While the legal industry was initially quite hesitant to adopt AI tools, we saw the adoption rate jump from 19% in 2023 to 79% in 2024. That’s huge. And while not all legal tech tools use AI, the increased prevalence of AI in all industries implies that AI technologies make up a fair percentage of this whopping number.
According to Haley Sylvester, Associate at Pryor Cashman: “The legal profession, often characterized by a reverence for tradition and resistance to change, is at the cusp of a technological revolution that promises the potential to reshape the profession.”
So if you’re wondering what all the hype around AI is about, we’re here to explain. AI for lawyers, especially plaintiffs’ attorneys, can dramatically streamline many aspects of their day-to-day workflows, including case development, legal research, contract writing, and a whole lot more.
What is AI for lawyers?
Let’s start with the basics.
In a recent podcast interview, Darrow co-founder and CTO, Gila Hayat, defined AI in the following terms:
“Artificial Intelligence, in a nutshell, is a system that has ingested huge amounts of human intelligence that has been curated for a long period of time. It might look like magic, or even dark magic for some people.
But at the core of things, AI is a machine that has been exposed to a huge amount of data, that due to the sheer volume, couldn't be comprehended by a single human being. Thanks to the exposure, the machine then has the ability to derive or predict the answer to any kind of question. So the more data you put into it, the better results you will get.”
Essentially, AI involves creating machines and algorithms that are capable of performing tasks that typically require human intelligence.
For plaintiffs' attorneys, AI and legal tech are particularly valuable because they operate outside the traditional billable hours model. This provides them the opportunities to leverage AI’s speed, efficiency, and cost-saving capabilities, without needing to worry about reducing their bottom lines.
Types of AI used in the legal industry
There are many types of AI, some of which you’re probably already familiar with. The two most common types of AI used in the legal industry today are powered by machine learning, a technology in which computers learn from data and improve performance over time.
These include natural language processing (NLP) and large language models (LLM).
NLP
Natural language processing (NLP) is a branch of artificial intelligence that helps computers understand and work with human language, like text or speech. It’s what makes things like chatbots, translation apps, and voice assistants work. NLP breaks language down into parts, figures out the meaning, and then performs tasks like analyzing, translating, or responding.
Here’s a few examples of how legal tech technologies utilize NLP:
- Anomaly detection: Identify irregularities or potential violations in contracts, pricing, or other legal records.
- Clause extraction: Discover specific legal clauses in contracts for quick review.
- Regulatory monitoring: Automatically detects updates or changes in laws and regulations that affect a firm's operations.
- Document classification: Sorts large volumes of legal documents into categories like litigation, compliance, or IP.
- Obligation tracking: Extracts and organizes contractual obligations to ensure parties meet their terms.
- Litigation risk assessment: Identifies patterns in case filings to flag potential legal vulnerabilities.
- Language translation for international cases: Translates contracts, case law, and statutes into multiple languages while preserving legal context.
LLMs
LLMs are a type of NLP that are specifically designed for tasks like text generation, translation, summarization, and question-answering. ChatGPT is a prime example.
LLM models are trained by processing massive amounts of text data, like books, websites, and articles, to learn patterns, context, and relationships in language. During training, the model predicts the next word in a sentence repeatedly, adjusting its internal settings to improve accuracy over time.
These are a few ways LLMs are being used to make the legal industry more efficient:
- Brief and memo drafting: Generating first drafts of legal briefs, motions, and memos based on case facts or prompts.
- Advanced case summarization: Summarizing complex court rulings or legal opinions in plain language for quick understanding.
- Legal question answering: Providing detailed, context-aware answers to specific legal queries, such as "What is the statute of limitations for X?"
- Legal strategy suggestions: Analyzing previous rulings to recommend potential legal strategies for a case.
- Contract creation: Drafting customized contracts based on specific inputs, such as jurisdiction, industry, and party obligations.
- Discovery insights: Sifting through thousands of discovery documents to identify patterns, key evidence, or privileged materials.
What is generative AI?
Like LLMs, generative AI uses advanced machine learning models to produce new text, images, or other data that mimic human-created content. In the legal field, this means drafting documents, generating summaries, or even crafting arguments.
While traditional AI technologies perform rule-based or predictive tasks, generative AI goes a step further by helping lawyers produce creative, tailored content. Here’s how:
Generative AI builds on and enhances the capabilities of NLP and LLMs by moving from analysis to creation. Traditional NLP systems are designed to extract and analyze data, such as identifying legal terms or categorizing documents, whereas generative AI uses this understanding to produce entirely new text.
