Lawyer, hacker, photographer

Introduction

The emergence of generative AI in the legal field presents a transformative opportunity for attorneys to enhance the quality of their work and better serve their clients. While AI has the potential to revolutionize the way lawyers approach their practice, it is important to recognize that it is not merely a search replacement or a tool for creating generic drafts. Instead, AI can be a powerful ally when used thoughtfully and in alignment with our professional obligations.

To effectively harness the power of AI, attorneys should work with these tools in a controlled manner, ensuring that they remain grounded in verifiable case law and other reliable sources. By maintaining control and staying within their zone of competence, lawyers can leverage AI to enhance their work while mitigating potential risks.

As we navigate the integration of AI into legal practice, it is essential to keep our ethical obligations at the forefront. The principles of diligence, candor, and confidentiality that have guided our profession through the dawns of the industrial age, the space age, and the information age will continue to serve as our compass in the AI age. Moreover, in an era of widespread user data collection for AI training, we must be vigilant in prioritizing the security and confidentiality of our clients’ information. This paper will explore strategies for finding secure ways to use AI that protect the integrity of our work and the trust placed in us by our clients.

In the following sections, we will focus on the scope of text-generative AI tools and their applications in legal practice. We will explore three primary use cases: legal research, AI as a copilot, and language models for document review. Each of these applications will be examined in detail, providing practical guidance for attorneys considering integrating AI into their work. Additionally, we will briefly touch on other potential use cases that hold promise for the future of legal practice. Finally, we will dedicate a section to addressing the crucial topic of confidentiality in the context of AI, discussing strategies for ensuring the security of client information while leveraging these powerful tools.

As the legal landscape continues to evolve, attorneys have the opportunity to proactively explore the transformative potential of AI. By doing so, we can enhance the quality of our work, better serve our clients, and position ourselves at the forefront of the legal profession’s technological evolution. I invite you to join me in exploring how AI can empower us to navigate the complexities of modern legal practice while upholding the values that define our profession.

Breaking Down AI

Depending on the context, AI has meant a number of different things for quite some time. For the purposes of this paper, I’ll be focusing specifically on AI in the context of large language models used for working with text. While image generation, video generation, audio generation, and music generation are all worthy topics of discussion, they fall outside the scope of this paper.

As we narrow our focus to text-based AI and its potential applications in the practice of law, a range of questions emerge. How effective are language models at legal research? Can they accurately review documents? And to what extent can they assist in legal writing? While there are no clear answers to these questions yet, two things are crystal clear to me.

First, any dismissal of the effectiveness of large language models for a particular purpose should be viewed with a skeptical and curious eye, as the capabilities of these models are rapidly evolving. Second, while AI has not yet fundamentally changed the way law is practiced, it will do so very soon, whether we like it or not. Yesterday’s pleas for firms to avoid ChatGPT and all AI technology for fear of their model training are being replaced by tentative inquiries about capabilities. These will soon give way to questions, either asked directly or rhetorically by clients, about why a particular task took so long when an AI could produce a similar result in a matter of moments.

Ethan Mollick, author of the one book I truly recommend that every person read about AI, points out a central tenet: any language model you use today will be the least capable one you’ll ever use. While the true power of these models lies under the hood, GPT-4 and its progeny are orders of magnitude more capable than the widely-used GPT-3.5. Context windows, which can be thought of as a language model’s short-term memory or RAM, are now as long as multiple books in Gemini 1.5 and can span full days’ worth of recorded testimony in Claude. Moreover, privacy policies exist to ensure that you can input meaningful information into these models without fear of your data being used for training (though there are some caveats to this, which I’ll discuss later).

The rapid advancements in AI technology are not limited to high-end, resource-intensive models. Apple’s custom silicon can run a top-tier model from Meta, such as Llama 3, locally on a laptop without even spinning up the fan. There are even apps like Private LLM that allow you to run dozens of smaller models directly on your iPhone. While these models may not be capable of replacing human jobs just yet, they illustrate the potential for powerful AI at the low-cost, accessible end of the spectrum.

As we explore the implications of text-based AI for the legal profession, it’s essential to keep in mind the breakneck pace of development in this field. The questions and challenges we face today may be rendered obsolete by tomorrow’s advancements. By maintaining an open and curious mindset, we can position ourselves to harness the power of AI for the benefit of our clients and our practice.

