Corpse identification in mass disasters and other violence: the ethical challenges of a humanitarian approach
Bibliographic record
Abstract
In October 2022, the Centre for Ethics of Yenepoya University hosted a national workshop entitled: "Respect for human dignity of the unidentified dead from mass disasters and other violence: strategies for the ethical management of biological samples and personal data". The aim was to explore and share experience and ethical considerations regarding the management and identification of human remains in the event of disasters, with the purpose to arrive at a general consensus about what constitutes the ethical foundation of the management of unidentified human remains in forensic practice and, in particular, contextualizing this in India. The main ethical consideration that emerged was tracing the missing and identifying the dead are crucial to maintaining or restoring basic human rights and responsible relief activities. Identification is not only an organizational and scientific achievement but, regardless of circumstances, also necessarily and always an activity with significant political, epistemic, and philosophical relevance and consequence. In India, it could be important to consider new legal provisions for the management of human samples so that this would provide a starting point for the treatment of human remains managed for forensic purposes with uniformity in the country. Another important step in which governments should take part regards the involvement and education of the general public to develop their interest in this important goal. In the field of forensic anthropology, artificial intelligence can support, through the use of algorithms, the decision-making process that leads to the identification of the victim or its remains. Furthermore, they can be used to extract new knowledge from huge databases and shorten identification through computer automation of data binding activities. Applying artificial intelligence tools in forensic sciences to collect new information from massive datasets to enhance knowledge, and reduce human subjectivity and errors, provides a greater scientific basis that could improve the strength of the evidence and support the admissibility of expert evidence. In light of the general lack of national/international guidance about ethical oversight for identification and care of human remains, the fact that regulations are frequently not adequate to govern ethical aspects, we hope that an internationally recognized body should develop such guidance in collaboration with relevant organizations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.047 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.059 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.015 | 0.019 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".