TY - JOUR
T1 - Introducing a predictive experiment
T2 - Ethics, data and public defence in Chile
AU - Arriagada, Arturo
AU - Cotoras, Dusan
N1 - Publisher Copyright:
© The Author(s) 2025. This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
PY - 2025/10/1
Y1 - 2025/10/1
N2 - This article examines the development of a predictive system intended to forecast favourable trial outcomes within Chile's Public Defender's Office, analysing how ethical principles are negotiated, contested, and transformed throughout its design and implementation. Drawing on six months of ethnographic fieldwork - including participant observation, planning meetings, and interviews with developers, officials, and public defenders - the study shows that ethics does not operate as a stable, universal framework. Instead, it emerges as a situated ‘matter of concern' shaped by institutional asymmetries, competing professional repertoires, and the constraints of public-sector infrastructures. While Chile's AI governance frameworks endorse principles such as fairness, accountability, and transparency, the translation of these ideals into practice generated frictions across three phases: problem identification, operationalization through ‘business rules’, and system rollout. Developers approached the project as a technical classification task; officials framed it as a managerial instrument for audit and optimization; defenders perceived it as a mechanism of surveillance that failed to capture the complexity of legal practice. These divergent imaginaries exposed the limits of abstract ethical guidelines and the risks of ethics-washing in public administration. By foregrounding everyday negotiations, the article argues for context-sensitive, participatory approaches to AI governance that extend beyond compliance with high-level ethical principles.
AB - This article examines the development of a predictive system intended to forecast favourable trial outcomes within Chile's Public Defender's Office, analysing how ethical principles are negotiated, contested, and transformed throughout its design and implementation. Drawing on six months of ethnographic fieldwork - including participant observation, planning meetings, and interviews with developers, officials, and public defenders - the study shows that ethics does not operate as a stable, universal framework. Instead, it emerges as a situated ‘matter of concern' shaped by institutional asymmetries, competing professional repertoires, and the constraints of public-sector infrastructures. While Chile's AI governance frameworks endorse principles such as fairness, accountability, and transparency, the translation of these ideals into practice generated frictions across three phases: problem identification, operationalization through ‘business rules’, and system rollout. Developers approached the project as a technical classification task; officials framed it as a managerial instrument for audit and optimization; defenders perceived it as a mechanism of surveillance that failed to capture the complexity of legal practice. These divergent imaginaries exposed the limits of abstract ethical guidelines and the risks of ethics-washing in public administration. By foregrounding everyday negotiations, the article argues for context-sensitive, participatory approaches to AI governance that extend beyond compliance with high-level ethical principles.
KW - Ethics
KW - Science and Technology Studies
KW - artificial intelligence
KW - data governance
KW - ethics-washing
KW - ethnography
UR - https://www.scopus.com/pages/publications/105025157734
U2 - 10.1177/20539517251407984
DO - 10.1177/20539517251407984
M3 - Article
AN - SCOPUS:105025157734
SN - 2053-9517
VL - 12
JO - Big Data and Society
JF - Big Data and Society
IS - 4
ER -