AI in the Legal Industry: The Basics in a Nutshell

AI is increasingly influential in the legal industry, with some applications already mature, others rapidly improving due to advances in Natural Language Processing (NLP) and machine learning, and some proposals remaining unrealistic. The most relevant current legal use cases cluster into two categories: NLP applications and predictive (regression-style) applications. NLP enables systems to read, generate, summarize, translate, and transcribe speech or text, making it well-suited to the document-heavy nature of legal work. Key NLP-driven uses include research support, relevance searches, contract review, and document automation. Recent model improvements—such as Recurrent Neural Networks (RNNs), attention mechanisms, and Graph Neural Networks (GNNs)—increase contextual understanding and improve search reliability and related tasks.

Predictive applications aim to forecast outcomes like case duration, litigation risk in communications, or lawyer success rates. While appealing, these tools are less mature and face major constraints in explainability, which is especially problematic in legal contexts that demand transparency, accountability, and defensibility in court. Decision-support systems raise additional ethical and legal concerns because algorithmic recommendations can influence judicial outcomes. The COMPAS risk assessment example illustrates how such tools can trigger appeals and concerns about disproportionate sentencing, even when judges can still use the tool with warnings about limitations.

Beyond operational efficiency, AI introduces legal implications around discrimination, fairness, and bias. Algorithms may discriminate by proxy using variables like name, ZIP code, or income as substitutes for protected characteristics, while their limited explainability complicates oversight. Regulation is evolving mostly through existing legal frameworks rather than dedicated AI laws, with the EU leading via GDPR (including restrictions on solely automated decisions), product liability rules, and non-binding Trustworthy AI ethics guidelines. The US has limited AI-touching regulation, notably around autonomous vehicles, and both regions tend to favor soft-law, multi-stakeholder approaches to sustain innovation.

• Applications of AI in the legal industry fall primarily into NLP applications and predictive regression-style applications.
• NLP can assign labels such as “relevant” or “not relevant” to documents.
• Audio or video can be made searchable by converting speech to text.
• Contract review is described as a mature AI use case in the legal industry.
• Document automation is described as another mature AI use case for legal work such as NDAs and patent applications.
• Predictive applications include forecasting lawsuit risk in written communications, case processing time by a judge, and lawyer success rates by case type.
• Predictive and decision-support applications have limited explainability, creating challenges in defending outcomes in court.
• COMPAS provided courts with recommendations about the likelihood of criminal re-offense and has contributed to appeals tied to sentencing outcomes.
• GDPR Article 22 states that an EU citizen has the right not to be subject to a decision based solely on an automated process.
• Approximately 60% of US states have adopted some form of legislation defining requirements for testing or using autonomous vehicles.

• “Applications of Artificial Intelligence in the legal industry fall in two primary categories: Natural Language Processing (NLP) applications, and predictive regression-style applications.”
• “The most logical business applications for AI in a legal setting is to help handle the numerous documents that lawyers, judges, paralegals, etc., must process in order to do their work efficiently.”
• “Regression models in AI attempt to make the most accurate predictions possible and are driven by performance, while traditional statistical methods attempt to understand the relationship between variables and is driven by significance of the relationship.”
• “Predictive applications are appealing but come with certain limitations.”
• “These solutions also have limited explainability, meaning that the model cannot provide a reason why each individual outcome was reached.”
• “Discrimination will look very different in the future when decisions are supported by algorithms.”
• “Current legal definitions of discrimination are based on distinct groups like gender, race, religion, sexual orientation; this is discrimination by proxy.”
• “There is no legislation on AI algorithms that clearly states if fairness should be aspirational… or if it should be reflective of the current environment.”
• “The general public will assume that fairness and the elimination of bias are the ultimate goals, and forget that some AI systems are meant to be biased.”
• “Artificial Intelligence in its many forms… is included in legislation, just not explicitly.”