Big Data, and unattainable scholarship’, First Monday, 21(5), p.1. doi: 10.5210/fm.v21i5.6358.

[10] Hoffmann, A. L. (2019). ‘Where fairness fails: data, algorithms, and the limits of antidiscrimination discourse’, Information, communication & society, 22(7), pp. 900-915. doi: 10.1080/1369118X.2019.1573912.

[11] Turow, J. & McGuigan, L. (2014). Retailing and Social Discrimination: The New Normal. Data and Discrimination: Collected Essays, pp. 27-29.

[12] Turow, J. (2012). The daily you: How the new advertising industry is defining your identity and your worth. Yale University Press.

[13] Marwick, A., Fontaine, C., & Boyd, Danah. (2017). ‘“Nobody Sees It, Nobody Gets Mad”: Social Media, Privacy, and Personal Responsibility Among Low-SES Youth’, Social media + society, 3(2), p. 205630511771045. doi: 10.1177/2056305117710455.

How to cite this paper

Can Data Discriminate?

How to cite this paper: Fangbin Li. (2023) Can Data Discriminate? Advances in Computer and Communication4(5), 299-303.

DOI: http://dx.doi.org/10.26855/acc.2023.10.007