In the following section, we will delve into the ethical considerations surrounding the use of AI in the legal profession, examining how the principles of diligence, candor, and confidentiality must be adapted and applied in this new context. By proactively addressing these considerations, we can ensure that our use of AI aligns with our professional obligations and values, allowing us to harness its power to improve the quality and efficiency of our services while upholding the trust and confidence of those we serve.

The ethical issues presented by generative AI are as numerous and diverse as its potential applications, ranging from the creative industry’s concerns about job displacement to the philosophical quandaries surrounding the technology’s development and goals.

Within the legal profession, the ethical issues boil down to two facets: confidentiality and trustworthiness. Confidentiality emerged as the primary concern due to OpenAI’s initial practices of collecting and using prompts and output for future use. Many of these concerns have been addressed and will be discussed later in this paper.

Trustworthiness is as difficult to define as it is to measure, and benchmarking that trust is something many, including myself, are working hard to accomplish. The legal system’s reliance on the principles of the rules of evidence to establish trust and set the bar for admissibility of evidence provides a solid foundation for judging whether an application of AI should be brought into your work.

As we examine how generative AI can be applied within legal practice, consider it through a similar framework. Ask whether the application of generative AI is relevant and helpful to what you’re doing, whether the output is reliable, and whether there’s a risk of harm caused by systemic bias in a given language model.

By keeping these principles in mind, we can effectively align the use of AI with the core values of our profession, ensuring that we harness its potential while upholding our ethical responsibilities to our clients and the legal system as a whole.

When evaluating the relevance of an AI application to your work, it’s important to consider not only the potential benefits but also the costs and risks involved. This assessment requires a balance of business judgment and a clear understanding of the capabilities and limitations of the AI system in question.

In the following section, we will explore the factors to consider when determining the relevance of an AI application to your legal practice. We’ll examine how to weigh the potential efficiency gains against the investment of time and resources required to integrate the technology effectively. Additionally, we’ll discuss the importance of assessing whether the AI system aligns with your specific practice area and client needs.

By carefully evaluating the relevance of an AI application, you can make informed decisions about whether and how to incorporate this technology into your work, ensuring that it adds value without compromising the quality of your legal services.

Relevance of the Technology to the Practice

When it comes to integrating AI into our legal practice, it’s important to approach it with a balanced perspective. While AI tools have the potential to revolutionize the way we work, offering increased efficiency and accuracy, we need to be mindful of the challenges and limitations that come with adopting these technologies.

As legal professionals, it’s our responsibility to carefully evaluate the relevance of AI applications to our specific practice areas. This means taking the time to understand the underlying technology, such as the frontier models being used (e.g., GPT, Claude, Gemini, or Llama), and assessing how well they align with our needs and goals. By familiarizing ourselves with the capabilities and limitations of these models, we can make informed decisions about which tools are worth investing in and how to best leverage them in our work.

It’s also crucial to recognize that the more we rely on AI systems, the more diligent we need to be in monitoring their performance and ensuring that appropriate safeguards are in place. This may involve regularly auditing the outputs generated by these tools, establishing clear protocols for their use, and being prepared to adjust our approach as needed.

As the AI landscape continues to evolve, we can expect to see variability in pricing models for these technologies. While cost is certainly a factor to consider, it’s important not to let it be the sole driving force behind our adoption decisions. Instead, we should take a holistic view, weighing the potential long-term value an AI tool can bring to our practice against the risks and the resources required to effectively integrate it into our workflows.

Ultimately, the most successful implementations of AI in legal practice will be those that emerge from a culture of experimentation, collaboration, and continuous learning. By fostering an environment that encourages exploration and idea-sharing, we can position ourselves to identify innovative applications of these technologies and stay at the forefront of the industry.

As we navigate this new frontier, however, we must remain grounded in the core values and principles that define our profession. Our focus should always be on leveraging AI in a way that enhances our ability to deliver high-quality legal services to our clients, rather than getting sidetracked by the hype or the business aspects surrounding these tools.

By approaching the integration of AI with a critical eye, a commitment to understanding the technology, and a willingness to adapt and innovate, we can harness the power of these tools to drive our practice forward – while always maintaining our dedication to the highest standards of legal excellence.

Reliability of the Output

The reliability of AI output is a critical consideration when evaluating its use in legal practice. While AI tools have demonstrated impressive capabilities in various tasks, from legal research to document review, their output’s trustworthiness is not always guaranteed.

