The National Archives and Records Administration (NARA) recently published a white paper analyzing the impact of “cognitive technologies”, including artificial intelligence (AI), machine learning (ML), robotic process automation (RPA ), the Internet of Things (IoT), and other emerging technologies, on document management and data governance. The exponential growth of data and the continued development of technologies designed to support or replace human decision-making have had a profound impact on how we work and on the records and information we create, use and retain to meet changing needs. of the company.
There are two important areas to consider highlighted by this article:
- The potential of these technologies to assist and contribute to the records management function through the automation of business processes, such as scanning, classification, access to information, assignment of retention rules and automation of the destruction or transfer of documents and information.
- The potential need for data created by these technologies to be captured and retained as required within an organization’s information governance framework. For example, predictive information or output data generated by cognitive technologies that documents transactions or business decisions can be considered records, requiring organizations to manage them throughout their lifecycle.
Implementing these technologies can also come with challenges, and organizations must be prepared to meet them. For example:
- Biases and ethical concerns related to algorithms used in AI and ML. Consideration of biases and ethical implications should be included in development and training processes. One way to address this issue is to establish data standards to ensure the use of appropriate and representative data.
- The fluid nature of data can sometimes make it difficult to enforce record retention requirements. One way to solve this problem is to consider planning the systems containing the data rather than the data itself.
- Guarantee the authenticity and integrity of documents and information. The current technical environment presents a number of risks that must be analyzed and managed to ensure that appropriate data security measures are in place. One such risk is the potential for manipulation of machine learning systems, compromising results at the time of creation and undermining the authenticity and integrity of records.
The growing reliance and use of cognitive technologies in the workplace highlights the importance of collaboration between document and information managers and other people in their organizations, such as data scientists, systems engineers and compliance teams. Working together will help ensure that systems are configured appropriately to meet both business needs and legal obligations, and that the records created through these technologies, such as the algorithms and datasets that result, are integrated into their organization’s information governance framework.
Although the document focuses on federal records, many of the same principles, challenges, and concerns will apply to all organizations using these and similar technologies. The continued development of legislation, guidelines and standards surrounding cognitive technologies further underscores the need to focus on these areas from a legal compliance and information governance perspective within organizations seeking to leverage part of their abilities.
As new ways to create and capture data are created, data management often lags behind. While the volume can be staggering, the data must be managed by agencies within a records management framework. The retention period continues to depend on business needs and legal requirements. Often large data collections containing personally identifiable information and other sensitive information are gathered for analysis and use. Maintaining data provenance through metadata and other methods will be essential to ensure privacy and security requirements are met.
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