The Information Governance Initiative points out that “There is a deep connection between improving electronic recordkeeping practices throughout government and improving public access to government records through the mechanism of the Freedom of Information Act (FOIA).”
We agree. Government agencies are now and have for a long time been at work to solve important records management problems. When they find success, they’ll also alleviate issues that cause delayed, incomplete, and inappropriately denied FOIA and public records disclosures. These challenges include the need to optimize records disclosure processes with:
The 2012 Managing Government Records Directive required the National Archives and Records Administration (NARA) to provide a comprehensive plan describing “suitable approaches for the automated management of email, social media, and other types of digital record content, including advanced search techniques.” In 2014, NARA released its Automated Electronic Records Management Report/Plan.
NARA discovered that most federal agencies are not consistent in their recordkeeping practices because they rely on individual staff members to capture and categorize electronic records, “if they are managing electronic records at all”. Inconsistency is intolerable in records management. NARA said also that these manual processes cannot scale to manage the volume of email, social media, and other electronic records created now.
A records retention approach NARA found more consistent and that also scales easily is an automated process called “autocategorization.” In this approach, machine learning and predictive coding are applied to automatically identify and categorize records through algorithms that have learned to recognize certain patterns. These same proven techniques are used in the legal field during the eDiscovery process. They are specifically designed to effectively manage massive amounts of records and files and deliver reliable results.
NARA said in the report said the automated approach can categorize records from unstructured business processes, including email, with a “high degree of sophistication” and “The advanced search space, including machine learning or predictive coding as used in eDiscovery, is one of several promising areas for records management exploration.”
Records that are categorized based on their contents are retained and retrieved more easily and effectively. In essence, automation equals speed and accuracy. When applied to centralized, standardized records management systems, the much-sought-after speed and accuracy of automation are delivered even when processing videos, audio instant messages, social media, handwritten text, blueprints, and so on.
NARA’s conclusions encourage the adoption of automation to reduce the burden on staff. Three positive effects NARA found are: 1) records are more consistently captured and managed and therefore more accessible for support of the agency mission; 2) processes can scale up to handle a higher volume of information; and 3) staff members have more time available for the agency mission.
The automated approach described by NARA includes techniques that nearly all federal, state, county, and city government agencies can employ not just in their records management systems but also their public records disclosure practices.
What’s more, automation itself is just the tip of the iceberg. eDiscovery platforms offer the ability to use other techniques such as auto-redaction, topic modeling, and advanced search and filtering capabilities that deliver more thorough, more accurate, and faster public records disclosures.
Download the white paper Take Control of Public Records Requests: A Maturity Model for a closer look at how the automated approach shared by NARA and other advanced technologies benefit government agencies in responding to public records requests.