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As summer arrives and my grading season has ended, I find myself eagerly anticipating some quality time experimenting with generative AI. After a spring semester filled with teaching a course on AI and the Law and conducting numerous prompting experiments, I am ready to continue my exploration.

This excitement is tempered by the sheer volume of AI programs and self-proclaimed experts emerging in the legal field seemingly on a daily basis. It seems that so many say that they are gurus and geniuses and know so much, but I can’t help but feel like a student and that there is still so much for us all to learn and we need much more time before we make proclamations.

Lately, it feels like I’ve been inundated with announcements of programs promising definitive answers and best practices for using generative AI in law. These proclamations often make me scratch my head in bewilderment. As someone who considers himself a perpetual learner, I find these definitive statements somewhat premature. The reality is that the field of AI, particularly generative AI, is still evolving, and our understanding is far from complete. Seriously, we don’t even know “best practices” for human activities, so I’m doubtful we have reach the “best practices” stage on AI already.

In this post, I want to share my thoughts on how to critically evaluate the ever-growing claims of expertise in legal AI. To navigate this landscape, I suggest adopting a 3 Hs approach: Homework, Hands-on work, and Humility.

Homework:

The first step is to do your homework. It’s crucial to research and understand the backgrounds of those claiming to be experts. What are their qualifications? What kind of work have they done? When did their AI experience and expertise begin? By digging deeper into their credentials and experiences, you can better determine the reliability and value of their insights.

Hands-on Work:

Next, put in the hands-on work. There is no substitute for personal experience when it comes to understanding generative AI tools like GPT-4. Experiment with these tools, run your own tests, and see firsthand what they can and cannot do. This practical experience will not only enhance your understanding but also allow you to critically assess the claims made by others. I consider this approach mandatory and it is as close as I come these days to calling something a “best practice.”

Humility:

Finally, approach this subject with humility. Despite the rapid advancements in AI, we are still in the early stages of understanding its full potential and limitations. The legal field, with its unique complexities and nuances, presents particular challenges for AI implementation. A humble approach acknowledges that we are all learning and that there is still much to discover. And a key part is being willing to learn from those outside the legal profession.

I was genuinely surprised to see self-proclaimed legal AI experts making sweeping statements about what ChatGPT-4.0 could do within 24 hours of its release. A bit of a rush to judgment?

I still have a lot of work to do to fully grasp the capabilities and implications of these tools, and I am looking forward to that work.

As we venture further into the era of legal AI, let’s prioritize continuous learning, critical thinking, and a willingness to admit when we don’t have all the answers. By doing so, we can separate the wheat from the chaff and truly advance our understanding of how AI can benefit the legal profession.

If you are in Michigan and are interested in hearing more about my current approaches to AI and experiments, I’ll be speaking at the State Bar of Michigan’s Great Lakes Legal Conference (https://www.michbar.org/GLLC) on June 14 and 15 on Mackinac Island.

NOTE: The phrase “lions and tigers and bears, oh my!” is a famous line from the movie “The Wizard of Oz.” In the movie, Dorothy and her friends chant it as they venture into a dark and unknown forest, expressing their fear of the potential dangers ahead.

[Originally posted on DennisKennedy.Blog (https://www.denniskennedy.com/blog/)]

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I’m wrapping up a busy semester. Lots of AI. I taught a class called AI and the Law and did a 3-session mini-class called the LegalRnD Studio at Michigan State University College of Law to teach students and others about AI prompting. I have a couple of other AI CLE presentations coming up in June at the Great Lakes Legal Conference. I also finished reading Ethan Moillick’s great new book on generative AI – Co-intelligence: Living and Working with AI. Spoiler Alert: With a couple of smallish exceptions, I’m very much in alignment with Mollick’s approach.

I’ve done a ton of AI experiments this semester, primarily in GPT-4, but they feel like they were a bit ad hoc and scattered to me. I want to focus on a major coordinated AI project for the summer.

As I wait for final papers to be submitted for me to grade, I’ve taken a little time to think about what that big project might be. And I’ve found it.

In my AI and the Law class, we discussed several times the idea of “AI as assistant” or “AI as agent.” More specifically, we talked about learning assistants and ways to personalize AI learning tools. That’s where I’m heading.

I plan to experiment with personalized learning tools in many ways – many tools, many personalizations, and many personal learning assistant prompts added to my AI prompt toolbox.

I’m waiting until after I submit grades to start on this project, but I can already feel it start to take shape. I plan to share updates on my progress from to time this summer.

[Originally posted on DennisKennedy.Blog (https://www.denniskennedy.com/blog/)]

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And this blog reaches age 21. I’m planning a light celebration with cake, but the blog is lobbying hard for drinks and a night out. That’s been an ongoing source of tension.

I was asked in maybe 2006 how long I expected to keep writing this blog. My answer was that I couldn’t really see a time that I would stop, although I expected that what and how I wrote would evolve over time. That’s been true.

My biggest surprise with the blog was that I named it “DennisKennedy.Blog” as a joke that blogs would ever rise to the level of getting a .blog top-level domain. The joke was on me when that in fact happened and I was forced to buy the .blog domain name.

Today, I’m reflecting on how much I’ve put on this blog over the years and the many topics and themes I’ve covered. It’s striking to me in retrospect how much “innovation” in many forms has been the subject of my posts since the beginning.

