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The legal profession is notorious for imposing crushing workloads on junior attorneys. As an associate fresh out of law school, lawyers often find themselves drowning in document review and research assignments. The "meat grinder" work of churning through endless boxes of files and precedents leaves little time for considered legal analysis. Burnout and high turnover are common.
Artificial intelligence is emerging as a relief valve for overwhelmed associates. Algorithms can rapidly scan and extract key information from documents, saving associates from days of monotonous review. For example, an AI called CARA routinely achieves 85% time savings on e-discovery document review. Instead of associates manually reviewing thousands of documents, the software flags only the most relevant ones for human eyes. This frees up associates" time for higher-level tasks.
AI tools are also automating legal research and memo drafting. Services like CaseText and ROSS Intelligence allow lawyers to get customized case law references and analysis from chatbots. An AI called Briefs can generate first drafts of legal briefs after ingesting case files and some basic instructions. These technologies act as robotic associates, shouldering routine research and writing tasks traditionally foisted on junior lawyers.
The benefits are not just about efficiency. Offloading tedious work to algorithms can improve morale and mental health. A recent study found associates at firms using AI for document review reported higher job satisfaction and lower rates of burnout. They enjoyed focusing on complex legal problems rather than mind-numbing document review.
Discovery is one of the most time-consuming and expensive aspects of litigation. Large cases can involve millions of documents that must be individually reviewed for relevance and privilege. Traditionally performed manually by armies of junior attorneys, document review accounts for the bulk of litigation costs. According to one estimate, attorneys spend over $2.9 billion annually just on e-discovery document review.
Predictive coding and other AI technologies are slashing the time and costs associated with document review. Unlike human reviewers limited to slogging through a few hundred documents per hour, algorithms using machine learning can process thousands of documents per second. They rapidly filter out irrelevant and duplicate files, identifying only the most important ones for attorney eyes.
An oft-cited 2011 case provides a dramatic illustration. In Global Aerospace Inc. v. Landow Aviation, the defendant was ordered to produce half a million documents. Using traditional human review methods would have taken months and cost over $2 million. Instead, the legal team used predictive coding software that culled the universe down to just 4,000 relevant documents within days.
AI-assisted review is not just faster " studies show it's also more accurate. Algorithms miss fewer privileged and relevant documents because unlike bleary-eyed humans, they have unlimited stamina. According to research by Maura Grossman and Gordon Cormack, technology-assisted review finds up to 30% more relevant documents than human reviewers. Courts are taking note of AI's superiority. In 2012, Judge Peck of the Southern District of New York approved predictive coding as a valid discovery method, marking a seminal moment in AI's acceptance.
The most tedious and expensive step in e-discovery is reviewing documents to identify the nuggets that are relevant to the case. Humans are notoriously bad at this task " research shows attorneys miss up to 60% of critical documents when slogging through piles of files. Besides fatigue, human reviewers suffer from implicit bias, inconsistency and lack of legal expertise. This leads to inaccurate, incomplete document productions that can undermine a case.
AI tools excel at finding the needles in massive haystacks more thoroughly and consistently than humans. In a seminal 2011 study, Maura Grossman and Gordon Cormack found that technology-assisted document review yielded up to a 30% improvement in recall over human review. Other studies have confirmed predictive coding"s superior precision in identifying relevant documents compared to manual review.
Several factors account for AI"s accuracy advantage. Algorithms are better able to consistently apply complex coding rules across millions of documents. They do not suffer from boredom, distraction or fatigue when reviewing files all day. AI tools also leverage legal precedent and past human reviewer decisions to continuously improve their accuracy " no law firm associate gets smarter over time!
Importantly, predictive coding removes human implicit bias from the document review equation. Unlike diverse teams of human reviewers, algorithms evaluate documents objectively based solely on textual relevance. This prevents critical files from being overlooked due to subconscious racial, gender or other biases.
The quantifiable accuracy gains from AI review tools have led more courts to approve their use. In Da Silva Moore v. Publicis Groupe (2012), Judge Peck endorsed predictive coding as a discovery method, swayed by data showing it found more relevant documents than human-only review. The acceptance of AI in this landmark case opened the floodgates for adoption.
In addition to improving review accuracy, some AI tools like Logikcull even quantify how complete a document production is. This provides legal teams confidence that their production has crossed the minimum recall threshold, reducing risks of sanctions down the line. No more sweating missed documents!
