AI for Legal Research: A Guide for Legal Professionals

How to use AI for legal research

Legal research is one of the most common ways legal professionals use AI in their practice. Lawyers and nonlawyers alike can use traditional AI technology, like machine learning, and newer generative AI tools to carry out comprehensive legal research more efficiently.

For example, AI can quickly analyze massive amounts of data, helping researchers to identify the most important sources and even avoid missing relevant documents. Instead of spending time reviewing individual cases that may prove irrelevant, legal researchers can use AI tools to more easily find the leading case law, guiding legal principles, and the best legal language to support their argument.

Generative AI can aid legal researchers with conducting a document review, summarizing case law, or analyze briefs and complaints.

While there are enormous time-saving benefits to using legal AI tools for research, legal professionals need to understand the risks and disadvantages that could come with using AI for legal research, including hallucinations, fair use questions, and the safeguarding of confidential information.

And when harnessing the power of AI for legal research, attorneys also should remember that these groundbreaking digital research and analytical tools should be seen as an enhancement to legal research – not a replacement for their own expert review, nuanced analysis, and professional judgment.

The benefits of using AI for legal research

The benefits of AI in law are clear: the unmatched speed of AI layered into a trusted legal research database can help attorneys prepare better and faster than ever before, resulting in added efficiencies, more streamlined workflows, better training and education, elevated profits, and, ultimately, greater client satisfaction.

Legal AI tools can automate time-intensive tasks such as reviewing evidence or producing initial drafts of motions, legal briefs, contracts, and settlement agreements.

Faster, more targeted docket searches

Crafting searches is already a critical skill for using legal research software effectively. The right docket search query can uncover a wealth of information about relevant cases, jurisdictions, judges, and opposing counsel.

AI-powered docket search functionality allows users to find the precise form they need in less time. For example, Bloomberg Law’s Docket Key sets a new standard for how quickly you can access exemplar forms across 20 filing types, including briefs, motions, and complaints.

In addition to standard fields like court and nature of suit, you can search briefs & motions by motion type, by law firm motion filer, and by motion outcome.

This can be particularly helpful when searching dockets for outcomes, looking for specific example pleadings, or zeroing in on a particular research topic such as sanctions, use of experts, or discovery rulings.

Case strategy informed by data

With the power of AI, modern legal research tools can help legal professionals access valuable information to guide case strategy.

For example, before litigation analytics were common, a partner may have asked a junior associate to find all summary judgment motions ruled on by a specific judge to determine how often that judge grants or denies them. The attorney could have manually searched PACER or searched through court opinions, which would have taken a long time.

AI-powered litigation analytics features, like Bloomberg Law’s, enable users to search millions of legal data points by company, law firm, or judge to better help predict possible outcomes and extrapolate litigation costs. The tool allows users to view litigation history and identify any risks to support due diligence.

For forum shopping purposes, researchers can instantly compare active federal district judges and jurisdictions and gain insight into a judge’s actions and trends across jurisdictions within the context of the national average, including how long cases take to resolve.

Our Docket Path tool further uses AI to provide attorneys predictive insights into the likely outcomes of specific cases based on historical trends in closed dockets.

Deliver value to your clients or business

Now that legal research can be done so much more efficiently with AI, it frees up time for a legal professional to focus on other activities that benefit their company or clients.

Both in-house and law firm attorneys agree that the amount of time they can spend on higher-value tasks with increase thanks to generative AI.

This is important for law firms in an environment where clients are increasingly expecting firms to employ AI technology, and even pushing back on billing for excessive legal research hours.

When it comes to in-house legal departments, AI can help them retain more work in-house, be more selective in the work they outsource, and give them more leverage to structure their fees under alternative fee arrangements for work sent to outside counsel.

What makes an AI research tool “legal-grade”?

It’s important to consider the golden rule for generative AI models: the quality of the inputs directly affects the quality of the output. This means it’s critically important to know exactly what sources a generative AI tool is using as the basis for its results.

While most AI tools rely on similar underlying technology, it’s wrong to assume that general-purpose generative AI platforms like ChatGPT are suitable for legal work. General-use AI tools often pull from a vast, unvetted pool of online information.

The best AI tools for law are designed specifically for the legal field and built on transparent, traceable, and verifiable legal data. At Bloomberg Law, we’ve built our technology on a foundation of comprehensive primary and secondary sources, in-depth news, and expert

attorney-written analyses. Tools like our AI Assistant further give users access to the model’s sources, allowing them to verify for accuracy or dig deeper.