I used to be considered a prolific blogger, but the number of posts has decreased over the years. That’s mainly because most of my best ideas and content are now going into The Kennedy-Mighell Report podcast (just about to enter its 17th year).

I have some plans to be more regular in blog posting in 2024 as part of my move away from Twitter. I even set a Minimum Posts Target (MPT) for this year as a SMART goal.

As I do every year, I thank all the readers of the blog, especially the longtime readers and those who have told me about how my posts have helped, and look forward to the new paths and directions this blog takes.

Maybe we will have that drink after all. The 21st birthday is a special one. But I don’t expect to be talked into going out in the snow.

[Originally posted on DennisKennedy.Blog (https://www.denniskennedy.com/blog/)]

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I had hundreds of reasons to leave Twitter/X in 2023. However, I found that more difficult to do than I would have expected. So, I lingered there in a kind of downward-drifting limbo.

I told many people over the last several months that the most difficult issue for me in leaving was saying goodbye to my Twitter persona, which I had grown quite fond of over the past sixteen years.

That persona grew and evolved over sixteen years and it had a voice I enjoyed writing with, once I learned who it was. I was very reluctant to let it go.

But now is the time to bring it to an end and, to paraphrase Lebron James, either take its talents to “somewhere else” or let it retire. I’ve decided on the latter. This is the announcement. And, I hope, an appropriate send-off from me.

As I tried to make The Decision, I finally realized that my Twitter persona had found its home on Twitter, but that Twitter no longer existed. Like many, I don’t even like to use “X” and still default to “Twitter” and “tweets.”

Whatever X is or becomes, it no longer feels anything like a home for my Twitter persona. And I’m finding no desire to craft and build a new “X” persona. It’s time to retire my Twitter persona.

I have found recently that my Twitter persona occasionally is making appearances elsewhere, like Zoom chats. That was also a signal to me that it was ready to leave X. Places like Zoom chats might become its every-now-and-then venue. It might also just go out to pasture after serving me and, I hope, its audience well for many years.

I’ve captured the archive (40,000+ tweets is, well, a lot of tweets). I will still maintain the @denniskennedy Twitter/X handle and make enough posts to keep the handle alive in the X world, but will not use the Twitter persona and voice there. It’ll be much more announcement-y in tone.

Look for this blog and LinkedIn to become my two main channels in 2024, along with The Kennedy-Mighell Report podcast.

I resisted shutting down my Twitter persona for a long time, even though I knew the time had come to do so. The first day of 2024 felt like the right time to take that step and, with some mixed emotions, I’ve decided to do so and say goodbye to one of my favorite characters I’ve ever created. I want to do this in a way that feels like a retirement choice by me and not an eviction, which it would likely have soon become.

I’ll miss you, my old friend. I agree with you that this is the right time and the right way to go.

Image of my twiiter handle - bike in background with headshot of me in mask

[Originally posted on DennisKennedy.Blog (https://www.denniskennedy.com/blog/)]

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As an avid reader, for many years I have set the goal of reading 52 books in 52 weeks. It’s ambitious, but reading is a hobby and this exercise helps me keep track of what I’ve read. In 2023, I read 92 books, exceeding my goal by quite a bit. Or, more accurately, I listed 92 books that I read. I don’t list books that might reveal certain things I might (or might not) be working on.

Picasso like image of people gathered and looking at book

In 2022, the total number was 80.

I again found that I was starting and abandoning quite a few books. And I’m reading way more audiobooks than ever before – largely a function of convenience and the trickiness of fonts and font sizes as I get older.

If you forced me to pick my top books for 2023 (in alphabetical order) that I’d recommend, I’d probably list:

  • Artificial Intelligence, Melanie Mitchell
  • Breaking Through, Katalin Kariko
  • Bridge, Lauren Beukes
  • The Earth Transformed, Peter Frankopan
  • Every Man for Himself and God Against All, Werner Herzog
  • Excellent Advice for Living, Kevin Kelly
  • Moshe Feldenkrais: A Life in Movement, Mark Reese
  • Saving Time, Jenny Odell
  • The Smart Mission, Edward Hoffman, et al
  • Unmasking AI, Joy Buolamwini

I’m doing the same thing in 2024. My approach is the same as in previous years – I’ll simply update this post from time to time from time to time throughout the year as I finish books.

I’ve enjoyed doing this challenge every year and hope you find the list useful. And I encourage you to take the challenge yourself.

I welcome your recommendations of good books I might read this year.

As Bill Taylor says, “Are you learning as fast as the world is changing?”

Challenging yourself to read 52 books is probably a good way to start to answer that question.

