A Short & Happy Guide to Artificial Intelligence for Lawyers
An accessible guide to AI across criminal, commercial, intellectual property, employment, and consumer law—and its implications for legal practice.
By James M. Cooper and Kashyap Kompella
Understanding AI—and the law it changes.
Artificial intelligence raises questions across legal practice: how decisions are made, who is responsible for harm, and how existing rights apply to new technologies. James M. Cooper and Kashyap Kompella give legal readers an accessible grounding in AI and examine its consequences across the profession.
The guide brings together legal and technological perspectives, drawing on research, reports, and industry material. It introduces the concepts without assuming a technical background, then explores the issues lawyers encounter in advising clients, assessing evidence, and responding to changes in the law.
Inside the book
- Foundations: algorithms, automated decisions, bias, transparency, legal analytics, and the role of human judgment.
- Criminal justice: predictive policing, risk assessment, pretrial and post-trial applications, and the challenges of relying on automated systems.
- Commercial and intellectual property law: banking, insurance, contracts, securities, competition, corporate governance, and questions about AI-generated work and ownership.
- Employment and consumer protection: hiring decisions, wage discrimination, workplace safety, healthcare, and facial recognition.
- Ethics and regulation: corporate principles, professional guidance, and approaches in the United States, European Union, and China.
- The profession’s future: legal education, litigation, judicial skills, and access to justice. Appendices include a glossary, legal technology applications, and AI in film.
Who it is for
Attorneys, judges, judicial administrators, paralegals, and law students who want to understand AI’s significance for their work. Readers can begin with the foundations or turn to the practice areas most relevant to them.
Browse the table of contents (PDF) ↗
- Published
- 2024
Questions about AI for legal professionals.
Who should read Artificial Intelligence for Lawyers?
The guide is written for attorneys, judges, judicial administrators, paralegals and law students who want to understand AI and its implications for legal work.
The relevant questions extend beyond using a new drafting tool. Lawyers may need to examine data rights, confidentiality and the implications of unreliable output in an organization’s AI plans. Kashyap’s responsible-strategy article outlines these risk areas and gives readers a broader context for advising on adoption. Read more about the risks in generative AI strategy.
Does the guide require programming knowledge?
No. James M. Cooper and Kashyap Kompella explain core AI concepts in accessible language, alongside ethics, regulation and applications across legal practice.
Understanding the system’s limitations matters more than familiarity with technical vocabulary alone. For instance, connecting an AI tool to documents does not guarantee that it will retrieve or interpret them correctly. Kashyap’s article on retrieval-based systems explains this distinction in terms relevant to professional use. Read more about why source access does not guarantee accuracy.
Why do lawyers need to understand AI hallucinations?
A fluent answer can contain invented information. Kashyap’s legal education work stresses the danger of overreliance, making verification an essential part of using these tools.
Treat an answer as something to examine before relying on it. Consider what information the tool was permitted to use, the task it was approved for and who will review the result. Kashyap’s language-model risk guide connects these decisions with policies, monitoring and responsibility for correction. Read more about managing language-model risk.
Why does AI literacy matter for legal judgment?
Understanding how AI works helps lawyers assess its uses and limitations. Kashyap’s published discussion of the book places legal professionals at the center of putting responsible AI into practice.
Professional competence needs to be connected to actual responsibilities. Knowing how to request an answer is different from knowing whether it is suitable for a particular use. Kashyap’s workforce-skills analysis makes the case for learning that addresses verification, information protection and the systems people work with. Read more about professional AI skills.