Advancing honour and dignity in death for victims of armed conflicts: Exploring the challenges and opportunities of AI and machine learning in humanitarian forensic action under IHL.
Published In: International Review of the Red Cross, 2024, v. 106, n. 926. P. 760 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Madziwa, Edward 3 of 3
Abstract
With technological developments presenting tremendous opportunities, rapid developments in data-driven artificial intelligence (AI) and machine learning (ML) have the potential to significantly transform humanitarian forensic action. Yet, their role in the forensic identification of dead bodies remains unexamined. The correct and early identification of dead bodies is not only important to afford the deceased their honour and dignity and to ensure that their families know the fate of their loved ones, but also has broader implications for human rights and international humanitarian law (IHL). This article examines the opportunities and challenges of AI and ML in advancing honour and dignity in death for armed conflict victims in humanitarian forensic action under IHL. It argues that the application of AI and ML in humanitarian forensic action has the potential to revolutionize and support forensic practitioners in the identification of human remains. This will consequently guarantee that families know the fate of their loved ones and that the deceased are afforded dignified burials according to their religious and cultural rites. The article proposes recommendations for the future use of AI and ML in humanitarian forensic action, including the adoption of a legally binding international instrument governing their use, the development of guidelines for their use, the training of forensic actors in IHL and human rights law, and the use of new technologies in humanitarian action. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:International Review of the Red Cross. 2024/08, Vol. 106, Issue 926, p760
- Document Type:Article
- Subject Area:Applied Sciences
- Publication Date:2024
- ISSN:1816-3831
- DOI:10.1017/S181638312400033X
- Accession Number:181735416
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