December

November

October

September

August

60. How the World Ran Out of Everything, Peter Goodman
59. The Infernal Machine, Steven Johnso
58. The Presidents and the People, Corey Brettschneider
57. Brave New Words, Salman Khan
56. A Death in Cornwall, Daniel Silva
55. 1177 BC, Eric Cline
54. The Maltese Falcon, Dashiell Hammett
53. 3 Shades of Blue, James Kaplan
52. The Revolutionary: Samuel Adams, Stacy Schiff
51. The Wolves of K Street, Brody Mullins and Luke Mullins

July

50. The Incorruptibles, Dan Slater
49. The Talented Mrs. Mandelbaum, Margalit Fox
48. Misbelief, Dan Ariely
47. Stories are Weapons, Annalee Newitz
46. Ask Not, Maureen Callaheen
45. Craig Johnson, First Frost
44. All the Worst Humans, Phil Elwood
43, After 1177 B.C, Eric CLine
42. Code Dependent, Madhumita Murgia
41. The Age of Magical Overthinking, Amanda Montell

June

40. The Great River, Boyce Upholt
39. The Man Who Came in from the Cold, John Le Carre
38. The Explorers, Amanda Bellows
37. The Demon of Unrest, Erik Larson
36. Hellhound on his Trail, Hampton Sides
35. The Devil’s Chessboard, David Talbot
34. The End of Everything, Victor Davis Hanson

May

33. Where God was Born, Bruce Feiler
32. A Brief History of Intelligence, Max Bennett
31. Blood and Thunder, Hampton Sides
30. The Light Eaters, Zoe Schlanger
29. Empire of the World, Sathnam Sanghera
28. ADHD is Awesome, Penn and Kim Holderness
27. The Algebra of Wealth, Scott Galloway
26. Lost Birds, Ann Hillerman
25. The 6 Types of Working Genius, Patrick Lencioni
24. The Wide Wide Sea, Hampton Sides

April

23. Co-intelligence, Ethan Mollick
22. Glad We Met, Steven Rogelberg
21. A Fever in the Heartland, Timothy Egan
20. The Anatomy of Fascism, Robert O. Paxton
19. 10X is Easier than 2X, Dan Sullivan and Benjamin Hardy

March

18. How to Win an Information War, Peter Pomerantsev
17. Burn Book, Kara
16. Slow Productivity, Cal Newport
15. Zen and the Art of Motorcycle Maintenance, RObert Pirsig
14. Supercommunicators, Charles Duhigg
13. The Lantern’s Dance, Laurie R. King

February

12. Technofeudalism, Yanis Varoufakis
11. Sins of the Shovel, Rachel Morgan
10. Germany 1923, Volker Ullrich
9. Eighteen Days in October, Uri Kaufman
8. How to ADHD, Jessica McCabe

January

7. The Almanack of Naval Ravikant, Eric Jorgenson, ed.
6. Artificial Intelligence, Michelle Mitchell
5. Right Kind of Wrong, Amy Edmondson
4. Damascus Station, David McCloskey
3. Endgame, Omid Scobie
2. The 32 Principles, Rener Gracie
1. The Worlds I See, Fei-Fei Li

[Originally posted on DennisKennedy.Blog (https://www.denniskennedy.com/blog/)]

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The end of the year is a time of sharing at DennisKennedy.Blog. I have a special set of gifts for readers of the blog to celebrate the end of 2023.

Help yourself to one or all of these:

  1. Free PDF full version of my book, Successful Innovation Outcomes in Law: A Practical Guide for Law Firms, Law Departments and Other Legal Organizations.
  2. Free PDF handout of the materials for my approach to Personal Quarterly Offsites, the single most effective approach to planning and productivity I’ve found and consistently practice.
  3. For Generative AI experimenters, my white paper, “Adding a ‘Group Advisory Layer’ to Your Use of Generative AI Tools Through Structured Prompting: Using Personas for Advisory Boards, Task Forces, Mastermind Groups, and Other Collections of Personas to Assist in Evaluations, Assessments, Recommendations, Decision-making, and much more (Including Law-related Examples)“
  4. The Fresh Voices on Legal Tech interview series on The Kennedy-Mighell Report podcast. The most recent one was with Catherine Bamford.
  5. My article, “Displaying and Evidencing Contract Terms in a Post-Visual Era,” which was one of my favorite pieces of writing from 2023
  6. For members of the LegalTech Hub, my column on law department innovation.
  7. For those working in law department innovation, my Law Department Innovation Library.

This list will give you a good selection of reading for the end of the year and the beginning of next year. Enjoy!

Happy holidays and new year to all.

[Originally posted on DennisKennedy.Blog (https://www.denniskennedy.com/blog/)]

DennisKennedy.com is the home of the Kennedy Idea Propulsion Laboratory

Want to schedule a Zoom call to talk with me about Legal Innovation as a Service, Speaking, or other services? Schedule a Zoom with Dennis via Calendly.

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Ethical Implications of Generative AI for the Michigan Lawyer: Navigating the Digital Landscape

Frequently Asked Questions (December 2023 Version)

Dennis Kennedy*

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Executive Summary

How might the evolution of generative AI impact the legal profession in the next 5-10 years?

In the rapidly evolving landscape of legal technology, AI has emerged as a powerful tool with transformative potential. This list of Frequently Asked Questions (“FAQ”) is tailored for non-technical lawyers and seeks to demystify the ethical and operational aspects of AI in the legal arena. From understanding the basic definitions of AI and its integration in the legal sector to diving deep into the duty of technology competence, this FAQ addresses many key issues.

Central to the discussion is the lawyer’s ethical duty in an age of automation: ensuring competence, safeguarding client data, supervising non-lawyers effectively, and representing clients at a high level. In an era where technology and ethics intertwine even more tightly, this FAQ will help you navigate this complex and changing domain confidently.

