A Critical Data Ethics Analysis of Algorithmic Bias and the Mining/Scraping of Heirs' Property Records

Author:

Throne Robin1ORCID

Affiliation:

1. University of the Cumberlands, USA

Abstract

The data and research ethics surrounding artificial intelligence (AI), machine learning (ML), and data mining/scraping (DMS) have been widely discussed within scholarship and among regulatory bodies. Concurrently, the scholarship has continued to examine land rights within the Gullah Geechee community for heirs' property land rights and land dispossession along the Gullah Geechee Cultural Heritage Corridor (GGCHC). This chapter presents the results of a critical data ethics analysis for the risks of data brokerage, algorithmic bias, the use of DMS by dominant groups external to the GGCHC, and the ensuing privacy implications, discrimination, and ongoing land dispossession of heirs' property owners. Findings indicate a gap in documented research for heirs' property records, yet Gullah Geechee algorithmic bias was evident. Further research is needed to understand better the data privacy protections needed for heirs' property records and ongoing scrutiny of local versus federal policy for data privacy protections specific to heirs' property records.

Publisher

IGI Global

Reference32 articles.

1. African-American Burial Grounds Preservation Act. S. 3667, 117th Cong. (2022). https://www.congress.gov/bill/117th-congress/senate-bill/3667

2. Heirs Property, Critical Race Theory, and Reparations

3. Bargeman, K. B. (2023). The heirs' property dilemma: How more robust federal policies can help narrow the racial wealth gap. North Carolina Banking Institute, 27(1), 320-348.

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5. Cambridge. (2023). Data scraping. In The Cambridge dictionary. Cambridge University Press.

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