Ten Use Cases for Wearable AI Models | JD Supra

In recent years, there has been a noticeable shift in the legal community, moving from reluctance to use emerging technologies to embracing modern tools that can optimize processes and improve cost management. Legal teams that were once set in their ways and skeptical of technology are now looking for automated tools that can improve efficiency across the board. Wearable AI models are the new tool on the scene that can help not only in eDiscovery examination, but much more. These tools reuse prior knowledge to re-run the exam, eliminating the need to create and train a new model each time a similar problem or question arises.

The first type available is a predefined model that can help identify language in datasets on recurring topics such as privileged content or insulting behavior. The second type is a custom tailored model trained to identify issues or answer questions unique to a particular organization. Typically, a service provider has a library of pre-made templates on common topics that customers can easily use. Custom models will require working with a service provider to train unique models in-house. Both types of wearable AI models have potential for continued evolution and can be a key value driver for legal teams.

Use case

Use cases for wearable AI models are growing, the technology is more powerful than ever, and applications can extend beyond the legal department. Current knowledge of the number of available use cases is lacking, but that will absolutely change as adoption increases. This can only happen with more education opportunities on the app and the potential for return on investment.

Here are ten use cases where AI models can prove useful in litigation, investigations and beyond:

Data culling for document review

  1. Review of privileges: During the eDiscovery process, the privilege review phase can be tedious. Applying a predefined template from a trained provider to target privilege language can be a big help in the privilege identification and drafting process. This will streamline eDiscovery review while maintaining privacy where appropriate.
  1. Identification of sensitive data: A model targeting certain words or phrases that indicate misconduct has proven particularly effective in employment disputes. For example, in a case of sexual harassment, teams can apply AI models on communication data to identify sexually explicit themes, concepts, and language. This software can also detect comments about appearance, bullying, discrimination, harassment and/or threatening behavior. This can help parties get the examination started by identifying key players and witnesses earlier.
  1. Litigation risk analysis: Teams can apply wearable models even before reaching the eDiscovery phase as another way to perform early case assessment and make settlement decisions. Using the employment situation discussed above, having the ability to run a pre-built responsive language model during the investigation phase could save an organization the expense of moving forward with a case if it is more suitable for settlement or dismissal.
  1. Identify valuable keywords: This is an illustration of how layering technology can yield more efficient results. The legal team can first use a predefined template to determine the optimal keywords. Then they can use the keywords in conjunction with other tools to further extract the dataset and extract what is needed for manual review.
  1. Custodian identification: A difficult and time-consuming part of eDiscovery can be identifying the location of relevant data. While there are other technologies as well as information governance strategies that can aid in this feat, the use of a wearable AI model is just another beneficial tool to explore. This application is especially useful when organizations have created bespoke templates that have already been customized to accommodate internal workflows and unique data repositories.

Regulatory compliance functions

  1. Data disposal: Similar to litigation, the predefined and custom AI models will be useful during a regulatory investigation to weed out cumbersome datasets. Many regulators are imposing stricter deadlines, requiring tools that can expedite review to stay compliant. It is also an effective way to reduce costs, as survey budgets are generally smaller.
  1. Internal investigations: Teams can deploy models that will assign a sentiment score to prioritize evidence hotspots or detect fraudulent behavior that would increase compliance risk. For example, a model focused on bribery, insider trading, or related topics can help detect fraudulent patterns that are investigated internally. By running a predefined model on the data, teams can discover which custodians are using words and phrases indicative of fraudulent behavior so they can act quickly.
  1. DSAR compliance: Under the GDPR, consumers can request access to see how organizations use their data. Since a fast turnaround time is needed, a pre-trained AI model for identifying sources of personal information (which can take many forms) can help teams achieve full compliance quickly.
  1. Internal behavior monitoring: This application is beneficial in the financial services sector. Management can use a model to monitor employee behavior to ensure that they are acting appropriately and not promising their clients unattainable rates or assets.

Response to Data Breaches

  1. Post-offence analysis: Applying an AI model after a breach has occurred can help determine who to notify and where sensitive data resides. Time is of the essence in these situations, so being able to quickly apply a tool like this will greatly help mitigation efforts.


Wearable AI models are the new technology tools to watch. Use cases and maturity will only expand as more organizations become aware of how these models work and the benefits they can provide to legal and other departments. It is a tool that not only saves time and money, but also promotes efficiency and consistency. Now is the time to monitor new industry and court developments, evaluate investment opportunities with vendors offering pre-built or bespoke models, and discuss potential use cases with teams. of management.

To learn more about wearable AI models, consider reading our recent white paper on the subject.

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