The bottom line: it’s all about striking the right balance between leveraging AI’s capabilities and ensuring ethical practice.

Frequently Asked Questions

  1. What is generative AI and why should lawyers care about it? Generative AI refers to a subset of AI systems designed to generate content, whether it be text, images, or even legal documents. It is based on advanced machine learning models that learn patterns from vast amounts of data and can produce novel outputs based on that learning. reshape practices, making them more efficient and perhaps even more equitable. It is already considered to be a transformative technology. As legal professionals, understanding generative AI is crucial because of its potential to transform various facets of the legal process, from research and drafting to client interactions.
  2. Why is there so much buzz about generative AI in the legal sector? GPT4 advanced generative AI technologies to the point where an AI could achieve a passing score on the bar exam. Generative AI stands at the current frontier of legal technology innovation. By automating certain processes and introducing new capabilities, it offers opportunities to This technology promises to make certain legal processes more efficient, potentially revolutionizing how lawyers work.
  3. Are there ethical concerns about using AI in legal practice? Absolutely. Concerns range from data privacy and potential biases in AI-generated advice, to over-reliance on technology and the erosion of the human touch in legal practice. It’s crucial for lawyers to approach AI tools with a critical mind and ethical diligence. As AI becomes more integrated into legal practice, the duty of competence may evolve to include understanding and appropriately using AI tools, ensuring they align with ethical standards and don’t compromise the quality of legal services.
  4. How might AI tools impact the attorney-client relationship generally? AI has the potential to both enhance and challenge this relationship. While AI can improve efficiency and reduce costs, over-reliance without transparency might erode trust. Ensuring clear communication about the role of AI in legal processes is essential. While the core fiduciary duty—to act in the client’s best interest—remains unchanged, how lawyers uphold this in an AI-integrated practice might evolve, emphasizing transparency, tech competence, and responsible tool integration.
  5. What is the lawyer’s duty of technology competence? The duty of technology competence mandates that lawyers must maintain sufficient knowledge about the benefits and risks associated with relevant technology. It is set out in Comment 8 to the Duty of Competence (Rule 1.1). As legal practice increasingly relies on tech tools like AI, understanding and using them ethically and effectively becomes essential. As AI tools become commonplace in legal processes, the duty of technology competence may expand to cover understanding these tools, their benefits, limitations, and potential ethical pitfalls, ensuring lawyers use them responsibly. Beyond understanding the AI tool itself, ensuring competence means corroborating AI-generated advice with traditional methods, seeking second opinions when unsure, and continuously updating one’s knowledge about the technology.
  6. Are there guidelines on how lawyers should ethically use AI? Several legal associations are formulating guidelines. Familiarizing oneself with resources form the State Bar of Michigan, the American Bar Association and the resources set out at the end of these materials can offer guidance on the ethical use of AI in legal practice.
  7. What are the implications for attorney-client privilege when using generative AI? If generative AI tools access and process sensitive client information, there’s a potential risk to confidentiality. Ensuring that these tools respect and safeguard attorney-client privilege is paramount. Ensuring that AI tools respect and safeguard attorney-client privilege is paramount. This involves vetting vendors rigorously, ensuring data encryption, and understanding where and how client data is stored and processed. AI tools, especially those cloud-based, might inadvertently expose sensitive client communications if not properly secured. Lawyers must ensure that AI tools adhere to stringent security standards to maintain attorney-client privilege.
  8. How does the duty of confidentiality intersect with AI use? When using AI tools, especially cloud-based ones, lawyers must ensure client data is encrypted, stored securely, and not accessible by unauthorized parties, upholding the duty of confidentiality.
  9. How might generative AI perpetuate or amplify biases? What’s the ethical concern? Like all AI systems, generative AI learns from data. If the data contains biases, the AI might perpetuate or even exaggerate those biases. For lawyers, this can raise serious ethical concerns, especially if AI-generated advice or documents unintentionally favor one party over another due to these biases. AI systems, including generative models, can manifest biases from their training data. This raises ethical concerns, especially if biased AI-generated advice inadvertently favors one party over another, leading to unjust outcomes. subtle biases might not manifest immediately. It’s crucial to remain vigilant, consistently test AI tools, and ensure diverse and representative training data to minimize such biases.
  10. I’ve heard about transparency in AI. Why is it important ethically for lawyers? Transparency refers to understanding how an AI system arrives at its decisions. For lawyers, it’s essential to be able to explain to clients, courts, and other stakeholders how certain AI-driven decisions or recommendations were reached, ensuring trust and upholding our ethical obligations. “Black box” refers to AI systems where the decision-making process is opaque. For lawyers, it’s essential to understand and explain how AI recommendations are reached, both for trust and to ensure that decisions are made ethically and justly.
  11. What happens if an AI tool makes an error? Who’s responsible? The ethical responsibility often rests with the lawyer. While the AI tool may have generated the content, lawyers must exercise due diligence to ensure that the advice or document is accurate and appropriate. While generative AI can be a powerful tool, it’s not infallible. It’s crucial to double-check AI-generated advice and ensure that it aligns with legal standards and the specific nuances of each case. Understand the tool’s limitations, corroborate AI-generated advice with traditional research, and consider the specific nuances of each case.
  12. How do I supervise non-lawyers using AI tools in my practice? Supervision involves ensuring non-lawyers use AI responsibly, understand its limitations, and maintain client confidentiality. Regular training sessions, guidelines, and clear protocols can aid in effective supervision. Key concerns include ensuring the quality of work isn’t compromised, client data remains confidential, and that non-lawyers don’t unintentionally practice law or make critical legal decisions without proper oversight.
  13. Can AI tools replace paralegals or junior associates? While AI can handle some tasks typically assigned to paralegals or junior associates, the human touch, expertise, and judgment they offer remain crucial. AI should be seen as complementary, aiding rather than replacing these roles. Supervision will also be important.
