Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 8Ethics and Social Impacts of AIArtificial Intelligence in LawDigitalization, Law, and Regulation

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 access
Grace, Jamie·2019·SSRN Electronic Journal
DOI 10.2139/ssrn.3487454
Open this paper in CoChat — free account

Abstract

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).

Overview

Integrity signals

Registry

Crossref · 10.2139/ssrn.3487454

Cites

14 of 14 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Research works printed but not linked: 51

  1. Anonymous, 2019, My Sexual Assault Case Was Dropped When I Refused to Give Police My Phone | Anonymous, The Guardian. Guardian News and Media
  2. Philip Alston, 2019, How Britain's Welfare State Has Been Taken over by Shadowy Tech Consultants | Philip Alston and Christiaan Van Veen, The Guardian. Guardian News and Media
  3. Caelainn Barr, 2019, People Who Report Rape Face 'Routine' Demands for Their Mobile Data, The Guardian. Guardian News and Media
  4. Caelainn Barr, 2019, Police Face Legal Action over Requests for Rape Complainants' Data, The Guardian. Guardian News and Media
  5. Bbc, 2019, Automated Facial Recognition Trials Backed by Home Secretary, BBC News. BBC
  6. Natasha Bernal, 2019, Facial Recognition to Be Used by UK Police to Find Missing People, The Telegraph. Telegraph Media Group
  7. R Booth, 2019, Police face calls to end use of facial recognition software
  8. Owen Bowcott, 2019, Explosion' in Digital Evidence Has Left CPS Struggling, Says Union, The Guardian. Guardian News and Media
  9. Owen Bowcott, 2018, Police Trial AI Software to Help Process Mobile Phone Evidence, The Guardian. Guardian News and Media
  10. Centre for Data Ethics and Innovation). 2019, Landscape Summary -Bias in Algorithmic Decision-Making
  11. Centre For Women's Justice, 2019, police-failure-to-use-protective-measures-in-cases-involving-violence-againstwomen-and-girls City of Oakland Privacy Advisory Commission
  12. Lizzie Dearden, 2019, Police Testing Technology To 'Assess The Risk Of Someone Committing A Crime
  13. Vikram Dodd, 2019, Police Resources 'Drained To Dangerously Low Levels', Say Former Top Officers, The Guardian
  14. Vikram Dodd, 2019, Woefully Low': Cressida Dick Calls For Action On CrimeSolving Rates". The Guardian
  15. David Edmonds, 2019, Could An Algorithm Help Prevent Murders, BBC News
  16. 2019, Essex Centre for Data Analytics (Essex Partnership)
  17. Cyrus Farivar, 2018, Habeas data: Privacy vs. the rise of surveillance tech
  18. Carol Harlow, 2019, Proceduralism and Automation: Challenges to the Values of Administrative Law
  19. 2018, ICO (Information Commissioner's Office)
  20. 2018, Processing Gangs Information: A Checklist For Police Forces, Ico.Org.Uk
  21. ICO (Information Commissioner's Office). 2019a. Ico
  22. ICO (Information Commissioner's Office). 2019a. Ico
  23. 2019, Inspector General LA Police Commission, Lapdpolicecom.Lacity.Org
  24. Joshua Jowitt, 2019, Ian Mcewan's Machines Like Me And The Thorny Issue Of Robot Rights, The Conversation
  25. Naomi Larsson, 2019, It's An Educational Revolution': How AI Is Transforming University Life, The Guardian
  26. Dave Lee, 2019, An Algorithm Wipes Clean The Criminal Pasts Of Thousands, BBC News
  27. Sarah Marsh, 2019, Ethics committee raises alarm over 'predictive policing' tool, The Guardian
  28. Tamlin Magee, 2019, Cardiff University's Hatelab Unearths Online Hate Speech Using AI
  29. Steve Mckevitt, 2019, How the power of persuasion goes way beyond mere advertising, The Conversation
  30. 2018, MOPAC (Mayor's Office for Policing and Crime), Review of the Gangs Matrix
  31. Chi Onwurah, 2019, Chi Onwurah: We must end the use of biased algorithms to process visa applications with no transparency, PoliticsHome. Electronic copy
  32. Kari Paul, 2019, San Francisco is first US city to ban police use of facial recognition tech, The Guardian
  33. Mark Puente, 2019, LAPD to scrap some crime data programs after criticism, Los Angeles Times
  34. Mark Puente, 2019, LAPD pioneered predicting crime with data. Many police don't think it works, Los Angeles Times
  35. K Rawlinson, 2019, ICO opens investigation into use of facial recognition in King's Cross. The Guardian
  36. Rashida Richardson, 2019, Dirty Data, Bad Predictions: How Civil Rights Violations Impact Police Data, Predictive Policing Systems, and Justice, New York University Law Review
  37. 2019, RUSI partners with the Centre for Data Ethics and Innovation, RUSI
  38. Ian Sample, 2019, South Wales police to use facial recognition apps on phones, The Guardian
  39. Sebastian Schwiddessen, 2018, Germany's Network Enforcement Act -closing the net on fake news, E.I.P.R, 40, 539
  40. Sophie Stalla-Bourdillon, 2019, Data protection by design and data analytics: can we have both?, Privacy & Data Protection, 19, 8
  41. Kai Strittmatter, 2019, We Have Been Harmonised: Life in China's Surveillance State
  42. 2019, The Police Use of Automated Facial Recognition Technology with Surveillance Camera Systems
  43. 2019, As face-recognition technology spreads, so do ideas for subverting it, The Economist
  44. Chris Todd, 2019, West Midlands Police Head of Professional Standards and NPCC Lead for Data Analytics), Policing Insight
  45. Mark Townsend, 2019, Police forces halt trials of facial recognition systems, The Guardian
  46. Mark Townsend, 2019, Black people '40 times more likely' to be stopped and searched in UK, The Guardian
  47. Tom Wall, 2019, I'm 57 and my parents have to feed me': the universal credit digital obstacle course, The Guardian
  48. Wmpcc, 2019, Committee meeting minutes from April 2019 for the WMPCC Independent Data Analytics Ethics Committee discussions of the "Integrated Offender Management" (IOM) tool
  49. Paul Wiles, 2018, Commissioner for the Retention and Use of Biometric Material: Annual Report, Her Majesty's Stationery Office
  50. Rebecca Williams, 2019, Accountability key to the adoption of surveillance technology
  51. Tom Winsor, 2018, Her Majesty's Inspectorate of Constabulary and Fire and Rescue Electronic copy

Cited by

See all 8

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)
DOI
10.2139/ssrn.3487454
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
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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.