For instance, NLP might identify missing clauses in a contract, but generative AI can go further by drafting those clauses. Similarly, while LLMs provide the underlying language understanding that enables context-aware tasks, generative AI applies this foundation to create tailored legal documents, summaries, or arguments, pushing the boundaries of what AI can achieve.
The three waves of legal tech
Let’s talk about the evolution of legal tech over the past 25 years.
NFX, the venture capital firm famously known for investing in disruptors (and one of Darrow’s early investors), explains in a 2023 blog post that the introduction of AI in the legal profession actually represents the third wave of the ongoing legal tech revolution.
This illustrates the impact of artificial intelligence on the legal industry, marking a significant evolution in the way technology is transforming legal practices.
First wave: 2000-2011
The first wave was predominantly dominated by traditional Software-as-a-service (SaaS) companies, such as DocuSign and LegalZoom. While these companies have achieved considerable success in providing valuable products to attorneys (i.e. by automating or digitizing processes and enabling remote work), they primarily operated without significant integration of AI.
During this time, there was a notable shift towards legal tech and AI with the introduction of e-discovery solutions. AI technologies, like machine learning, began to be applied for tasks like data search, ranking, and categorization, ultimately reducing the manual effort required for document analysis. According to NFX, this was a relatively slow period for the industry, with fewer than ten startups founded annually between 2000 and 2010.
Second wave: 2012 - 2020
During the second wave, tech companies started creating multi-use platforms with a number of capabilities, rather than single-purpose tools. For instance, Smith.ai, developed a customer engagement platform, while other firms focused on contracts management, offering services for drafting contracts, contract storage, and contract analysis.
During this period, legal tech companies began to introduce the use of LLMs, though they were not yet fully practical for lawyers. Some of the pioneering companies during this wave began utilizing NLP to enhance efficiency, work quality, and overall customer experience in the legal field, marking a significant step towards a more integrated and effective legal AI ecosystem.
Third wave: 2021 - present
LLMs have evolved significantly, now being trained on extensive datasets of legal language. When developed and trained correctly, these LLMs have the potential to achieve language proficiency on par with that of a highly trained lawyer. Their ability to comprehend, interpret, and generate legal text makes them valuable tools in various legal applications, including document review, contract analysis, and legal research.
This advancement in LLMs represents a transformative leap in the legal industry, as it becomes a human-like assistant for legal professionals, allowing legal professionals to succeed more than ever before. The way LLMs are already enhancing the overall efficiency and accuracy of legal processes is ultimately reshaping the way legal work is conducted.
How is AI being used in the legal industry?
2024 has been a pivotal year for global legal tech. The market size was estimated at $27.32 billion, and is expected to continue climbing at a rapid pace over the next 10 years. By 2034, the market is estimated to be valued at $65.51 billion!
As legal tech continues to grow in popularity in law firms, it’s important to have an understanding as to what attorneys are actually using AI for. Here are 10 examples:
- AI for legal research: Advanced AI systems analyze legal databases to identify relevant case law, statutes, and legal precedents. These tools help attorneys uncover existing case law and insights to develop cases that might otherwise be missed.
- Contract analysis tools: AI-powered contract-analysis tools review contracts to identify risks, inconsistencies, and compliance issues. They assist attorneys in drafting and negotiating agreements by providing detailed analysis of contractual terms.
- Predictive analytics: Data analytics software uses AI to analyze historical case data to predict outcomes, estimate settlement amounts, and assess case risks.
- Anomaly detection algorithms: At the moment, Darrow is the only company doing this. We use AI algorithms to scan databases and other publicly available records to detect anomalies that might indicate potential cases. While not available as a software for purchase, attorneys partner with us to develop, strategize, and litigate class and mass action cases.
- E-discovery AI software: Attorneys use this software to automatically identify and categorize electronically stored information so it’s easier to locate relevant data, reduce the burden of manual review, and improve the accuracy of document identification.
- Legal chatbots: AI-driven chatbots handle routine client inquiries from firms’ websites, provide legal information, and gather initial case details from a firm’s website. They improve client engagement and accelerate the intake process.
- Document review and automation: Attorneys are using AI-powered document management software to analyze and draft legal documents. Software with NLP-capabilities identify errors, flag important clauses, and automate tasks.
- Litigation management software: AI-powered litigation software analyzes case histories, legal filings, and court records to uncover legal trends that shape case strategies. It evaluates factors like judge rulings and opposing counsel’s behavior to predict outcomes and highlight opportunities for stronger arguments.
