Using Artificial Intelligence Tools to R

Using Artificial Intelligence Tools to Run Proactive “Health Check” Investigations

Using Artificial Intelligence Tools to Run Proactive “Health Check” Investigations

 

In the legal world, and especially the planet of electronic discovery, AI (AI) has been around for quite a decade. It’s not unusual or controversial for organizations to use AI technologies in litigation, especially where large or complex data sets are involved. Legal teams now routinely address AI to defensibly accelerate the method of identifying documents likely to be aware of requests for evidence. Innovations like technology assisted review (TAR), for instance, rely heavily on machine learning and tongue processing to form connections and identify patterns within a body of knowledge during a matter of seconds. This is often work that might take even the foremost qualified human reviewers many, many hours to try to manually, and with less accuracy. Apart from sheer computing power, one among the foremost useful features of AI technology like machine learning is its ability to quickly “learn” and continuously improve the accuracy of its outputs with the essentially passive assistance of human reviewers. In continuous active learning (CAL), now a feature of leading eDiscovery platforms, even the method of “training” machines to seek out what you’re trying to find is performed algorithmically with no direction from human document reviewers beyond the coding or labeling they perform within the process of manual review. This is often a remarkably efficient and cost-effective thanks to teach machines to spot responsive information, and its enormous potential for other vital corporate functions.

A notable example is compliance.

The usefulness of active learning as a proactive compliance and knowledge governance tool has only recently begun to be explored and appreciated. Across the company landscape, reactive approaches to potential problems hidden in data stores are much more common—and ultimately more costly and risky. Companies will typically wait until a whistleblower complains or an employee happens upon a possible problem, then respond by launching an indoor investigation.

AI technology can help your organization avoid this scenario. You can use it to:

Look for potential “privacy holes” in your data. This is especially relevant as national and local governments enact regulatory frameworks like the recently passed California Consumer Privacy Act (CCPA) and the EU’s General Data Protection Regulation (GDPR). If your organization is sharing information between a California office and a replacement York office, as an example, you'll use AI to run regular checks to spot and flag transfers of knowledge which will contain personally identifiable information (PII).

  • This would be particularly relevant for organizations working with distributors or resellers across multiple national or international jurisdictions. Proactively identify potential issues in the HR domain, such as mental distress, inappropriate or offensive messages, a rapid decline in employee performance, or perhaps a misuse of corporate accounts.

  • You can also use AI to efficiently collect and analyze information from custodians who are the foremost likely to be involved during a particular instance of potential misconduct. Identify data security weaknesses or potential data breaches before they explode into an existential crisis that poses a significant financial, legal and reputational threat to the corporate.

  • This is especially relevant within the context of recent pandemic-related work-from-home mandates, which have introduced new security vulnerabilities as more data is accessed and transmitted across multiple locations with multiple devices, including employees’ personal devices. Address and correct instances of human error due to poor due diligence, ineffective processes, lack of training, or other systemic shortcomings.

  • At large financial institutions, for instance, compliance officers must monitor massive amounts of knowledge associated with transactions, customers, and operations. AI is very good at identifying anomalies, oversights and outright calculation errors in data that might otherwise be missed. Analyze the workflows and work product of compliance officers, use that information to categorize those activities and the associated data, and alert those officers to impending deadlines, updates, events, and other time-sensitive tasks.

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