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AI in E-Discovery: Transforming Document Review and Litigation Support

KEY TAKEAWAYS

AI is transforming document review and litigation support in e-discovery, improving efficiency, accuracy, and cost-effectiveness. Technology Assisted Review (TAR) prioritizes relevant documents, reducing time and effort. Challenges include obtaining quality training data, lack of AI transparency, adapting to e-discovery changes, and addressing ethical/legal concerns. Nonetheless, AI offers promising solutions for managing large volumes of electronic data in legal proceedings.

In today’s digital world, electronic information plays a crucial role in the legal process. However, the increasing amount of electronic data is making it difficult for legal professionals to search, analyze, and present information during litigation.

Fortunately, advances in artificial intelligence (AI) have made it possible for documents to be reviewed more effectively. These advancements are transforming the review of documents in litigation or investigation, saving time, reducing costs, and improving effectiveness.

In this article, we take a close look at how AI is transforming document review and litigation support.

What Is E-Discovery?

Electronic discovery, or e-discovery, refers to the process of identifying, collecting, reviewing, and producing electronic information as evidence in legal proceedings.

Today, much of our information is stored electronically, such as emails, documents, databases, and other digital files. The process of e-discovery begins when there is a need to gather electronic evidence for a legal case. This involves searching and retrieving relevant electronic data from various sources, such as computer systems, servers, email archives, cloud storage, social media, and more.

Once the data is collected, it goes through a series of steps to organize, filter, and analyze the information. Specialized software and tools are used to help with this process, allowing legal teams to search for specific keywords, dates, or file types to narrow down the data set. This helps to find the most relevant information for the case.

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Once the review and analysis of the data are complete, legal proceedings can utilize the selected documents or files as evidence. This could include presenting them in court or submitting them to the opposing party.

E-discovery is crucial in modern litigation because it enables the efficient handling of large volumes of electronic information that would be impractical to review manually.

Challenges with Traditional E-Discovery

E-discovery is primarily performed using keyword searches in order to narrow down the documents collected for legal review.

While this helped in reducing the number of documents for review, the approach had several shortcomings:

  • Inefficiency in identifying relevant documents;
  • Legal experts have to review potentially irrelevant data, making it a costly and time-consuming process;
  • Keyword searches focus on finding evidence rather than understanding context and meaning;
  • Growing volumes of electronically stored information make it hard to access relevant information.

AI and Legal Domain

In recent years, AI has made remarkable progress in text understanding and generation. These advancements are driven by improvements in how AI models are designed and trained. One key factor is the use of transformer architectures, which help create more powerful pre-trained models (trained on huge text data) that can be used for various tasks and offer impressive performance even with smaller datasets.

Moreover, the development of user-friendly APIs (Application Programming Interfaces) has made it convenient to build new applications with these advanced AI models, even for people with little or no coding experience.

These developments have caught the attention of legal professionals and AI researchers, as they see many opportunities to automate repetitive tasks in the legal field. Tasks like reviewing documents, analyzing contracts, and conducting legal research can be time-consuming and require a lot of effort.

By leveraging AI technologies, organizations can achieve greater efficiency and alleviate the burdens associated with these tasks.

Technology-Assisted Review in E-Discovery

To tackle the challenges of e-discovery and harness the advantages of AI’s recent advancements, a new approach called Technology Assisted Review (TAR) has emerged in e-discovery. TAR utilizes AI algorithms to analyze and categorize large volumes of electronic documents based on their relevance to a legal case. The process entails training the AI algorithms using a subset of documents that legal experts have manually reviewed and tagged as either relevant or non-relevant. The algorithms learn from these human decisions by identifying patterns and characteristics associated with the relevant documents.

Once the training phase is complete, the AI algorithms apply their learned knowledge to rank the remaining un-reviewed documents according to their relevancy. This ranking allows legal professionals to focus their efforts on the most relevant documents, thereby reducing the need for exhaustive manual review of a large number of documents.

Advantages of AI in E-Discovery

TAR’s use of AI brings several advantages to the e-discovery process:

Reduce time and effort

TAR could significantly reduce the time and effort required for document review. Instead of reviewing an extensive collection of documents, TAR enables legal teams to prioritize their efforts on the subset of documents that have a higher probability of relevance.

This saves considerable time and resources, allowing for a more efficient review process.

Improve accuracy

TAR could improve the accuracy of document reviews by leveraging the power of AI algorithms. These algorithms can analyze complex patterns and relationships within the data, going beyond simple keyword matching.

As a result, traditional methods may overlook critical evidence, whereas TAR reduces the risk of missing it.

Ensure fairness and reliability

TAR provides a consistent and standardized approach to document review. Unlike human reviewers, who may introduce inconsistencies or biases, AI algorithms apply consistent criteria throughout the process, ensuring fairness and reliability in identifying relevant documents.

Challenges of AI in E-Discovery

Besides many advantages, TAR is not without challenges:

Getting the right training data

TAR systems need good examples to learn from. Collecting high-quality and unbiased training data can be difficult and time-consuming.

Lack of transparency

TAR utilizes advanced, complicated AI algorithms that do not provide clear explanations for their decisions. This makes it hard to understand and trust the results they provide.

Keeping up with changes

E-discovery is always evolving, and new types of data and legal challenges arise regularly. TAR systems need to adapt to these changes, which can be a constant challenge.

Ethical and legal concerns

Using AI in e-discovery raises ethical and legal questions. Issues like privacy, fairness, and avoiding biases need careful attention to ensure compliance with laws and regulations.

The Bottom Line

AI is transforming document review and litigation support in e-discovery, which is the process of collecting and analyzing electronic information for legal cases. Traditional methods, such as keyword searches, have limitations in finding relevant documents and understanding their context.

TAR is a new approach that uses AI algorithms to categorize and prioritize documents based on their relevance. It reduces the time and effort required for review, improves accuracy, and provides a consistent approach.

However, challenges include obtaining high-quality training data, lack of transparency in AI decisions, adapting to changes in e-discovery, and addressing ethical and legal concerns.

Despite these challenges, AI in e-discovery offers efficient, cost-effective, and accurate solutions for handling the growing volume of electronic data in legal proceedings.

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Dr. Tehseen Zia

Dr. Tehseen Zia has Doctorate and more than 10 years of post-Doctorate research experience in Artificial Intelligence (AI). He is Tenured Associate Professor and leads AI research at Comsats University Islamabad, and co-principle investigator in National Center of Artificial Intelligence Pakistan. In the past, he has worked as research consultant on European Union funded AI project Dream4cars.