But can attorneys trust AI? There are a few risks
You might be wondering if AI tools can truly be trusted and whether or not AI tools are ethical. While the benefits are undeniable, there are real risks, too.
Here are 5 important risks to keep in mind:
1. Hallucinations
Hallucinations occur when AI generates information that appears accurate but is actually false. This information might look convincing but can include incorrect data, like non-existent case law or misinterpreted statutes.
Here’s a prime example. In June 2023, two lawyers were fined $5,000 after submitting a legal brief created by ChatGPT, which cited several nonexistent cases and nonexistent quotes and holdings. The judge stated that, “Technological advances are commonplace and there is nothing inherently improper about using a reliable artificial intelligence tool for assistance. … But existing rules impose a gatekeeping role on attorneys to ensure the accuracy of their filings.”
As legal tech becomes more prevalent, it’s crucial that attorneys don’t solely rely on AI to do their work. Instead, it should be treated as a helpful tool to expedite processes, while still using human expertise to verify work.
2. Bias
AI learns from the data it’s trained on. An article by the New York State Bar Association states: “This input affects the machines like the process of imprinting. AI bias is the voluntary or involuntary imprinting of one or more human biases in one or more datasets. The model delivers biased results because of fallacious assumptions of the training data provided to the neural network.”
Historical patterns of inequality in legal outcomes can influence the recommendations an AI tool provides. This could mean skewed risk assessments or subtly biased language in documents.
3. Lack of transparency
AI doesn’t always explain its reasoning when providing you with an answer to a question or general information. In a profession where clear reasoning and accountability are key, this lack of transparency can make it hard to trust the AI’s outputs or use them confidently in legal decisions.
4. Data privacy concerns
Lawyers handle highly sensitive client information, and AI tools, especially cloud-based ones, introduce risks around data security. If proper cybersecurity and data encryption is not in place, confidential client data might be exposed, creating ethical dilemmas or even legal repercussions. It’s essential to ensure any AI tool meets strict privacy and compliance standards.
These risks just go to show that no, AI is not coming for our jobs. While AI has changed how attorneys perform some part of their jobs, there are elements to being a lawyer that no machine can replicate. Strategizing complex cases, maintaining client relationships, litigating in the courtroom, and making judgment calls rooted in empathy and ethics are an important part of any attorney’s job.
But how can attorneys counter these risks?
Yes, AI comes with risks, but attorneys have control over mitigating them. Here are 5 strategies lawyers can use to reduce the risk of errors, bias, and ethical challenges when incorporating AI into their practices:
- Verify outputs: Use AI as a starting point, not the final word. Always review AI-generated content for accuracy and ensure it aligns with legal standards to ensure there are no hallucinations.
- Understand the technology: Familiarize yourself with how the AI works, including its limitations and decision-making processes. Transparency from the tool’s provider is key to building trust and understanding outputs.
- Apply ethical oversight: Combine AI insights with human judgment to maintain ethical standards.
- Prioritize data security: Use tools that comply with data protection laws and implement strong encryption and security measures. Avoid exposing sensitive client information to systems that don’t meet strict privacy standards.
Federal legislative oversight
The risks associated with AI, coupled with the growing instances of unethical practices by lawyers using these tools, have pushed states and the federal government to introduce legislative oversight.
During Biden’s presidency, he signed two executive orders:
- Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence: On October 30, 2023, President Biden established new standards for AI safety and security. This order established guidelines to ensure the safe, secure, and trustworthy development and use of AI technologies across various sectors.
- The White House Blueprint for an AI Bill of Rights: Provides five principles and associated practices to help guide the design, use and deployment of AI systems.
However, Trump revoked both orders when he entered the White House and is in the process of creating his own executive order on AI. His order will provide less government oversight than Biden’s but will still still push for more innovation and government funding towards AI.
There has also been a lot of talk in the Senate about creating more specific laws surrounding the use of AI, and there are several federal proposed laws in the works.
But we have also seen older, pre-existing laws being used to monitor the use of AI, much like how the 1988 Video Privacy Protection Act is used to regulate modern-day online streaming services.
For example, the Federal Trade Commission (FTC) combats deceptive pricing and unethical business practices. Its mission doesn’t specifically mention AI–but it doesn’t exclude it either.
For example, on September 25, 2024, as part of its Operation AI Comply, the FTC presented five cases exposing AI-related deception. One of these FTC complaints was against DoNotPay, which marketed itself as "the world's first robot lawyer" and an "AI lawyer.” The FTC claimed that DoNotPay did not live up to its claims, inherently misleading consumers.
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