Legal Innovation through Frontier Technology Lab (liftlab)
The mission of the Legal Innovation through Frontier Technology Lab (liftlab) is to increase access to high quality legal services in the private sector by leveraging artificial intelligence (AI) and other frontier technologies. We develop, investigate and evaluate AI in legal education and private practice that is designed to both reduce the cost and increase the quality of legal services in a sustainable and responsible way. To bridge the gap between theory and practice, our work extends beyond conceptualization and encompasses the building of prototypes that help explore the utility of AI-based solutions. In doing so, we are uniquely positioned to make research contributions with the potential for immediate practical relevance in a rapidly changing field.
Key Pillars
Our work is organized around four Key Pillars with the underlying goal to empirically evaluate and promote the quality of legal services in the private sector. We anticipate a multi-prong research effort across verticals of legal analysis, tech development, and stakeholder collaboration.
Highlighted Projects
- Legal Personas: We are examining the potential of large language models (LLMs) to elicit, synthesize and encode implicit knowledge held by experienced practitioners. Our aim is to assess the extent to which such tailored models can serve as effective representations of expert judgment, thereby advancing both empirical understanding and methodological development in the study of legal practice.
- Contractual Drafting Risk Assessment (via MCC): We are employing AI to identify common causes of contract interpretation disputes, such as linguistic vagueness or indeterminacy. We then feed that information into the contract production process, in turn enabling legal personnel to write better contracts.
- Multi-Agent Simulation for Evaluation: We explore possible applications of agentic AI to the legal field, e.g. in analyzing the internal processes of law firms through simulations.
- AI-driven simulation training (e.g. Atelier): We are developing and evaluating AI training platforms to enable young attorneys and law students to master negotiation and other skills in legal practice through simulation tailored to real-world scenarios.
- Applied Mechanistic Interpretability in Legal Contexts (e.g. Breaking Down Bias): We are developing novel approaches to identifying and mitigating racial bias in LLMs. Using a method known as model pruning (dissecting the internal structure of LLMs), we selectively deactivate or remove specific neurons that were identified as contributing to biased behavior.
Featured Video
The Legal Innovation through Frontier Technology Lab, or liftlab, explores how artificial intelligence can reshape legal services—not just to make them faster and cheaper, but better and more widely accessible.
News
Stanford Law rolls out liftlab to rethink AI’s role in the legal profession
EdTech Innovation Hub
Nyarko said, “The legal profession has not just an opportunity, but an obligation, to do more than just boost efficiency. We want to understand how technology can surface the expertise and reasoning that underpin the legal system in order to deliver better legal advice, prevent disputes, efficiently train new lawyers,…
Read More : Stanford Law rolls out liftlab to rethink AI’s role in the legal professionRigorous Evaluation
To promote the development and adoption of beneficial AI in the legal sector, we must establish rigorous and transparent evaluation standards. However, benchmarking in the legal domain presents unique challenges that our lab is actively addressing.
- Defining and Measuring Quality: Assessing the quality of legal services is notoriously complex, as it involves subjective elements like the creativity of a legal argument or the thoroughness of an analysis. We are collaborating with industry partners to create precise, measurable criteria for evaluating legal AI, moving beyond simple metrics to capture the nuanced advantages a tool may provide.
- Human-Machine Interaction: High performance on a benchmark does not necessarily translate into practical utility. Recognizing the importance of evaluating the full legal workflow, we focus on complex, collaborative tasks (such as due diligence) to empirically assess how AI tools may enhance or impede overall performance. This approach can yield new insights into the ways technology integrates with, and potentially improves, the practice of law.
AI to Elevate Legal Services
Many current AI innovations focus on automating routine tasks to increase speed and reduce costs—work that junior associates could typically handle. While valuable, this approach only scratches the surface of potentially beneficial AI use cases. We believe that a more creative application of AI can fundamentally improve the quality of legal services. Instead of merely automating existing workflows, we aim to develop AI that augments the work of legal experts and uncovers novel insights from large datasets.
The Future of Legal Education and Training
The accelerating integration of artificial intelligence into legal practice is reshaping the competencies required for professional development. Simultaneously, law schools have been slow to adapt, and for recent law graduates, many enter the practice without prior experiential knowledge. We explore methods that can provide law students and young lawyers with continuous training in the practice of law. These tools complement traditional legal education, e.g. by allowing participants to apply theoretical concepts via simulation in realistic, interactive scenarios, thereby bridging the gap between academic knowledge and the skills required in an evolving legal environment.
Advanced Methods for Legal Analysis
Artificial intelligence presents powerful new methodologies for legal analysis. By processing large corpora of legal texts, AI systems can detect emergent patterns in legal reasoning and identify novel argumentative strategies. Concurrently, advances in the field of mechanistic interpretability aim to elucidate the internal decision-making processes of these systems, thereby reducing their opacity. For the legal profession, such developments hold the potential to enhance the detection and mitigation of biases in AI models, providing new mechanisms for risk management and supporting the fair and ethical deployment of these technologies in legal contexts.