  14. Will I be held ethically responsible if I don’t adopt AI tools in my practice? While not using AI won’t inherently breach ethical duties, if failing to use available technology leads to inefficiencies or oversights that harm clients, there could be ethical implications. Continuous learning and updating practice methods remain essential.
  15. How can AI tools affect client expectations in terms of service speed and cost? Clients might expect faster turnarounds and reduced costs as AI integration becomes more common in the legal sector. Managing these expectations while ensuring quality and ethical service is a delicate balance lawyers must strike.
  16. Does relying on AI compromise my ability to represent clients effectively? Not necessarily. If used correctly, AI can enhance representation by improving efficiency. However, over-reliance without understanding the tool can jeopardize the quality of representation.
  17. How can I balance efficiency, brought by AI, with ethical representation? Balancing involves using AI for routine and data-heavy tasks while ensuring that critical decisions, especially those requiring nuanced human judgment, remain in the hands of the lawyer. Open communication with clients about the role of AI is also crucial.
  18. How can AI affect client-lawyer communication? AI, especially chatbots, can streamline initial client interactions. However, it’s vital to ensure that AI tools don’t replace critical human-led conversations, especially when discussing complex legal matters.
  19. How can I ensure my clients are comfortable with the integration of AI in their legal process? Transparency is crucial. Clearly explain how and why AI tools are used, their benefits, potential limitations, and ensure clients that human expertise remains central to their representation.
  20. With AI predicting legal outcomes, how do I ensure I’m not over-relying on it? While AI can assist in predicting outcomes based on historical data, every case has unique nuances. Lawyers must combine AI insights with their expertise, understanding of current law, and specifics of the case at hand.
  21. Could AI lead to unauthorized practice of law (UPL) issues? Potentially. If AI tools are used to provide direct legal advice to clients without lawyer oversight, it could be seen as UPL. It’s essential to ensure that AI augments, rather than replaces, the lawyer’s role.
  22. Can AI-generated advice ever be considered “legal malpractice”? If AI-generated advice leads to significant harm due to its inaccuracy and a lawyer blindly follows it without due diligence, it could potentially lead to claims of malpractice. Lawyers must always verify and corroborate AI advice.
  23. Are there risks of AI tools being “over-trained” on specific types of data, leading to narrow or biased advice? If AI is trained predominantly on a narrow dataset, it might provide skewed advice. Diverse and comprehensive training data is essential to prevent such issues.
  24. How can AI tools affect the cost of legal services? With increased efficiency, there’s potential for cost reduction. However, lawyers must balance this with the ethical obligation to provide competent representation, ensuring that reduced costs don’t compromise quality.
  25. How can I vet AI vendors to ensure they align with ethical guidelines? Vetting involves understanding a vendor’s data sources, security measures, AI’s decision-making process, and any known biases or errors. Seeking independent reviews and testing the tool before full integration can also help.
  26. How might AI impact the client intake process? AI can streamline client intake by automating data collection, performing preliminary case analysis, and identifying potential conflicts of interest. However, a human touch remains essential to understand client needs fully.
  27. What are the potential pitfalls when AI tools communicate directly with clients? Direct AI-client communication might lead to misunderstandings, missed nuances in human communication, or inaccurate advice. It’s essential to monitor and limit AI’s direct interactions with clients.
  28. Are there ethical concerns surrounding the use of AI in legal advertising and client outreach? Yes. AI-driven advertising must be transparent, not misleading, and respect privacy regulations. Lawyers must ensure that AI doesn’t make exaggerated claims or target vulnerable individuals unethically.
  29. Can AI assist lawyers in maintaining records and ensuring compliance with record-keeping regulations? Absolutely. AI can automate record-keeping, ensure timely data backups, and alert lawyers to potential compliance issues or data breaches, assisting in upholding ethical standards.
  30. Could AI lead to a breach of the duty of loyalty to a client? If not used judiciously, AI might inadvertently expose client data or strategies, especially in firms representing opposing parties in different cases. Ensuring strict data segregation and security is essential.
  31. Can AI-generated documents be submitted as evidence in court? The admissibility of AI-generated documents as evidence is an evolving area. Lawyers need to understand the origin of such documents, their authenticity, and the technology behind their creation to make a compelling case for their inclusion.
  32. If AI assists in a legal decision that’s later challenged, how can I defend its use? Familiarizing oneself with the technology behind the AI tool, its training data, and decision-making process is key. This knowledge, combined with a lawyer’s judgment and expertise, will aid in presenting a robust defense.
  33. How do we ensure fairness and equality in the outcomes of generative AI tools? By critically assessing the data used to train these systems and regularly testing them for biases. It’s also essential to remain actively involved in the decision-making process, using AI as an assistant rather than a replacement.
  34. Can AI play a role in pro bono services and increasing access to justice? Indeed. AI can streamline routine tasks in pro bono cases, making legal services more accessible. Chatbots, for instance, can provide basic legal information to those who otherwise couldn’t afford it. Promoting AI literacy, offering community training, and creating subsidized or free AI tools for underserved populations can ensure a level playing field, preventing potential imbalances in access to justice.
  35. How can AI affect alternative dispute resolution processes? AI can help in analyzing past resolutions to suggest possible outcomes, making the mediation or arbitration process more efficient. However, human judgment remains critical in understanding the intricacies of each case.
  36. How can I vet AI vendors for ethical considerations? When selecting an AI vendor, ensure they prioritize data security, transparency, and have protocols to address biases. Understanding their data sources and training processes can also offer insights into their ethical standards.
  37. What might be the top ethical challenges I should prepare for in the future?
    The evolution of AI will likely bring challenges related to:
    • Greater autonomy in AI decision-making.
    • AI’s role in shaping legal standards and precedents.
    • Ethical considerations in globalized legal cases involving AI.
    • The balance between efficiency and human judgment.
  38. What especially difficult ethical issues might arise over the next three to five years?