Advanced language models and machine learning algorithms can achieve high levels of accuracy and consistency, augmenting human expertise by identifying relevant information and catching errors. However, no AI system is infallible, and the reliability of their output can vary based on factors such as data quality, task specificity, and algorithmic limitations. Even the most advanced AI models can produce errors, biases, or inconsistencies that require human oversight.

Relying on inaccurate or misleading information can have serious consequences in any context, but especially within the practice of law. The use of AI raises important questions about liability, accountability, and ethical obligations to clients.

Therefore, when evaluating the reliability of AI output for legal applications, a cautious, case-by-case approach is essential. This involves carefully assessing the specific tool and use case, understanding its limitations and potential failure modes, and establishing clear protocols for human review and oversight.

In the following sections, we’ll explore three specific applications of generative AI in legal practice: legal research, document review, and using AI as a working partner.

By presenting a balanced assessment of these applications and their current reliability, we aim to provide a framework for legal professionals to make informed decisions about incorporating AI into their work while upholding the highest ethical and professional standards.

The recent study from Stanford University’s Center for Human-Centered Artificial Intelligence (HAI) has sparked a lively debate within the legal community about the current state and potential future of AI-powered legal research tools. The study, which evaluated the performance of generative AI legal research tools from LexisNexis and Thomson Reuters, has been met with both criticism and commentary from the developers and the broader legal industry.

Critics of the study, now commonly referred to as “The Stanford Paper,” have questioned the fairness of setting such a high bar for success and whether the input prompts used in the research were representative of how paying customers, who receive training on these tools, would actually use them. However, setting aside these potential criticisms, the technical challenges of applying language models to legal research are far more complex than those unfamiliar with the intricacies of law libraries might appreciate.

My own experiences with systems similar to those used in large-scale legal research or document management systems (DMS) and knowledge management applications, known as Retrieval-Augmented Generation (RAG) systems, have been both promising and challenging. The basic concept is straightforward: when presented with a question, the system searches a specialized index, feeds non-reversible numbers into a language model, and provides an informed response. However, the success of this approach heavily relies on the quality of the search results, which must be sufficiently relevant and informative to generate a reliable answer. This means that even when attempting to move beyond traditional search methods by leveraging AI, the search component remains a critical factor and a potential point of failure.

Developing an effective RAG system involves more than just working directly with a language model; it requires granular control and tailored strategies for different applications. For instance, if the goal is to extract broad themes, the index should be based on larger chunks of text, while pinpoint precision demands smaller chunks. The success of these systems, as I’ve observed in my work with personal RAGs, is highly dependent on the performance of the underlying search engine. As most users have experienced, both traditional keyword search and legacy machine learning-based concept searching have their limitations. While I don’t claim to have more expertise than Lexis or West, it seems prudent to promote these tools cautiously and mindfully, rather than making overly ambitious claims, however precise the wording may be.

Despite the challenges highlighted by the Stanford study, the state of AI in legal research, one of the few areas with published public research, remains fluid and rapidly evolving. The technologies underpinning these products are advancing by the day, and I’m confident that commercial vendors are diligently working to enhance the effectiveness of their systems, regardless of the quality of the prompts used, and to develop tools and training programs that help users leverage language model-based research systems more effectively.

For most legal professionals, the decision to adopt AI-powered legal research tools will ultimately come down to a question of time and attention. While AI-enabled legal research may not currently be more efficient than traditional methods, this is unlikely to remain the case indefinitely. Although the present limitations of AI in legal research should not be seen as an indictment of AI’s overall potential in the legal field, they serve as a valuable reminder of the challenges that must be addressed as this technology continues to mature.

As the debate surrounding the Stanford Paper demonstrates, the reliability and effectiveness of AI-powered legal research tools will likely remain a topic of scrutiny and discussion within the legal community for some time. However, by approaching these tools with a critical eye, a commitment to ongoing evaluation, and a willingness to adapt as the technology evolves, legal professionals can position themselves to harness the potential benefits of AI while mitigating the risks and limitations.

GenAI as a Co-Pilot

The typical demonstration of using AI for writing of any kind is fairly typical, a person makes a generic request like “plan a party for me” or “write an email.” This comes accompanied with both the suggestion and the warning that AI is only a first draft, implying that writing with AI is a transactional endeavor much like a Google search. You can certainly do that, but you’ll likely come away unimpressed with the output. Frankly, this is not the way to use any GenAI tool and expect meaningful output.

