Corpse identification in mass disasters and other violence: the ethical challenges of a humanitarian approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.077 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it