• Transparency in AI Decision-Making: Ethical obligations of lawyers to understand and explain AI-powered recommendations.
• Accountability in AI Errors: Who bears the ethical responsibility when an AI tool makes an incorrect prediction or recommendation?
• Informed Consent with AI Tools: Ethical considerations when using AI in client interactions or on their behalf.
• Fair Representation Using AI: Ensuring AI doesn’t compromise a lawyer’s duty to provide competent representation.
• Authenticity of AI-Generated Documents: Ethical challenges in determining the authenticity of AI-generated evidence or contracts.
• Avoiding Over-reliance on AI: Ensuring AI tools don’t lead to complacency or ethical shortcuts in legal practice.
• Ethical Advertising and AI: Concerns about overstating the capabilities of AI-powered legal services in advertising.
• AI, Conflict of Interest, and Disclosure: Understanding potential conflicts when AI tools are developed by parties with vested interests.
• Client Autonomy and AI Recommendations: Respecting client decisions that diverge from AI suggestions.
• AI in Alternative Dispute Resolution: Ethical considerations when AI tools are used in mediations or arbitrations.
• Financial Incentives and AI Adoption: Addressing the ethical challenges when there are financial incentives for lawyers to adopt specific AI tools.
• Duty to Warn about AI Limitations: Ethical obligations to inform clients about the potential limitations or inaccuracies of AI tools.

  1. Should the use and/or non-use of AI be addressed specifically in client engagement letters? Reaching an understanding with clients about the use of AI is highly recommended, especially when you plan to be using it or your client has the expectation that you will use it. It is advisable to address generative AI permitted uses in your engagement letters, perhaps as a modification or addition to your generation provisions on use of legal technology, ideally giving you flexibility in making decisions about current and future uses of AI.
  2. Lastly, will AI replace human lawyers? Remember that AI is a tool, not a replacement. Prioritize interpersonal relationships, active listening, and empathy. Use AI to handle routine tasks, but let human judgment, ethics, and expertise drive the core of your practice. Finding the best place for humans to fit into the loop with AI will become a key lawyers skill of the future.

Some Useful Resources

  1. Melanie, Mitchell, Artificial Intelligence: A Guide for Thinking Humans. An excellent plain language discussion of AI that I use as a foundation book for my AI and the Law Class at Michigan State University College of Law.
  2. Ethan Mollick, One Useful Thing (https://www.oneusefulthing.org/), articles and email newsletter from one of the premier writers and thinkers on practical uses of generative AI.
  3. ABA Model Rules of Professional Conduct – The American Bar Association’s guide on ethical rules.
  4. Legal Talk Network – Featuring various podcasts, including The Kennedy-Mighell Report, which discusses the intersection of law and technology, especially the episode “The Current State of Generative AI in Legal” (https://legaltalknetwork.com/podcasts/kennedy-mighell-report/2023/07/the-current-state-of-generative-ai-in-legal/).
  5. Legal Technology Resource Center (LTRC) – An initiative by ABA, offering guidance on leveraging technology in the legal sector.
  6. Dennis Kennedy, “Adding a ‘Group Advisory Layer’ to Your Use of Generative AI Tools Through Structured Prompting: Using Personas for Advisory Boards, Task Forces, Mastermind Groups, and Other Collections of Personas to Assist in Evaluations, Assessments, Recommendations, Decision-making, and much more (Including Law-related Examples)” at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4570860.
  • Dennis Kennedy is the Director of the Michigan State University Center for Law, Technology & Innovation, the author of the book, Successful Innovation Outcomes in Law, and the founder of the Law Department Innovation Library (www.ldilibrary.com). He writes and speaks frequently on legal innovation and technology topics, including AI, focusing on law departments. He has co-hosted The Kennedy-Mighell Report podcast on legal technology with Tom Mighell since 2006.

This post is a version of a handout for a panel presentation I participated in for the Institute of Continuing Legal Education on December 7, 2023.

I have placed this version of the handout under the Creative Commons CC BY 4.0 License. I’m happy for you to use this material with attribution to me.