In the world of artificial intelligence, context is king. In AI you’ll hear about context in two…well…contexts. First is in talking about the words and data that you feed to the model. The second is a more technical term referred to as the context window. Think of the context window as the amount of stuff that the model can process of yours at any given time. The longer the context window, the more stuff you can give to a language model. When ChatGPT 3.5 was released it had a context window of around 4,000 tokens or (roughly) words. That’s not a lot. Modern models have introduced substantially larger context windows. Claude 3.0 has a context window that will hold around 500 pages of text and Gemeni 1.5 around 1500 pages. That is a lot.

So what’s a person do with such a large context window? Two things have emerged for me through my research. The first being document analysis or synthesis in support of writing or merely thinking issues through. The second is having conversations which become more valuable over time and thereby creating tiny snowballs of efficiency and effectiveness. Lets start with document analysis and writing.

Given the frankly privacy hostile environment around AI in its earlier months, I was pretty skeptical of giving anything to any model (more on this below) so when Claude said it could work with documents the first documents I worked with were Claudes privacy policies, terms of service, etc. I started by telling the thread what I wanted to do, asking if it had follow up questions about what my goals were, told it what I was going to upload, then uploaded the data. At that point I was ready to dig in.

What I saw was really great. These kinds of documents are hard for anyone to read, but the model chewed right through it. I asked for pinpoint cites, chased them down and when working with files you’ve fed to the context window I found it at this scale to be free of inaccuracies in the output. Not only was the summary good, Claude was really effective in talking through deciding what my current comfort levels could be given their privacy policies.

From there I was off to the races, seeing how much and what kinds of data I could put in and what the language model could tell me with this fairly extensive, but narrow focus of conversation. From the experiments continued and expanded. Among other things I accomplished were:

  • Instead of calling vendor support, I fed their documentation on a feature that I’d not used, but had been tasked to troubleshoot. Not only did it get me the correct answer, it walked me though the user interface more effectively than any chat based support agent I’ve used. Ran the problem and solution by the vendor by the time they responded to the ticket and they were fairly impressed with it’s diagnosis and the solution.
  • I fed it a six hours of deposition testimony of a former President and asked it questions, had it extract tables of participants, it identified attorneys in the room and who they were representing, and then it generated a fairly sophisticated table that frankly could work as an excellent demonstrative exhibit on its own. All of this just came from just the transcript testimony. I’ve spot checked the numbers and they generally match up. In a real world situation you’d know the facts fairly well already and would be able to spot inaccuracies and correct them as you go.
  • I took an example presentation from a quirky slide generating software called iA Presenter and fed it to a thread. From then on I used this thread to do things like generate a slide deck targeted towards TikTok based on the full text of FDR’s address to congress following the invasion of Pearl Harbor. Frankly it’s amazing, and a tremendous example of how a language model can detect things like sentiment, and perhaps even some incredibly dark humor once Claude realizes it’s in on a bit depending on how much you read into it’s choices in emoji.

Confidentiality

ChatGPT was released to the world shrouded in mystery, garnering amazement from early testers, and interest across the world. It was also released to the world in a form that had almost an enthusiasm for the collection of user data that quickly led privacy advocates and those concerned with the confidentiality of the data of which they’ve been entrusted to have serious and legitimate concerns over what or even if sensitive information should be put into a large language model. Companies and law firms reacted accordingly with stern statements about what should be kept out if not outright bans on the use of the technology inside some organizations or from clients via outside counsel guidelines. This begs the question, what exactly is different about generative AI that makes it more risky from a data privacy and security perspective?

Rule 1.6, titled “Confidentiality of Information,” states that a lawyer shall not reveal information relating to the representation of a client unless the client gives informed consent, the disclosure is impliedly authorized in order to carry out the representation, or the disclosure is permitted by paragraph (b). Paragraph (b) outlines several scenarios where a lawyer may reveal information, such as to prevent reasonably certain death or substantial bodily harm, to prevent the client from committing a crime or fraud, or to comply with other law or a court order.

Rule 1.82, titled “Duties to Former Clients,” states that a lawyer shall not reveal information protected by Rule 1.6 when the lawyer changes employment or when there is a change in the composition or ownership of a firm, but only if the revealed information would not compromise the attorney-client privilege or otherwise prejudice the client. This rule aims to protect the confidentiality of information even when a lawyer changes firms or employment status.

