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SUMMARY:Automating International Human Rights Adjudication - Dr Veronica F
 ikfak (Political Science\, UCL)\, Professor Laurence R. Helfer (Law\, Duke
  University)
DTSTART;VALUE=DATE-TIME:20240306T123000Z
DTEND;VALUE=DATE-TIME:20240306T133000Z
UID:https://talks.ox.ac.uk/talks/id/3e287e0c-9710-4af8-b2a6-b0c372161b0a/
DESCRIPTION:International human rights courts and treaty bodies are increa
 singly turning to automated decision-making (ADM) technologies to expedite
  and enhance their review of individual complaints. Algorithms\, machine l
 earning\, and AI offer numerous potential benefits to achieve these goals\
 , such as improving the processing and sorting of complaints\, identifying
  patterns in case law\, enhancing the consistency of decisions\, and predi
 cting outcomes. However\, these courts and quasi-judicial bodies have yet 
 to consider the many legal\, normative\, and practical issues raised by th
 e use of different types of automation technologies for these purposes. \n
 \nThis article offers a comprehensive and balanced assessment of the benef
 its and challenges of introducing ADM into international human rights adju
 dication. We reject the use of fully automated decision-making tools on le
 gal\, normative\, and practical grounds. In contrast\, we conclude that se
 mi-automated systems—in which ADM makes recommendations that judges\, tr
 eaty body members\, and secretariat or registry lawyers can accept\, rejec
 t or modify—is justified provided that judicial discretion is preserved 
 and cognitive biases are minimised. \n\nApplying this framework\, we find 
 a strong case for using ADM to digitize documents and for internal case ma
 nagement purposes\, such as assigning complaints according to expertise. W
 e also endorse the use of facilitated ADM to make straightforward recommen
 dations regarding registration\, inadmissibility\, and the calculation of 
 damages. Conversely\, we reject the use of algorithms or AI to predict whe
 ther a state has violated a human rights treaty. In between these polar ca
 tegories we discuss semi-automated programs that cluster similar cases tog
 ether\, summarize and translate key texts\, and recommend relevant precede
 nts. The benefits of introducing these tools to improve international huma
 n rights adjudication need to be weighed against the challenges posed by t
 wo types of cognitive biases—biases inherent in the datasets on which AD
 M is trained\, and biases arising from interactions between humans and mac
 hines. \n\nWe also introduce a framework for enhancing the accountability 
 of ADM in international human rights adjudication. This includes public re
 view\, consultations\, and external oversight before automation tools are 
 adopted\, as well as systemic and case-specific explanations about how the
  tools have been deployed in individual cases. Concerns about the ability 
 of humans to meaningfully supervise machine learning and AI programs also 
 raise questions about revisiting the finality of international decisions m
 ade with the assistance of ADM. \n\nSpeakers:\nDr Veronica Fikfak (Politic
 al Science\, UCL)\, Professor Laurence R. Helfer (Law\, Duke University)
LOCATION:Please register to receive venue details
TZID:Europe/London
URL:https://talks.ox.ac.uk/talks/id/3e287e0c-9710-4af8-b2a6-b0c372161b0a/
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DESCRIPTION:Talk:Automating International Human Rights Adjudication - Dr V
 eronica Fikfak (Political Science\, UCL)\, Professor Laurence R. Helfer (L
 aw\, Duke University)
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