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Data-gathering, governance, and algorithms : how accountable and transparent practices can mitigate algorithmic threats

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thesis
posted on 23.05.2021, 13:10 by Alexander Gramegna
Corporate use of algorithms for marketing purposes often entails that user data is collected and processed by corporations to influence consumers online. Despite the technological efficiencies that many algorithms provide, algorithms often pose threats to human autonomy and privacy in a consumer context. While algorithms have the capacity to influence individuals and shape their behaviour, human inputs and regulations shape their functions and mandates. Regulatory measures and government legislation are also capable of shaping algorithmic functions, sometimes in ways that mitigate threats to user autonomy and privacy. Many scholars suggest that implementing practices of accountability and transparency into algorithmic regulation can mitigate the threats algorithms pose to society. This Major Research Paper will conceptualize algorithmic threats to user privacy and autonomy, as well as practices of accountability and transparency. A critical analysis of the European Union’s General Data Protection Regulation will assist in recognizing specific practices that are capable of mitigating algorithmic threats to user privacy and autonomy. The analysis and discussion of the GDPR’s potential efficacy will use mutual shaping theory to explore the role legislation plays in the co-evolution of algorithmic technology and society. Key Words: Algorithms, Data-Gathering, Privacy, Autonomy, Accountability, Transparency, General Data Protection Regulation, GDPR, European Union, Mutual Shaping Theory

History

Language

eng

Degree

Master of Professional Communication

Program

Professional Communication

Granting Institution

Ryerson University

LAC Thesis Type

Thesis