The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Machine Learning Technologies and Their Inherent Human Rights Issues in Criminal Justice Contexts
Open accessAbstract
This essay is an introductory exploration of machine learning technologies and their inherent human rights issues in criminal justice contexts. These inherent human rights issues include privacy concerns, the chilling of freedom of expression, problems around potential for racial discrimination, and the rights of victims of crime to be treated with dignity. This essay is built around three case studies - with the first on the digital 'mining' of rape complainants' mobile phones for evidence for disclosure to defence counsel. This first case study seeks to show how AI or machine learning tech might hypothetically either ease or inflame some of the tensions involved for human rights in this context. The second case study is concerned with the human rights challenges of facial recognition of suspects by police forces, using automated algorithms (live facial recognition) in public places. The third case study is concerned with the development of useful self-regulation in algorithmic governance practices in UK policing. This essay concludes with an emphasis on the need for the 'politics of information' (Lyon, 2007) to catch up with the 'politics of public protection' (Nash, 2010).
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“These works often focus on the difficulties of constraining algorithmic discrimination in many contexts, proposing alternative legal frameworks that would allow for more regulatory enforcement of algorithmic bias.”
From Data Governance in the Age of Large-Scale Data-Driven Language Technology (2022) · cites this paper as [64]
In the citing paper
“A number of prior works, particularly from a U.S.-centric perspective, have connected ML to legal frameworks for human rights, especially antidiscrimination [47, 48, 64, 70, 72, 140, 142, 146].”
From Data Governance in the Age of Large-Scale Data-Driven Language Technology (2022) · cites this paper as [64]
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Cited by
Data Governance in the Age of Large-Scale Data-Driven Language Technology
Jernite, Y., Nguyen, H., Biderman, S., Rogers, A., Masoud, M., Danchev, V., Tan, S., Luccioni, A. S., Subramani, N., Johnson, I., et al. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3534637 · Cited by 75
Metadata
- Authors
- Grace, J.
- Affiliations
- Sheffield Hallam University (GB)
- Corresponding author
- Grace, Jamie
- ORCID
- Grace, Jamie
- Venue
- SSRN Electronic Journal
- Publisher
- Elsevier BV
- Published
- 2019
- ISSN
- 1556-5068 (ISSN-L)
- OpenAlex
- W2990604621
- Cited by
- 8
- References
- 14
- Citations by year
- 2025: 1 · 2024: 1 · 2022: 4 · 2020: 2
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Locations
- SSRN Electronic Journal (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Law › Artificial Intelligence in Law
- Social Sciences › Law › Digitalization, Law, and Regulation
- Keywords
- machine learning, human rights, criminal justice, privacy, freedom of expression, racial discrimination, victims' rights, face recognition, automated algorithm, algorithmic governance, self-regulation, UK policing, information politics
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