then be complemented with real-world data for the contextual evaluation of the models, thus 8 EC, Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with providing insights into their performance under regard to the processing of personal data and on the free movement of actual operating conditions. [...] As such, the intersection between AI and data remains very while falling outside of the military domain, much underexplored in the humanitarian context, the development, adoption and integration of calls for concrete approaches and solutions to AI solutions add further layers of complexity to address the issues it raises are growing (Spencer ensuring the security and resilience of CNIs, for 2024). [...] In the case of the training and testing of data sets’ quality, which, GDPR, for example, and as discussed earlier, it is ultimately, will have an impact on the reliability clear that the processing of data for security and and performance of the AI system in question. [...] In the same vein, governance efforts to foster data equity in the civilian space There is evidence of the benefits of enabling are seeking to adopt a similar approach.32 Yet in inclusive and meaningful multi-stakeholder the military domain, while transparency lies at cooperation to support and advance AI governance, the heart of efforts and initiatives to promote including in security and defence. [...] leveraged in the civilian sphere, such as PETs, Data Equity federated learning and blockchain technologies, can not only promote privacy in data trading As discussed above, responsible AI is currently processes but also be used as a means of tracing at the core of many of the discussions on the use the source and transfer history of the traded data of AI in the military domain.
Mentioned Organizations
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- Canada