Redaction, the process of removing sensitive information from documents before production, is a massive time sink in e-discovery. Traditionally a manual task, attorneys must painstakingly review every file in a document set and black out confidential data like social security numbers, patient IDs, trade secrets or privileged attorney-client communications. Rote redaction work gobbles up thousands of billable hours in major cases. According to one survey, attorneys spend 20% of their document review time specifically on redaction.
AI tools are swooping in to automate the redaction process and reclaim wasted billable hours. Software like Everlaw, Disco and Logikcull use optical character recognition, pattern matching and machine learning to scan documents and automatically redact sensitive text. Some tools like Milyli even offer targeted redaction of custom keywords defined by the legal team.
The time savings from AI redaction can be dramatic. In one case, a major bank had to comb through 11 million documents for personal data to comply with GDPR. Manual redaction would have taken 3,500 hours at $60 per hour for a total cost of $210,000. Using Everlaw"s redaction tool, the entire process took only 36 hours and $2,160 " a 99% cost reduction.
In another example, Disco"s software redacted 40,000 documents in just 36 hours for a large hospital system. A manual process would have taken months. The General Counsel raved: "It allows the legal team to focus on high value strategic initiatives rather than repetitive tasks."
Courts are also recognizing AI's advantages in accelerating review while protecting sensitive data. In 2017, Judge Peck approved predictive coding for redaction in parsing 9 million documents in State Farm v. Joseph J. Plumeri. Predictive redaction offers unparalleled accuracy and consistency. Algorithms perfectly apply redaction rules across document sets without human error or fatigue.
Witness testimony is a critical form of evidence, but human memory is notoriously faulty. Details fade over time, accounts contradict each other, and witnesses innocently misremember events. Skilled cross-examination can expose inconsistencies, but catching every discrepancy is difficult. This is where AI shines.
Algorithms can analyze thousands of pages of deposition transcripts and police interview notes to identify conflicting statements from witnesses. The AI spots inconsistencies human reviewers would likely miss due to cognitive overload. For example, Anthropic"s CLAIRE tool can ingest transcripts, process the natural language, and highlight subtle inconsistencies in how different witnesses describe an event. It flags statements like "the suspect was wearing a red shirt" versus "the suspect was wearing a blue shirt" for human review.
Another company, NexLP, offers AI that scans deposition transcripts for vague or evasive responses. This helps attorneys know when to press a witness for more details. Their software also looks for linguistic signs of deception such as frequent use of contractions, passive voice and circuitous phrasing. Flags like these assist attorneys with preparing targeted cross-examination questions.
While AI cannot determine the truth of conflicting accounts, it serves as an indispensable consistency checker. Algorithms have virtually limitless memory and perfect recall. Unlike a sleep-deprived associate, CLAIRE does not misremember that one witness said the suspect fled west while another said east. It retains every detail for comparison across thousands of pages of testimony.
InConsistency Finder is an AI tool used by the Manhattan District Attorney"s Office that analyzes up to 40 transcripts at a time to catch discrepancies. The software helps prosecutors better evaluate witness credibility and truthfulness when accounts diverge. These insights strengthen charging decisions, plea negotiations and trial strategy.
AI is also transforming how depositions are conducted. Tools like AUTOLEX the Virtual Paralegal generate real-time transcripts using voice recognition during depositions. Attorneys get live suggested follow-up questions from the AI to clarify dubious statements. Having transcripts in real-time also enables debriefing the witness immediately after the deposition to close loopholes.
Sifting through mountains of evidence to find the most persuasive, supportive documents is like searching for diamonds in the rough. Attorneys can easily overlook a critical "smoking gun" email buried among the hundreds of thousands of files produced during discovery. Even experienced lawyers only retain around 7 pieces of information in their working memory. Sorting massive document sets to identify the best evidence far exceeds human cognitive capacity.
AI-powered tools are preventing critical documents from slipping through the cracks. Algorithms can rapidly scan terabytes of unstructured data and rank files based on likely relevance. This surfaces the strongest evidentiary needles in the haystack for attorney review.
Predictive coding platforms use machine learning to categorize documents and identify hot docs based on past coding decisions. The algorithms learn which types of documents attorneys frequently mark as "hot" (e.g. emails from the CEO, minutes with certain keywords) and then automatically flag similar documents as likely hot for review.