Risk of Input/Output Exposure Through Model Training

The difference between the level of concern surrounding Generative AI tools and other cloud based or non-cloud based technologies is that unlike software run by businesses who have little incentive to do much with your data other than mine it for marketing insights, right now companies building and tuning Generative AI models are fairly interested in the substance of your inputs and outputs. These outputs can be used in two ways, human review of prompt/response pairs so the model’s performance can be improved or by the inputs and outputs themselves being used for pre-training of a future frontier model. Neither of these sound great on their face, but lets dig a little deeper and consider the reality of the privacy landscape in mid-2024 instead of early 2023.

Human Review of Inputs and Outputs

As of now, most tools I’ve reviewed and tested state that they will not take your prompts and make them available to a person for review unless that prompt or response triggers one of the content guardrails. This is the case for public ChatGPT and for Claude.ai. This should be a red flag for those who are considering using these technologies to help in the aid of review and work on matters involving things of a sexual manner or violence. In my experience, both models are sensitive about engaging on these topics making them ineffective under certain circumstances.

This risk goes away partially if a person uses a model like Claude or GPT-4 through the API, or via a third party. If using a third party, triggering the content guardrail does not make it available for review if using Claude but may by OpenAI unless the software provider has taken an extra step with Microsoft and OpenAI to gain this exception.

Other smaller models, or those build based on the foundation of open source models, then trained and tuned on future use may still be interested in taking your data and reviewing it so reviewing the privacy policies to understand this is critical whether you’re using these services for client data or not.

Use of Inputs and Outputs being Used in Future Models

The visceral concern here is focused on the nightmare scenario where someday a person puts in a magic code and out pops some kind of identifiable or useful information to them that you’d put into a language model today. Again, a concern that’s not unjustified however it is likely a tad overstated. In terms of model training, model developers have exhausted most sources to train on that have come from humans, instead beginning to train on synthetic data, or data created by one LLM for use in another. Presuming that trend holds, as does the trend towards giving users at least the option of opting out of ‘model improvement’ as it’s often called, this concern should continue to diminish. Also, given the amount of data that goes into the pre-training of the newer and more effective models, the odds that someone draws your string of characters is likely extremely low.

Risk of Data Exposure via Data Breach

Once we get beyond the new aspects of companies perhaps being incentivized to log and use all of your activity or at least review it for model improvement, we turn to the overall security of the platform. On one hand, it’s easy to dismiss this analysis as being no big deal and the same as you would do for any other platform where you’ll be communicating with or storing client information, though that process can be extremely rigorous depending on your circumstances.

Even if you just use a Generative AI platform for personal use, and never utter the name of a client to it, if you’re going to get the most from the tools you’re going to want to give it full context meaning you’re likely to have more candid and confidential words stored there than your own email of even your text messages. Given this, be sure to always enable two-factor authentication and if that’s not an option consider a different platform. Many startups which are coming along to disrupt the existing business may be doing so on limited budgets where security investment receives lower priorities than research or simply covering their operating expenses and API bills from the model providers.

As you would expect, the major players like Claude and ChatGPT have some robust security controls, certifications, and real time reporting to back it up. Smaller startups perhaps not as much.

Controlling Confidentiality When Using Generative AI

With all of this in mind, how do we approach the analysis of any given platform? Personally I’ve fallen into the following routine:

  • Download and review using two different language models the privacy policies, terms of service, and any other documentation related to data handling, security and privacy for a company
    • I like to use two models just to make sure that one doesn’t have an inherent bias towards being overly proud of itself
  • Check for key points related to model improvement or training, and if there’s no opt out or a clear statement that my data is mine and mine alone I pass on the platform. An opt-out is totally acceptable in my book, and shows that a company can ask for their users to contribute but not require them to do so.
  • Examine the overall security posture of the company, look for positive signs like marketing towards enterprise level customers, compliance certifications, etc. Always understand that compliance is not security, but companies that spend money on compliance certifications are also more likely to be staffing up with security operations folks too.

Local Chat Storage and Model Hosting Options

Lots of folks just aren’t comfortable yet using Generative AI in the cloud due to privacy and confidentiality concerns and those folks aren’t left completely in the cold either. There are beginning to be tools which will help you run language models locally or at least keep your chat threads locally, sending data to models via their API and back securely.

Fair warning, both these options fall on the right side of the ‘nerd’ column and would not be for everyone, nor would I recommend that this be your first foray into learning how to use Generative AI.