This work is licensed under CC BY 4.0

[Originally posted on DennisKennedy.Blog (https://www.denniskennedy.com/blog/)]

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I held my Q3 Personal Quarterly Offsite last week. I developed this technique several years ago and have found it incredibly useful.

The premise is that, like a corporate or business retreat or offsite, you can and should set aside a few hours every quarter to focus on your personal goals, developments, and status, and then work on strategies and tactics to move forward.

My recent retreat was a mental and strategic decluttering effort and I left it energized, focused, and with a clear path. That is not how I entered.

This is the most important exercise I do on a regular basis. I want to get it out to more of you so you can try the approach, share your experiences and feedback, and let me know about how it works for you.

I’ve drastically reduced the price of my online Productive Personal Quarterly Offsites for Busy Legal Professionals course down to $39.99 for at least the rest of 2023. Link to course: https://kennedy-idea-propulsion-laboratory.mn.co/plans/342413

Based on my own experiences using and evolving the technique, you will get much more value from the course than that.

Doesn’t your personal strategy and planning deserve at least this level of attention?

Purchase the course here.

[Originally posted on DennisKennedy.Blog (https://www.denniskennedy.com/blog/)]

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I recently presented to a great audience at the State Bar of Michigan Information Technology Law Section’s Annual conference about AI. I described my presentation as an introduction to AI for not-overly-technical lawyers. And it was a lot of fun. Although I’m now concentrating on speaking a private events, it was great to do a presentation for my local community of lawyers in Michigan.

I wanted to share a PDF of my slides from the session and a link to the PDF of my recent white paper on some of my own uses of generative AI tools.

I used the term “promptcrafting” in the session as an alternative to “prompt engineering” and was surprised how positively people reacted to that term. It better reflects how I feel about my approach to prompt, as it seems more art than science. I also am thinking that “conversation AI” is a more apt term than “generative AI.” I’ll probably write more about that in the future.

After the session, I received the following email from the organizer, which made me feel like I had delivered what they wanted for this conference:

“I wanted to extend my heartfelt gratitude for your participation in the Michigan Bar IT Law Section’s Annual Conference. Your presentation on unraveling the story of AI in the legal field was enlightening and a highlight of our event. The way you delved into AI’s history and evolution within the legal landscape provided our attendees with a comprehensive understanding of this transformative technology. Your insights into the key opportunities and challenges associated with AI in law were incredibly valuable. Your presentation added immense value to our conference, and we are truly thankful for your participation.“

[Originally posted on DennisKennedy.Blog (https://www.denniskennedy.com/blog/)]

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I’m starting to publish regular blog posts from my generative AI experiments in what I now refer to as my “AI Lab.” In this post, I offer for your consideration the output generated by ChatGPT after a series of prompts I tried. I’m now calling what I’m doing “promptcrafting,” because, hey, why not? “Prompt engineering” no longer seems like an accurate term to me.

I’ve long been fascinated by scenario planning. This summer, I took a great (and highly recommended) online course from the New School on scenario planning. I’ve been exploring ways that I might get ChatGPT to do the lion’s share of the work on first drafts of scenario plans.

This experiment provides a good illustration of AI-generated scenario planning results. My prompting was focused on creating four likely future scenarios for legal education. The output is interesting enough that I’d consider doing a design thinking or similar event based on the scenarios. I might also use these scenarios as part of a law school’s planning discussions.

See what you think.

Four Scenarios for the Future of Legal Education

Scenario 1: Fully Digitalized Law School

The future of legal education in this scenario has undergone a complete digital transformation. Law schools are no longer bound by the physical confines of buildings and campuses, but instead exist entirely in the digital realm. They function as international centers of learning, with students logging in from all corners of the world. This opens doors to a diverse range of students, including those who may have been unable to attend traditional law schools due to geographical limitations or personal commitments.

With technology facilitating this transformation, the approach to teaching and learning is revolutionized. Artificial intelligence (AI) becomes a significant driver of personalization, with advanced algorithms analyzing each student’s performance, learning style, and pace. This data-driven approach enables a personalized education pathway for each student, helping them to focus on their areas of weakness and capitalize on their strengths.

AI-powered chatbots provide 24/7 assistance to students, answering questions and clarifying doubts. These tools make student support more efficient and readily available, reducing the need for students to wait for office hours or appointments. They provide instant support and guidance, enabling students to make progress in their learning at any time.

Virtual reality (VR) plays a crucial role in providing a hands-on learning experience. Students can participate in virtual court simulations, presenting their cases, arguing motions, and receiving instant feedback. This immersive technology not only enhances learning but also helps students hone their argumentation and presentation skills, preparing them for real-world legal practice.

In terms of networking and socialization, VR facilitates virtual meetups and study groups. Despite being physically distant, students can interact with their peers, collaborate on projects, and engage in discussions, fostering a sense of community. This makes up for the lack of physical interaction and ensures that students do not miss out on the social aspects of law school.

The fully digitalized law school also allows for an expanded pool of instructors. Professors and experts from around the globe can be brought in for guest lectures or seminars, providing students with diverse perspectives and insights into different legal systems and practices. This broadens the learning experience and exposes students to a global understanding of law.