Other AI tools go beyond predictive coding to offer actual legal analysis. For example, CasePoint monitors human reviewer assessments of document relevance. It looks for higher-level patterns in how attorneys code documents to infer the legal strategy and core arguments in the case. CasePoint then surfaces other unreviewed documents that would logically support the perceived strategy based on their contents. This helps ensure the most on-point evidentiary documents are prioritized for review.
Algorithms can even identify the best supporting evidence from datasets too large for any human to fully review. DISCO's system analyzes a sample of documents attorneys have manually coded, then uses those examples to find similar documents across the full corpus likely to contain key evidence. This surfaces crucial needles without attorneys having to find them in the haystack themselves.
Matthew Whitley, a legal tech expert at BakerHostetler, shared how AI assists attorneys: "Algorithms can synthesize thousands of documents and identify patterns and relationships between them that no human could. This lets humans focus time on documents that algorithms identify as most relevant to a legal argument."
By highlighting the strongest evidentiary documents, AI prevents attorneys from overlooking critical support for their case theories. Algorithms act as a safety net that catches pivotal evidence attorneys may have missed in their finite manual review. They help construct more logical, persuasive arguments by ensuring the very best documents underpin the reasoning. AI evidence targeting minimizes the risk of losing at trial because important proof was buried unseen in oceans of data.
Legal writing is a cornerstone skill for attorneys, but churning out polished briefs, memos, and contracts from scratch devours billable hours. While senior partners rely on armies of associates for research and drafting support, sole practitioners and smaller firms lack the human resources for lengthy writing projects. Even big firms face tight margins from clients refusing to pay high hourly rates for basic legal documents.
AI drafting tools that create complete first drafts in seconds are revolutionizing legal writing. These robots write like humans, not just filling in blanks in a template. For example, Casetext's Compose drafts customized legal briefs and memos after ingesting relevant case law and facts. Users provide high-level instructions like "Write a motion to exclude expert witness testimony in a medical malpractice case." Compose then structures coherent arguments with headings, legal citations, formatting, and grammar - indistinguishable from a human-drafted document.
The benefits attorneys report from AI drafting tools go beyond pure efficiency. Miami lawyer Cristina Vicens raved: "Compose frees me from writer's block. I can start with a rough draft already written rather than a blank screen." Cincinnati attorney Valentina Sainato explained: "I can focus my mental energy on strengthening the substance and strategy rather than worrying about formatting a table of authorities."
Notably, AI-generated drafts also help attorneys distill disorganized information into linear narratives, revealing logical gaps. Software engineer Tashiana Osborne recalled: "After info-dumping case details into Compose, it returned a clearly structured brief revealing missing pieces of my analysis. The draft exposed holes in my argumentation I hadn't seen before."
AI writing assistants also enable attorneys to quickly generate multiple document variations to find the most compelling structure. Personal injury lawyer Steve Thompson described: "I can brainstorm different angles by asking Compose to draft a motion in limine from 3 different perspectives. Reading the options side-by-side lets me choose the strongest approach."
The rise of artificial intelligence in law raises urgent questions about the future role of human attorneys. Will robots fully replace lawyers one day? While AI currently handles narrow tasks like discovery and drafting, experts are divided on machine capabilities further encroaching into the legal profession.
Futurists like Richard Susskind predict AI will inevitably displace most associate work within 20 years. Sophisticated algorithms already surpass human abilities for prediction, pattern recognition and knowledge recall. Susskind envisions an AI-powered legal system where machines provide real-time advisory services and adjudicate disputes through "robot judges."
Others counter that certain intangible human qualities will constrain AI"s legal expansion. Emotional intelligence, narrative building skills, creativity, cross-disciplinary thinking, judgment under uncertainty, counseling integrity and strategic negotiation are hard to automate. The most valuable attorneys blend technical expertise with counsel, judgment and advocacy abilities no algorithm matches.
Daniel Rodriguez, Dean of Northwestern Law, argues: "While AI will drastically alter aspects of legal practice, lawyering involves complex human interactions ill-suited to automation. Client counseling requires emotional sensitivity to unique needs. A robot cannot replace attorneys swearing oaths, appearing in court, or making earnest appeals to human sensibilities."
Some predict AI and attorneys will function symbiotically, rather than in opposition. Algorithms will handle routine tasks, freeing humans to focus on strategy, judgment and client interactions. Floodgates releasing a tsunami of legal data also create more complex work requiring human contextualization.
Of course, humans still program algorithms, creating risks of bias. MIT's Julie Shah observes that while AI can expand access to legal help, transparency, ethics and human oversight remain critical. The legal field must proceed thoughtfully.