Current Proven Legal AI Use Cases

Last time, we continued our “nuts and bolts” series of artificial intelligence (AI) for legal professionals with a look at some of the current AI regulations that exist, as well as some resources to stay current as the AI regulation landscape evolves.

While many think that the application of AI to legal use cases is only beginning to emerge, there are several current legal use cases to which AI is being applied today that have demonstrated proven benefits. In this post, we will discuss some of the use cases that can empower users today.

What Counts as a Legal AI Use Case?

AI, analytics, and automation can all be applied to support legal use cases and they can overlap considerably. Not every use case that involves analytics and automation leverages AI. To identify current proven legal AI use cases, we first need to define what we consider to be a use case that involves AI versus those that involve analytics or automation without AI. For that, let’s refer to our blog on Coming To “Terms” with AI, which discussed 18 of the most important terms that legal professionals should know related to AI. Two of those terms were as follows:

Machine learning is a subset of artificial intelligence that enables computers to improve their performance on tasks by processing data and learning from it without being explicitly programmed. It involves algorithms that find patterns or regularities in data, and based on these insights, the system can make predictions, decisions, or classifications. Over time, as more data becomes available and the model adjusts its internal parameters, its accuracy and effectiveness can increase, allowing it to autonomously adapt to new information and evolving situations.

Natural Language Processing (NLP) is a branch of artificial intelligence that focuses on the interaction between computers and human language. It seeks to enable machines to understand, interpret, and generate human language in a way that is both meaningful and contextually relevant. NLP encompasses a range of tasks including sentiment analysis, machine translation, speech recognition, and text summarization, leveraging various techniques from linguistics and machine learning to bridge the gap between human communication and computer understanding.

Because both are AI-related approaches, we’re considering any current legal use case that uses either (or, in many cases, both) of these approaches to be a legal AI use case.

Ten Current Proven Legal AI Use Cases

With that requirement set, here are ten use cases for AI that have demonstrated proven benefits for legal teams.

Traditional Use Cases

These are traditional use cases that legal teams have been benefitting from for several years, especially when it comes to discovery and litigation:

• Concept Clustering: Concept clustering leverages AI through machine learning and NLP techniques to automatically group similar concepts, items, or documents based on their characteristics or semantic similarities.
• Predictive Coding: Regardless of the name or acronym used, the ability to apply machine learning to assess and prioritize hundreds of thousands of documents in seconds has become “must have” application of AI for discovery.
• Continuous Multi-Modal Learning (CMML): This approach is an advanced AI strategy that integrates and continuously learns from various types of data models or algorithms to improve understanding, prediction, and decision-making across collections, using capabilities of other approaches such as predictive coding.

General and Discovery Analytics

This group of legal use cases involves the analysis of data to develop an understanding of that data to support general and discovery needs:

• Key Entity Analysis: This analysis involves identifying and extracting the most important entities (such as people, places, organizations, dates, and other specific information) from your data, using NLP and machine learning to perform that analysis.
• Timeline Analysis: Understanding the sequence of events and identifying missing or inconsistent information is important for accurate analysis and decision-making, and timeline analysis leverages both NLP and machine learning to accomplish that task.
• Communication Analysis: Identification of patterns in communications also leverages NLP (which allows computers to understand and interpret human language) and machine learning (which can learn from data to identify patterns and anomalies within large datasets of communications).
• Cognitive Analytics: Cognitive analysis refers to the process of understanding behavioral signals within a set of data (it can also be referred to as sentiment analysis). It uses AI techniques, including machine learning, NLP, and deep learning, to analyze and interpret complex data.
• Privilege Analytics: The analysis of potentially privileged communications within data collections often utilizes both NLP (to parse text for indications of legal advice) and machine learning (to learn from previously classified documents as privileged or non-privileged).
• PII Analytics: The identification of personally identifiable information (PII) involves the use of NLP to analyze text to detect and classify PII, while machine learning models can be trained on datasets labeled with examples of PII and non-PII to accurately identify PII in new, unseen datasets.

Contract Analytics

Because of the unique nature and requirements associated with contracts, the ability to analyze thousands of contracts and their different clauses through an automated workflow has become a key legal use case, and this advanced application of AI involves using NLP, machine learning, and sometimes deep learning to automate the extraction, interpretation, and analysis of key information from contracts, as well as analysis of the trends, patterns, and benchmarks over a contract collection, offering strategic insights that can guide decision-making.

Conclusion

There are already several legal use cases that leverage AI that have proven benefits within legal, discovery and contract management workflows. The application of AI approaches such as machine learning and NLP are already providing significant benefits to legal teams today, with future use cases and benefits to come! The sky is the limit!

In our final post in the series, we’ll discuss emerging legal use cases we expect AI will be applied to and where AI may provide benefits to the legal community in the future!

For more regarding Cimplifi specialized expertise regarding AI & machine learning, click here.

In case you missed the previous blogs in this series, you can catch up here:

The “Nuts and Bolts” of Artificial Intelligence for Legal Professionals

The “Nuts and Bolts” of AI: Defining AI

The “Nuts and Bolts” of AI: Types of Bias in AI

The “Nuts and Bolts” of AI: Privacy Considerations

The “Nuts and Bolts” of AI: Transparency, Explainability, and Interpretability, of AI

The “Nuts and Bolts” of AI: ABA Guidance on the Use of AI

The “Nuts and Bolts” of AI: The Current State of AI Regulations

The “Nuts and Bolts” of AI: Current Proven AI Legal Use Cases

The “Nuts and Bolts” of AI: Emerging Use Cases and the Future of AI for Legal

We invite you to stay informed and join the conversation about AI. If you have questions, insights, or thoughts to share, please don’t hesitate to reach out.