In summary, the fully digitalized law school model democratizes legal education, making it accessible to a much wider audience. It harnesses the power of technology to enhance learning and provide a customized, immersive, and globally oriented education. This scenario prepares students not only for the practice of law but also for the digital future of the legal profession.


Scenario 2: Blended Law School Experience

The blended law school model combines the best of traditional and digital education. This scenario offers a well-balanced approach, where technology enhances learning without overshadowing the fundamental aspects of legal education. The traditional Socratic method of teaching, with its emphasis on critical thinking and in-depth understanding, is maintained for core subjects, delivered in physical classrooms.

However, flexibility is introduced with elective courses offered online. These courses cater to the diverse interests of students, providing them with the opportunity to explore different facets of law from their preferred environment. Whether it’s international law, technology law, or environmental law, students can choose electives that align with their career goals or interests. This hybrid model offers students the best of both worlds: the richness and depth of traditional law education, and the flexibility and convenience of online learning.

Experiential learning forms a core part of this blended model. Law schools collaborate with external organizations, including law firms, NGOs, and government agencies, to provide real-world learning opportunities. Students participate in internships, externships, and pro bono work, applying their classroom knowledge to practical situations. They get a first-hand experience of the legal profession, understanding the complexities and challenges of legal practice.

Real-life case studies are integrated into the curriculum, enabling students to analyze and dissect actual legal cases. They work in teams to devise strategies, argue their cases, and receive feedback from practicing lawyers or professors. This bridges the gap between theory and practice, equipping students with the skills and experience needed for their future careers.

Moreover, this model incorporates a career-focused approach. Professional development workshops, networking events, and mentorship programs are part of the law school experience. Students receive guidance and support to prepare them for their career journey, from job search strategies to interview preparation.

Overall, the blended law school model provides a comprehensive and well-rounded education. It combines foundational legal knowledge, practical experience, and career preparation, ensuring students are well-equipped to navigate the evolving landscape of the legal profession.

Scenario 3: Specialized Legal Education

In the specialized legal education scenario, law schools restructure their programs to cater to the evolving needs of the legal landscape. As the world becomes more complex, with advancements in technology, increasing environmental concerns, and growing human rights issues, the field of law also becomes more specialized. Law schools, in response, offer specialized degrees focusing on specific areas of law, such as technology law, environmental law, or international human rights law.

These programs are designed in collaboration with industry experts and organizations. For instance, a program in technology law might be designed in collaboration with tech companies, addressing legal issues related to data privacy, cybersecurity, or intellectual property. An environmental law program might be designed in partnership with environmental agencies or NGOs, focusing on laws related to climate change, pollution, or biodiversity conservation.

The curriculum of these programs is highly focused and applied. Students delve deep into their chosen area of law, learning from experts in the field. They engage in specialized research, case studies, and projects, gaining a deep understanding of the specific legal issues and challenges in their area of specialization.

Experiential learning forms a crucial part of these specialized programs. Students participate in internships or externships with relevant organizations, gaining first-hand experience of their chosen field. They also work on real-life cases, applying their specialized knowledge to solve complex legal problems.
Upon graduation, these specialized lawyers are highly sought after in the job market. Their deep knowledge and practical experience make them valuable assets for law firms and organizations. They are equipped to navigate the complexities of their chosen field, providing specialized legal services and contributing to the advancement of their area of law.

Overall, the specialized legal education scenario offers a focused, applied, and career-oriented approach to legal education. It aligns with the evolving needs of the legal profession and prepares students for specialized roles, contributing to a more effective and efficient legal system.

Scenario 4: Decentralized Legal Education

In the decentralized legal education scenario, the traditional model of law schools is disrupted by the emergence of alternative education platforms and micro-credentialing. The concept of a law degree is replaced by a more flexible and personalized approach to legal education. Students can choose from an array of legal courses offered by various providers, including universities, law firms, online platforms, and even government agencies.

Law schools, in response to this shift, transform themselves into learning hubs rather than degree providers. They offer individual courses, workshops, seminars, and short-term professional development programs. Students can choose to take a course on contract law from one law school, a course on technology law from an online platform, and a negotiation workshop from a law firm. They can design their own learning pathway, gaining knowledge and skills relevant to their career goals or interests.

Micro-credentials, such as digital badges or certificates, are awarded upon completion of these courses or programs. These credentials validate the student’s skills and knowledge, providing them with a portfolio of qualifications that they can present to employers. This portfolio-based approach offers a more nuanced representation of a student’s capabilities, highlighting their specific skills and areas of expertise.

Moreover, this decentralized model fosters a culture of lifelong learning. As the legal landscape continues to evolve, professionals need to continually upgrade their skills and knowledge. This model provides them with the opportunity to do so, enabling them to keep up with the latest developments and stay relevant in their profession.

Overall, the decentralized legal education scenario disrupts the traditional model of legal education, offering a more flexible, personalized, and future-focused approach. It empowers learners, giving them control over their educational journey and equipping them with the skills and knowledge they need for the evolving legal profession.

[Originally posted on DennisKennedy.Blog (https://www.denniskennedy.com/blog/)]

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Noted legal technologists Dennis Kennedy and Tom Mighell bring listeners an in-depth analysis of the latest advancements in legal technology, in addition to best practices for utilizing existing tools.