{"@context":"https://schema.org","@type":"ScholarlyArticle","headline":"Ethical implications related to processing of personal data and artificial intelligence in humanitarian crises: a scoping review","author":[{"@type":"Person","name":"Tino Kreutzer"},{"@type":"Person","name":"James Orbinski"},{"@type":"Person","name":"Lora Appel"},{"@type":"Person","name":"Aijun An"},{"@type":"Person","name":"Jerome Marston"},{"@type":"Person","name":"Ella Boone"},{"@type":"Person","name":"Patrick Vinck"}],"datePublished":"2025","isPartOf":{"@type":"Periodical","name":"BMC Medical Ethics"},"identifier":"https://doi.org/10.1186/s12910-025-01189-2","description":"Abstract\n \n Background\n Humanitarian organizations are rapidly expanding their use of data in the pursuit of operational gains in effectiveness and efficiency. Ethical risks, particularly from artificial intelligence (AI) data processing, are increasingly recognized yet inadequately addressed by current humanitarian data protection guidelines. This study reports on a scoping review that maps the range of ethical issues that have been raised in the academic literature regarding data processing of people affected by humanitarian crises.\n \n \n Methods\n We systematically searched databases to identify peer-reviewed studies published since 2010. Data and findings were standardized, grouping ethical issues into the value categories of autonomy, beneficence, non-maleficence, and justice. The study protocol followed Arksey and O’Malley’s approach and PRISMA reporting guidelines.\n \n \n Results\n \n We identified 16,200 unique records and retained 218 relevant studies. Nearly one in three (\n n\n = 66) discussed technologies related to AI. Seventeen studies included an author from a lower-middle income country while four included an author from a low-income country. We identified 22 ethical issues which were then grouped along the four ethical value categories of autonomy, beneficence, non-maleficence, and justice. Slightly over half of included studies (\n n\n = 113) identified ethical issues based on real-world examples. The most-cited ethical issue (\n n\n = 134) was a concern for privacy in cases where personal or sensitive data might be inadvertently shared with third parties. Aside from AI, the technologies most frequently discussed in these studies included social media, crowdsourcing, and mapping tools.\n \n \n \n Conclusions\n Studies highlight significant concerns that data processing in humanitarian contexts can cause additional harm, may not provide direct benefits, may limit affected populations’ autonomy, and can lead to the unfair distribution of scarce resources. The increase in AI tool deployment for humanitarian assistance amplifies these concerns. Urgent development of specific, comprehensive guidelines, training, and auditing methods is required to address these ethical challenges. Moreover, empirical research from low and middle-income countries, disproportionally affected by humanitarian crises, is vital to ensure inclusive and diverse perspectives. This research should focus on the ethical implications of both emerging AI systems, as well as established humanitarian data management practices.\n \n \n Trial registration\n Not applicable.\n ","url":"https://innscience.ca/es/community/literature/a0aac9ed-9c3a-4c19-8d8e-ae62c0591b82"}