Deploying Modern Analytics for Today’s Critical Data Challenges in eDiscovery | Headlight


Although AI tools are no longer new, they are still evolving, and so is comfort and industry confidence in them. To dive deeper into the technology available and how lawyers can use it, Lighthouse hosted an expert panel with Mark Noel, Director of Advanced Client Data Solutions at Hogan Lovells, Sam Sessler, Deputy Director of Global eDiscovery Services at Norton Rose Fulbright, Bradley Johnston, senior counsel eDiscovery at Cardinal Health, and Paige Hunt, vice president of global discovery solutions at Lighthouse.

Some of the key themes and ideas that emerged from the discussion include:

  • Defining AI
  • Meet customer expectations
  • Understand the duty of competence of lawyers
  • Identify critical factors in choosing an AI tool
  • Evaluate the impact of AI on processes and strategy
  • The future of AI in the legal industry

Defining AI

The term “AI” can be misleading. It is important to recognize that at present it is an umbrella term encompassing many different techniques. The most common form of AI in the legal space is machine learning, and the earliest tools were document review technologies in the eDiscovery space. Other forms of AI include deep learning, continuous active learning (CAL), neural networks, and natural language processing (NLP).

While eDiscovery was a testing ground for these solutions, the legal industry is now seeing more prebuilt and portable algorithms used across a wide range of use cases, including data privacy, cybersecurity, and internal investigations. .

Expectations of Clients and Duties of Lawyers

The widespread adoption of AI technologies has been slow, which comes as no surprise to the legal industry. Lawyers tend to be wary of change, especially when it comes to techniques that can be difficult to understand. But our panel of experts agreed that barriers to entry were less of an issue at this point, and now many lawyers and clients expect to use AI.

Lawyers and clients alike have widely adopted AI techniques for electronic discovery and other privacy and security issues. However, customers place less emphasis on technology and more on efficiency. They want their law firms and vendors to deliver the most value for their budgets.

Another client expectation is to reduce risk wherever possible. For example, many AI technologies provide the consistency and accuracy needed to reduce the risk of inadvertent disclosures.

Added to client expectations is the lawyer’s duty to familiarize themselves with technology from a skills perspective. We are not at the point in the legal industry where lawyers violate their duty of competence if they do not use AI tools. However, the technology can mature to the point where it becomes an ethical issue for lawyers not to use AI.

Choose the right AI tool

Decide based on the research task

There is always the question of which AI technology to deploy and when. While less experienced lawyers might assume that the right tool depends on the area of ​​practice, the panelists all focused on the research task. Many of the same research tasks occur in all practice areas and in all companies.

Lawyers need to choose an AI technology that will give them the insights they need. For example, technology-assisted review (TAR) is well suited for document classification, while clustering is useful for exploration.

Focus more on features

Teams should consider the features and insights of different options when purchasing AI for eDiscovery. They should also consider the training protocol, process, and workflow. Ultimately, results must be repeatable and defensible. Several solutions may be suitable as long as the team can apply a scientific approach to the process and perform an early evaluation of the data. Other factors include connectivity with other technologies in the organization and cost.

The process and the results matter most. Lawyers are better off looking at the system as a whole and its features to decide which AI technology to deploy instead of focusing on the algorithm itself.

Although not strictly necessary, it can be helpful to choose a solution that the team can apply to multiple issues and tasks. Some tools are more flexible than others, so reuse is something to consider.

Some use cases allow for experimentation

There is also the choice between a well-established solution and a lesser-known technology. Again, correctness can push a team towards a well-known and respected tool. However, teams can take calculated risks with new technologies when dealing with exploratory and internal tasks.

A custom solution is not necessary

Participants noted more than once the rise of pre-made wearable AI solutions. Rarely will it be beneficial for a team to build a custom AI solution from scratch. There is no need to reinvent the wheel. Instead, attorneys should always try an off-the-shelf system first, even if it requires tweaking or tweaking.

The impact of AI on the process

Process and workflow are key no matter which solution a team chooses. Whether it’s an electronic discovery, internal investigation, or cybersecurity incident, attorneys need accurate and defensible results.

Some AI tools allow teams to track and document the process better than others. However, regardless of the functionality of the tool, lawyers should prioritize documentation. It’s up to them to thoughtfully train the chosen system, create a defensible workflow, and record their progress.

As the saying goes: trash in, trash out. The effort and information the team enters into the AI ​​tool will influence the validity of the results. The tool itself may slightly influence the team’s approach. However, any approach should stem from a scientific process and evidence-based decisions.

The influence of AI on strategy

AI has a lot of potential to help organizations more strategically manage their documents, data, and approach to cases. Consider privileged communications and redactions. Artificial intelligence tools allow organizations to review and classify documents as their employees create them, long before a dispute or other matter arises. Classification coding can travel with the document, from one legal issue to another and even from one provider to another, saving organizations time and money.

Consistency is also relevant. Organizations can use AI tools to improve accuracy and consistency in identifying, classifying, and redacting information. A well-trained AI tool can deliver better results than people who may be inconsistently trained, biased, or distracted.

Another factor is the reuse of AI technology for multiple research tasks. Depending on the tool, an organization can use it multiple times. Or he can use the results from one project to another. This can be like knowing which documents are privileged in advance or having an on-going writing diary. It can also look like using a set of documents to better train the algorithm for the next task.

The future of AI

The panelists concluded the webinar by discussing what they expect for the future of AI in the legal space. They agreed that the ability to reuse work products and the concept of data lakes will become even more of a priority. Reuse can have a significant impact on tasks that have traditionally been a huge financial burden, such as privilege reviews and logs, identifying sensitive data, data breaches, and cyber incidents.

Another likelihood is that AI technology will expand to more use cases. While attorneys tend to use these tools for similar research tasks, the technology itself has potential for many other legal issues, both adversarial and transactional.

To learn more about what the experts had to say, watch the webinar “Deploying Modern Analytics to Meet Today’s Critical Data Challenges”.

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