Artificial Intelligence and the Law
Generative Artificial Intelligence (AI) presents attorneys with an opportunity to evolve as legal practitioners, to increase efficiency, to expand abilities, and to transform the delivery of legal services—making them faster and more cost effective for clients. That is of course, if you can master the technology and learn how to interact with it in a way that produces the best results. In this post, I will detail some of the prompt engineering techniques attorneys need to effectively implement AI in their practice, and some of the challenges they face.
The integration of AI has the potential to be a transformative and positive addition for both lawyers and clients. AI, exemplified by cutting-edge systems like those used by Spellbook, Westlaw, and LexisNexis, holds immense potential to revolutionize a vast swath of legal activities. These AI models, trained on vast datasets, can conduct legal research, and generate coherent and contextually relevant content, ranging from case synopses to contract clauses, with remarkable accuracy and efficiency.
Beyond the basics of document review and legal research, AI technologies also can offer a nuanced array of applications tailored to specific legal domains. For instance, predictive analytics powered by AI can forecast litigation outcomes, enhancing strategic planning for trial lawyers. In corporate law, AI-driven tools can streamline the due diligence process by rapidly analyzing contracts and identifying potential risks in merger and acquisition transactions. Furthermore, natural language processing (NLP) technologies enable more sophisticated interaction with legal databases, allowing for queries that mirror natural human language and improve the relevancy of search results. Understanding these technologies’ underpinnings, from machine learning models to NLP algorithms, is crucial for attorneys looking to leverage AI effectively. By grasping the technical mechanisms, lawyers can better assess which tools are most appropriate for their particular legal tasks, ensuring they harness AI’s full potential.
AI tools can significantly enhance productivity and enable lawyers to handle complex tasks with greater speed and precision. By automating routine tasks such as document review and drafting, and discovery, AI allows lawyers to focus their expertise on higher-level strategic analysis and client advocacy, ultimately delivering enhanced value to their clients.
Embracing AI: The Need for Lawyers to Adapt
It is imperative for lawyers to embrace and harness the capabilities of AI. This entails a shift in how lawyers approach their work, requiring them to acquire new skills and adapt to evolving technological landscapes. Though the prospect of integrating AI into legal practice may initially seem daunting, it offers myriad opportunities for professional growth and innovation.
To effectively utilize Generative AI tools, lawyers must train themselves in how to interact with its algorithms to generate end results they can use. One way to obtain this technical proficiency and strategic acumen is to actually start using it. When approaching AI in your work, don’t be discouraged by the initial result. Try tackling the same problem in different ways. Refine your query construction techniques. Use more specificity, qualifiers, and modifications. And hone your critical thinking skills to evaluate and interpret AI-generated outputs.
Prompt Engineering
For legal professionals new to AI, mastering prompt engineering techniques is crucial for effective utilization of these tools. When it comes to Generative AI like ChatGPT, prompt engineering becomes incredibly important.
Prompt engineering is the skill of talking to AI and generating accurate and relevant results. If you turn to AI and ask for an output on a particular topic, without effective prompt engineering, it is likely to give you a generic and bland response. Interacting with AI is all about good communication skills, it’s not about writing in code. When you get a bad response, the problem is not the AI, it’s the user.
When using ChatGPT many people advocate for using the RISEN prompt engineering technique. Here is an overview of the RISEN technique:
- ROLE – State the role or perspective from which the output is to be generated. Basically, tell the AI who it should act like You can say “Act as a litigation attorney.” That is the R in RISEN.
- INPUT or INSTRUCTIONS – Right after the Role, include a data input or instructions. You need to say exactly what you want the AI to do. For example, “Write me a legal overview of the statute of frauds in California.” This can include a question, a statement, a set of data, or instructions on what to generate.
- STEPS- Give the exact set of steps you would give as if you are giving instructions to a human being. For example, “Start with an interesting introduction, followed by a definition of what the statute of frauds is, and then give me three main body points, each with examples, and finish with a conclusion that will result in people contacting me through my website.” The more articulate you are with your steps, the better.
- EXPECTATION or END GOAL- For this step, tell ChatGPT exactly what you expect the outcome to be. For example, “This relates to a matter where one party sold a car to another party without using a written contract and the selling party was never paid. The goal is to educate the selling party on how the statute of frauds could impact their case.”
- NARROWING- Give constraints and narrow the scope of what you want AI to produce. For example, “Don’t include a discussion of the statute of frauds as it relates to real estate sales.”
Though RISEN is just one technique for effective prompt engineering, every generative AI algorithm has its own nuances on how to produce good results, and attorneys will need to train up on effective prompt engineering techniques for whatever AI they will be using.
Challenges Faced by Legal Professionals Using AI
Despite the tremendous potential of Generative AI, its integration into legal practice is not without challenges. Legal practitioners may encounter hurdles in learning and implementing AI technologies, including the following:
- Technical Complexity: Generative AI systems operate on complex algorithms and require a nuanced understanding of machine learning principles, posing a potentially steep learning curve for lawyers unfamiliar with computational concepts.
- Ethical Considerations: The use of AI in legal practice raises ethical concerns regarding algorithmic bias, and the protection of client confidentiality. Legal professionals must navigate these ethical complexities while leveraging AI tools responsibly and ethically. Another significant concern is the potential for algorithmic bias, where AI systems might inadvertently perpetuate or amplify existing prejudices. This risk is particularly acute in sensitive areas such as criminal justice, where biased AI could affect sentencing recommendations or parole decisions. Additionally, the safeguarding of client confidentiality in the era of AI demands rigorous attention.
- AI Errors: AI is not perfect, and it has been known to generate erroneous and inaccurate results. Thoroughly reading and fact checking case citations and legal analyses is crucial, and using AI requires an investment of attorney time and attention to make certain any outputs are actually accurate and relevant. The potential for errors in AI outputs necessitates thorough review by legal professionals. For instance, AI-powered legal research tools can expedite initial research, but lawyers must still analyze the findings and ensure their accuracy.
- Job Losses: Though AI offers numerous advantages, it’s important to acknowledge concerns regarding potential job displacement within the legal profession. As AI automates tasks, some legal roles may evolve or change.
Anticipating the Future Implications of AI in Legal Practice
As AI technologies continue to evolve, their impact on the legal profession will deepen. One can envisage a future where AI not only automates routine tasks but also provides sophisticated insights that shape legal strategy. This future landscape will likely require lawyers to possess a new set of skills, blending traditional legal acumen with a robust understanding of AI technologies.
The role of legal education will also need to adapt, preparing new lawyers for a tech-driven legal environment with courses on AI ethics, data science, and technology law.
Navigating this future will demand thoughtful engagement with the ethical, practical, and professional challenges that accompany the rise of AI in law. By anticipating these developments, the legal profession can position itself to embrace the opportunities of AI while safeguarding the principles of equity and confidentiality that underpin the justice system.
Conclusion
The legal profession is well suited to incorporate AI in meaningful and beneficial ways for both lawyers and clients. Becoming proficient in prompt engineering techniques and learning to efficiently utilize AI on a wide range of platforms will help attorneys achieve better outcomes for their clients, and elevate the quality of their work.
The integration of AI presents both opportunities and challenges for the legal profession. Though AI can enhance efficiency and productivity, lawyers must navigate these advancements thoughtfully to mitigate potential drawbacks and ensure the ethical use of this technology.
