Detecting violations of data sharing agreements

Malatest及其主要负责人经常咨询政府和行业机构，这些机构正试图**与合作伙伴达成数据共享协议。数据是一种具有经济和社会价值的资产。然而，一旦**数据被共享，就不会有任何过程或尝试来验证共享协议是否得到遵守。几乎没有**有效的数据使用审计，主要是因为许可方不知道如何进行，也没有工具或**访问数据接收方分析平台来进行检查。因此，数据共享协议的制定非常耗时，而且大多数情况下，这些资产仍未得到充分利用。资产共享协议和数据**监控要求已经明确。**公司特有的问题有两个：第一，如何将自然语言数据共享协议**转换为计算机能够读取的象征性逻辑表示；第二，通过应用人工智能和机器学习技术和技术，确定是否**为数据集中的操作提供**日志，以**实现许可合规过程的自动化。**成功的解决方案将提供对数据共享合作伙伴遵守许可协议**条款的高度信心。这反过来将鼓励更多的数据共享。数据共享，特别是在**公共机构中的数据共享，被广泛认为对加拿大全面参与数据驱动的未来很重要。

Malatest and its key principals frequently consult with government and industry agencies which are attempting**to reach data sharing agreements with partners. Data is an asset with economic and social value. However, once**data is shared there is no process or attempt to verify that sharing agreements are being followed. Almost no**effective data usage auditing is done, mostly because licensors don't know how, and haven't got the tools or**access to data recipient analysis platforms to do inspections. As a result, data sharing agreements are**time-consuming to formulate, and most often, these assets remain underused. Asset sharing protocols and data**monitoring requirements have been specified.**The company specific problem is twofold: First, how to transform a natural language data sharing agreement**into a symbolic, logical representation capable of being read by a computer and secondly determine if provided**with a log of actions within a dataset the application of AI and machine learning techniques and technology to**allow automation of the process of license compliance.**A successful solution will provide a high degree of confidence that data sharing partners are abiding by the**terms of the licence agreement. This in turn will encourage more data sharing. Data sharing, particularly in**public agencies, is widely recognized as important for Canada to fully participate in the data-driven future.