Additionally, our products are pressure tested by hundreds of researchers, legal experts, and engineers, and we’re able to continuously update the guardrails to prevent the responses from drifting over time or becoming less accurate.

The risks of using AI for legal research

While AI tools offer automation capabilities that can resolve inefficiencies in legal practice, the use of AI technology raises serious issues for the legal field, including:

• Algorithmic bias: This occurs when the algorithm itself is biased, or when an otherwise unbiased algorithm is trained on biased data
• Hallucinations: The phenomenon by which AI chatbots may confidently provide false information in response to a prompt
• Inaccuracies
• Confidentiality concerns

Some legal professionals have been deploying AI for the better part of a decade to parse data and query documents. But the explosion of interest in certain powerful AI tools for general use has led to questions and concerns within the legal profession. As a result, a growing number of law firms and in-house legal departments are vetting AI vendors rigorously and developing AI data governance to mitigate potential risks.

“I think the thing that most people are worried about when they do research using generative AI is hallucinations,” said Cohen, pointing to news reports of lawyers who have submitted made-up citations generated by faulty AI research. “That is something that Bloomberg Law takes very seriously, and we’re working on ways to make sure that the data we pull from and the results that we give back are complete.”

Use AI to enhance, not replace, legal expertise

AI shouldn’t take the place of the review, analysis, and judgment of an attorney. AI tools should be used to enhance the legal research process by providing more data on a wider array of variables so that a legal professional can take even more information into consideration. Legal professionals should still rely on their own experience and knowledge of their clients, the facts, legal precedent, and expert opinions.

Which is the best AI for legal research?

The best AI for legal research should be designed just for that: legal research.

Legal AI tools are valuable because they can analyze massive amounts of data and unearth details that are undetectable to the human eye – potentially minimizing errors and helping researchers avoid missing important, relevant documents. Yet without human expertise and supervision ensuring the quality and accuracy of data, AI can potentially do more harm than good.

“Being able to analyze – that’s the bread and butter of attorneys,” said Cohen, noting that legal professionals must know how to apply their research and make a case. “That’s what the best legal platforms do that regular generative AI doesn’t.”

Recent advancements in AI technology require an extraordinary level of discernment and testing. General-use AI tools don’t have the capabilities to support the layered, nuanced research that lawyers need.

The latest legal research tools use AI technologies such as natural language processing and machine learning to help legal professionals more quickly discover valuable information that would otherwise take hours of manual research to find.

Natural language processing improves traditional legal research methods

Natural language processing refers to a computer’s ability to understand human language in the way that people naturally speak and write it.

Today, users are generally familiar with natural language searching, which is widely used on popular internet search engines. As platforms continue to adapt to changing technologies, legal researchers will begin gravitating away from traditional ways of searching such as using keywords or Boolean search.

One potential downside: These search engines can apply algorithms to rank the relevance of results, and the reasons for those rankings may not be obvious in all platforms. You may need to interpret whether the search is giving you everything you need.

When considering legal research software, check to see if the tool you’re vetting offers context on results. For example, Bloomberg Law’s Smart Code tool quickly pulls case extracts related to your natural language search and ranks each case interpretation as strong, moderate, or weak, so you don’t waste time on results you don’t need.

Supervised machine learning minimizes risk

Machine learning refers to a computer’s ability to “learn” by using algorithms to analyze data, find patterns, or predict outcomes.

There are three types of machine learning to understand when considering how to use AI for legal research:

1. Supervised machine learning: The AI application seeks and recognizes patterns within predefined data sets. These data sets are typically created by human domain experts who act as “guidance counselors” of sorts to the machines.
2. Unsupervised machine learning: The AI application creates data sets without known outputs or predefined data. The application is, in essence, learning and adapting to inputs on its own.
3. Reinforcement learning: The AI application uses an algorithm to create correlations. By incorporating rewards and learning from data feedback, the application uncovers the ultimate processing path.

The best AI for legal research is one that incorporates supervised machine learning tools. This approach offers the benefit of faster research than ever with less risk of inaccuracies or missing documents. Supervised machine learning can automate the tedious process of reviewing legal documents, checking citations, searching case law, and finding additional relevant content. It can also help you identify various document types across court dockets, so you’re able to search for a precise filing type.

Because the legal field places supreme importance on accuracy, legal professionals should be extremely cautious about relying on tools that use purely unsupervised learning techniques.