Running Models Locally

Despite what you hear about all of the resources required for generative AI, once the model is trained and finished up, the models themselves are fairly small (smaller than a 4k movie you download from the internet) and can run pretty well on your local computer if your local computer is modern and fast.

As of right now (May 6th, 2024) the easiest way to run a model locally is to do it on an Apple Silicon based Mac using a piece of software called LMStudio. LMStudio lets you download from a repository of available open source models so you can find one that works well on your machine and well for the tasks you want to complete with it.

This is also where you will find “Uncensored” versions of different open source models. As discussed previously, an uncensored model may be the most effective way to work with certain materials given the guardrails in place in cloud based deployments, however in my early experimentation the uncensored models are perhaps less trustworthy and because you may or may not know the motivations driving the person who put this onto the repository, it’s far from an exercise I recommend for anyone who hasn’t been working with AI extensively for some time.

Local Chat Storage - Communication to Cloud Models via their API

The final, and perhaps my favorite option right now, is running software locally which can house and manage all of your conversation threads with multiple language models while storing all of this data locally. Again, I find myself endorsing a piece of MacOS exclusive software called Raycast. Raycast’s privacy policies are short, clear, and your chat threads are stored locally in an encrypted database. This puts these chats at a relativity low risk of disclosure as long as your computer falls into the hands of someone who can break FileVault or knows your credentials.

The implementation is simple, straight forward, and likely sustainable on the part of the company that makes Raycast since they will not be paying huge storage bills for text on behalf of all of their customers.

Privacy Policies in Context

This table, based on the privacy policies as published as of the first week of May, 2024 outlines different AI services and tools I’ve evaluated and where they fall relative to each other in terms of data privacy. As you can see, the trend is towards greater user privacy. OpenAI changed their policy to allow opt-out of training without a severe penalty in functionality during the first week of May.

Service Type of Service Use of Prompts/Outputs for Human Reinforcement Use of Prompts/Outputs for Pre-Training Models User Control Over AI Data Use AI Data Privacy Concerns
OpenAI Primary access method Yes - for policy violations No (opt-out available for ChatGPT conversations) High (opt-out available) Low
Anthropic Primary access method Yes - for policy violations No High (no explicit opt-out, but no training mentioned) Low to Moderate
Perplexity Search engine and primary access method No No (assuming opt-out) High (assuming opt-out) Low
You.com Search engine with writing mode No No (assuming opt-out) High (assuming opt-out) Low
Poe LLM reseller Yes (shared with third-party AI providers and developers) Unclear Low (no opt-out mentioned) High

Conclusion and Outlook

Throughout this paper, we have explored the transformative potential of generative AI in the legal profession, focusing on its applications in legal research, document review, and as a collaborative tool. We have also examined the ethical considerations surrounding the use of AI, particularly in terms of confidentiality and the reliability of AI-generated output.

As we have seen, the rapid advancements in AI technology present both opportunities and challenges for legal professionals. While AI has the potential to revolutionize the way we approach legal work, enhancing efficiency and accuracy, it is crucial that we remain vigilant in aligning its use with the core values and ethical principles that define our profession.

The integration of AI into legal practice requires a thoughtful, balanced approach that takes into account the relevance of the technology to specific use cases, the reliability of the output generated, and the measures necessary to safeguard client confidentiality. By carefully evaluating these factors and establishing clear protocols for the use of AI tools, legal professionals can harness the power of this technology while mitigating potential risks.

As the AI landscape continues to evolve at a breakneck pace, it is essential that we remain open to experimentation and collaboration, fostering a culture of continuous learning and adaptation. By staying informed about the latest developments in AI technology and engaging in ongoing dialogue with our peers, we can position ourselves to identify innovative applications and best practices for integrating AI into our work.

However, even as we embrace the transformative potential of AI, we must never lose sight of the fundamental principles that underpin the legal profession. Our commitment to diligence, candor, and confidentiality must remain unwavering, serving as the foundation upon which we build our approach to AI adoption.

In conclusion, the emergence of generative AI presents both challenges and opportunities for the legal profession. By approaching this technology with a critical eye, a commitment to ethical principles, and a willingness to adapt and innovate, we can harness its power to enhance the quality and efficiency of our work, ultimately better serving our clients and the administration of justice. The future of AI in legal practice is bright, and it is up to us to shape it in a manner that aligns with the highest standards of our profession.