在聊天机器人设计中实现神经网络技术的社会文化和信息安全问题

N. Pokrovskaia
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引用次数: 0

摘要

神经网络是在实际材料上训练的。人们在交流中既使用社会文化的礼貌规则,又使用良好的品味,并根据长期经验和长期风险预测,考虑到保护数据的威胁。人类行为为神经网络提供了适当的行为和礼仪的例子,这些例子取决于上下文的有限应用,以及不可接受和不可接受的行为,例如粗鲁的情绪释放或相关元数据中的信息泄露。基于对聊天机器人实施和功能的几个案例的分析,显示了信息安全领域的主要伦理问题组和主要任务,确定了确保礼仪和数据保护的关键方法,并为企业生态系统中与聊天机器人相关的机器学习程序制定了建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sociocultural and Information Security Issues in the Implementation of Neural Network Technologies in Chat-bots Design
Neural networks are trained on actual material. People in their communication use both socio-cultural rules of courtesy and good taste, and take into account threats to protect data, based on long-term experience and long-term risk prediction. Human behavior provides neural networks with examples of both appropriate behavior and manners with limited application depending on the context, as well as unacceptable and unacceptable behaviors, such as rude emotional discharge or information disclosure in associated metadata. Based on the analysis of several cases of the implementation and functioning of chatbots, the main groups of ethical problems and the main tasks in the field of information security are shown, key approaches to ensuring etiquette and data protection are identified, and proposals are formulated for procedures for machine learning in relation to chatbots in corporate ecosystems.
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