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The benefits of AI in law — from efficiencies to eDiscovery

| Written by Altlaw

Artificial intelligence has gained popularity across various industries in recent years, and the legal sector is no exception. AI brings several benefits to the legal industry, including automating time-consuming tasks and saving costs.

In this blog post, we'll explore the benefits of AI in law and insights into some of the latest and most powerful tools available.


The benefits of AI in law

AI can accelerate litigation processes and generate efficiencies for legal professionals in several ways. 


Extra efficiencies

One of the most significant benefits of AI in law is increased efficiency. AI can automate mundane and repetitive tasks, including contract analysis, document review and due diligence.

Automating these tasks can free time to focus on more complex and strategic work. AI can also process large amounts of data accurately and quickly — an essential in litigation and eDiscovery processes.


Improved accuracy

Using AI can significantly improve the accuracy of legal work. AI-powered contract analysis tools can accurately extract key contract terms and clauses, reducing the risk of omissions or errors.

Similarly, AI-powered document review tools analyse thousands of documents in a fraction of the time it would take a human reviewer, meaning substantial time savings you can invest in other areas.


Cost reduction

AI can also reduce the costs of litigation practices. By automating time-consuming tasks and improving accuracy simultaneously, AI can decrease the number of billable hours required for a project, leading to cost savings and the opportunity to take on a larger workload.

There's also less need for manual data entry, a common error source. Without these errors, you're less likely to incur additional costs for your business.


Increased consistency

The introduction of AI can also bring consistency to legal work. Humans are prone to errors and inconsistencies when working on repetitive tasks, but AI can perform tasks with the same level of accuracy every time.

Plus, by using AI, you can predict how long a project will take more accurately. Software can estimate how long it'll take for a particular project, meaning you can scope projects accurately and be consistent with your timings across the board.


Improved decision-making

AI tools can provide valuable analysis and insights that can facilitate your business growth and the success of your projects.

AI-powered eDiscovery tools can analyse large amounts of data and identify patterns and trends that may not be immediately apparent to a human reviewer, leading to improved decision-making in legal cases.


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The impact of AI on eDiscovery

One area in which AI has a huge impact in the legal industry is eDiscovery. eDiscovery refers to identifying, preserving, collecting, processing, reviewing and producing electronically stored information (ESI) in legal matters.

eDiscovery has traditionally been a complex and time-consuming process, but AI can significantly improve this process by creating substantial time savings and efficiencies. This means legal teams can still benefit from the success eDiscovery brings in a fraction of the time. So what are some examples of AI in eDiscovery?


Automated document review

Document review is one of the most time-consuming aspects of eDiscovery. However, thanks to automation and the power of technology-assisted review (TAR), identifying relevant documents and information during the review process can be done in a fraction of the time.

An automated approach to document review can significantly reduce time and costs while improving accuracy and consistency in the process. TAR uses machine learning algorithms to analyse documents based on relevance, responsiveness and privilege.


Sentiment analysis

Sentiment analysis utilises natural language processing (NLP), machine learning and AI to analyse and determine the sentiment, opinion or emotion used in text or speech.

The tool can configure whether the sentiment of communication is positive, negative or neutral, allowing reviewers to better understand the context behind what an individual is saying.

RelativityOne's sentiment analysis tool scans documents and assigns a numerical score based on the likelihood of the sentence containing your desired sentiment. The tool can help reviewers add more detail to their reviews, combining both topic relevance and sentiment.


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Predictive coding

Predictive coding is another TAR method used in eDiscovery that relies on machine learning algorithms to identify relevant documents. It begins with training the system with a subset of relevant and non-relevant documents, allowing the algorithm to learn the difference between them.

Once the algorithm has been trained, it can apply this knowledge to new documents, classifying them as relevant or non-relevant and passing those likely to be relevant to your reviewers first in a prioritised review. This significantly reduces the resources and time needed to complete a document review.


Early case assessment (ECA)

Early case assessment (ECA) in eDiscovery refers to rapidly analysing an extensive data set at the beginning of a legal case to identify potentially relevant information.

ECA uses technology to prioritise data before the formal discovery process begins. By doing so, legal teams can gain an initial understanding of the case's scope, any potential issues and the types of documents that may be relevant. ECA can also help form litigation strategies early in the case, leading to more successful outcomes and effective early decision-making.


Concept clustering

Concept clustering is a technique used in eDiscovery to group documents based on conceptual similarity rather than keywords or metadata. NLP can identify underlying themes, topics and concepts within a set of documents, grouping documents that share common ideas or language together.

This tool can be highly effective when working with sentiment analysis, allowing documents to be grouped by analysing both the language used and the sentiment behind it.


Email threading

Email threading pulls together all connected documents — both emails and attachments — from an email chain. This allows you to locate other emails within the same thread coded as relevant and identify any coding inconsistencies.

The tool can significantly reduce the number of documents in a review queue by eliminating duplicate or irrelevant content, presenting you with the most complete versions of an email thread.


Level up your discovery processes with RelativityOne

RelativityOne is Relativity's fastest, most up-to-date and most secure platform for handling unstructured data. If you're looking to improve your eDiscovery processes and save time and money, RelativityOne is the perfect place to start. 

With RelativityOne, you can access a range of AI-powered tools, including:

  • Active learning
  • Email threading
  • Near-duplicate detection
  • Conceptual search
  • Language identification
  • Sentiment analysis
  • Clustering
  • Categorisation
  • Named entity recognition
  • Topic modelling

Want to learn more about these tools and how the platform can lead you to success? Head to our dedicated RelativityOne page by clicking here.


Learn about AI in eDiscovery with our Content Hub

Looking to take the next step in your eDiscovery learning journey? We can help. 

By signing up to our Content Hub, you'll gain unlimited access to our range of guides, eBooks and resources, covering several modern litigation subjects.

From modern data handling to DSARs and automation, our Content Hub is the perfect place to take your eDiscovery knowledge to the next level.