techsupport In today’s digital world, electronic information plays a crucial role in the legal process. However, the growing amount of electronic data makes it difficult for legal professionals to search, analyze, and present information during litigation.
Fortunately, artificial intelligence (AI) has made document review possible more efficiently. These advances are transforming document review into litigation or investigations, saving time, reducing costs, and improving efficiency.
In this article, we look at how AI transforms document review and litigation assistance.
What is e-discovery?
Electronic discovery, or e-discovery, refers to 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 e-discovery process begins when there is a need to gather electronic evidence for a legal case. This involves searching for and retrieving relevant electronic data from various sources, such as computer systems, servers, email files, cloud storage, social networks, etc.
Once the data is collected, it goes through a series of steps to organize, filter, and analyze the information. This process uses specialized programs and tools that allow legal teams to search for specific keywords, dates, or file types to narrow down the data set. This helps find the most relevant information for the case.
Once the data review and analysis are completed, judicial proceedings can use the selected documents or files as evidence. This may include presenting it to the court or delivering it to the opposing party.
E-discovery is crucial in modern litigation because it allows for the efficient management of large volumes of electronic information that would be impractical to review manually.
Challenges of traditional E-Discovery
E-discovery is primarily performed using keyword searches to narrow down the documents collected for legal review.
While this helped reduce the number of documents for review, the approach had several shortcomings:
- Ineffectiveness 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;
- Increasing volumes of electronically stored information make it difficult to access relevant information.
AI and the legal field
- AI has made notable progress in understanding and generating texts in recent years. These advances are due to improvements in the design and training of AI models.
- A key factor is transformer architectures, which help create more powerful pre-trained models (trained on massive text data) that can be used for various tasks and deliver impressive performance even with smaller data sets.
- Additionally, the development of easy-to-use APIs (application programming interfaces) has made it convenient to create new applications with these advanced AI models, even for people with little or no coding experience.
- These advances have caught the attention of legal professionals and AI researchers, as they see many opportunities to automate repetitive tasks in the legal field.
- Tasks such as reviewing documents, analyzing contracts, and conducting legal research can require much time and 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 address the challenges of e-discovery and take advantage of recent advances in AI, a new approach called Technology Assisted Review (TAR) in e-discovery has emerged.
TAR uses AI algorithms to analyze and classify large volumes of electronic documents based on their relevance to a legal case.
The process involves training AI algorithms using a subset of documents that legal experts have manually reviewed and labeled as relevant or irrelevant.
Algorithms learn from these human decisions by identifying patterns and characteristics associated with relevant documents.
Once the training phase is complete, the AI algorithms apply the acquired knowledge to classify the remaining unreviewed documents based on relevance. This classification allows legal professionals to focus on the most relevant documents, reducing the need for extensive manual review of many papers.
Advantages of AI in E-Discovery
The use of AI by the TAR 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 allows legal teams to prioritize their efforts on the subset of documents that are most likely to be relevant.
This considerably saves time and resources, allowing a more efficient review process.
Improve accuracy
TAR could improve document review accuracy by harnessing the power of AI algorithms. These algorithms can analyze complex patterns and relationships within data beyond simple keyword matching.
As a result, traditional methods may miss critical tests, while ART reduces the risk of ignoring them.
Ensure impartiality and reliability.
The TAR provides a consistent and standardized approach to document review. Unlike human reviewers, who can introduce inconsistencies or bias, AI algorithms apply consistent criteria throughout the process, ensuring fairness and reliability in identifying relevant documents.
Challenges of AI in E-Discovery
In addition to many advantages, ART is not without challenges:
Get the proper training data
TAR systems need good examples from which to learn. Collecting high-quality, unbiased training data can be difficult and time-consuming.
Lack of transparency
TAR uses advanced and complicated AI algorithms that do not clearly explain its decisions. This makes it difficult to understand and trust the results they offer.
Stay up to date with changes.
E-discovery constantly evolves, and new data types and legal challenges arise regularly. TAR systems must adapt to these changes, which can be a constant challenge.
Ethical and legal concerns
The use of AI in e-discovery raises ethical and legal questions. Privacy, fairness, and avoidance of bias require careful attention to ensure compliance with laws and regulations.
Conclusion
AI transforms document review and litigation support into e-discovery, 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 classify and prioritize documents based on their relevance. 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 to manage the growing volume of electronic data in judicial proceedings.
