top of page
  • twitterX_edited
  • LinkedIn

Embracing Human-Tech Interoperability

As digital tools, including generative AI, become integral to our daily lives, legal professionals must adapt to leverage these advancements effectively. Tasks traditionally handled by associates, paralegals, and other legal professionals are increasingly being delegated to efficient, tireless digital assistants and AI tools. For instance, consider a law firm where AI-powered tools automatically sort through discovery documents, identifying relevant information faster than any human could. This shift enhances productivity and efficiency without significantly increasing costs, freeing up human professionals to focus on more strategic, value-added tasks. Fostering a culture of “human-tech interoperability” is crucial. This involves prioritizing user experience and human-centric design for digital tools to ensure seamless integration with existing legal workflows and databases. Additionally, promoting effective collaboration between lawyers, non-lawyers, and digital tools is vital for breaking down organizational silos and communication barriers. Viewing technology as “teammates” can help overcome adoption and adaptation barriers. Legal professionals should be encouraged to perceive AI-powered tools as valuable allies that enhance their capabilities rather than as replacements or threats.   Optimizing any labor-based service business involves adjusting the labor mix, processes, or tools. Many legal practices focus on growth through size and leverage, often neglecting tooling improvements. The contemporary method for adjusting the labor mix, termed “rightsizing,” involves smartly balancing capacity and demand. However, rightsizing typically favors high-fee earners and marginalizes support staff and allied professionals, leading to potential brain drain and business impact. For example, a firm that heavily invests in its partners but neglects the development of its paralegals may face operational inefficiencies and lower morale.   Rather than rightsizing, legal practices should focus on equipping the right people with the right skills for the right tasks at the right time. This strategy includes upskilling or reskilling talent while transforming some roles into digital services, thereby creating scale and more opportunities for talent. For instance, a paralegal trained in using AI tools for document review can handle significantly more work than before, enhancing their role and career prospects. Effective delegation skills are crucial for leveraging technology. If teammates struggle to delegate tasks to one another, they will find it even harder to delegate to technology. Viewing task and service automation technologies as teammates can help overcome adoption and adaptation barriers.   Incorporating digital services into legal workflows offers specific use cases where technology can enhance processes. For example, these technologies can handle routine tasks such as scheduling, document retrieval, and initial contract drafting, allowing legal professionals to concentrate on more complex and strategic work. Additionally, AI-powered tools can expedite legal research by quickly analyzing vast amounts of legal texts to find relevant information, significantly speeding up the research process. Imagine a scenario where an associate uses an AI tool to conduct comprehensive case law research in minutes rather than hours, providing more time for client strategy sessions.   Implementing a digitally integrated workspace in legal practices requires practical strategies. Begin with a thorough assessment of current workflows to identify where digital services can add value. Invest in training programs to ensure all team members are proficient in using new technologies. Develop a phased implementation plan to gradually integrate digital tools, minimizing disruption to existing processes. Continuously review and adjust the strategy based on feedback and performance metrics to optimize the integration process. For example, a law firm could start by integrating AI tools into its research department and gradually extend their use to contract management and client interactions.   However, potential challenges to adoption extend beyond mindset shifts. Ensuring data security and privacy is paramount, as legal practices handle sensitive information. The costs associated with new technologies can also be significant, necessitating careful budget planning. Additionally, resistance from employees wary of changes to their workflow must be managed through clear communication about the benefits and ongoing support available. It’s also crucial to address the varying levels of tech proficiency among staff, providing targeted training to bridge knowledge gaps and ensure everyone can use new tools effectively. Furthermore, maintaining compliance with legal standards and regulations when integrating technology requires meticulous planning and oversight to avoid potential legal pitfalls.   Adopting advanced technology in legal practices often encounters human skepticism or resistance. Many seasoned attorneys and staff fear that AI and digital tools might replace their roles, leading to job insecurity. For instance, in a mid-sized law firm, senior partners may resist using AI for contract analysis, doubting its accuracy and fearing the loss of their traditional expertise. Similarly, in legal departments within corporations, employees might be wary of automated document review systems, concerned about their reliability and the potential for errors. To address these concerns, leaders must foster a culture of openness and continuous learning. They should highlight successful case studies where technology has augmented human capabilities, such as an AI tool streamlining due diligence processes and enabling lawyers to focus on complex negotiations. By directly addressing these fears and showcasing the benefits, firms and departments can build trust and encourage the adoption of new technologies.   Experimentation and iterative learning are essential for successfully integrating technology into legal workflows. Both law firms and legal departments should pilot new digital tools in controlled environments before full-scale implementation. For example, a law firm might initially use AI-powered research tools in a small team to refine the tool’s effectiveness and address any issues. Similarly, a corporate legal department could test an automated contract management system on a limited number of contracts to gather feedback and improve the system. This approach allows legal professionals to learn from hands-on experience, make necessary adjustments, and gradually scale up the use of technology. Encouraging a mindset of experimentation helps create a dynamic environment where continuous improvement is valued, reducing the fear of failure and fostering innovation.   Viewing technology as a partner, not a competitor, is crucial for successful integration in legal practices. AI and digital tools should be seen as allies that enhance human capabilities rather than threats to job security. For instance, in a large law firm, AI can handle routine tasks like document sorting and initial case assessments, freeing up lawyers to engage in strategic thinking and client interactions. In a corporate legal department, digital tools can manage compliance tracking and reporting, allowing legal professionals to focus on high-stakes decision-making and advising the business. By emphasizing the collaborative potential of technology, leaders can change the narrative from one of replacement to one of augmentation. This partnership mindset ensures that both human and technological resources are used to their fullest potential, leading to greater efficiency and innovation in legal practices.   Legal professionals interested in embracing digital tools and transforming their practices can take the following steps:                     1.             Conduct a thorough assessment of current workflows to identify areas where digital tools can add value.                   2.             Develop a phased implementation plan  to gradually integrate digital tools, minimizing disruption to existing processes.                   3.             Invest in training programs to ensure all team members are proficient in using new technologies.                   4.             Foster a culture of continuous learning and adaptation  by regularly sharing success stories and practical examples of technology integration.   The legal profession stands on the brink of a transformative era driven by technological advancements. Embracing these changes requires a strategic and thoughtful approach to integrating digital tools and fostering human-tech interoperability. By prioritizing user experience, enhancing collaboration, and viewing technology as an ally, legal practices can unlock unprecedented levels of efficiency, productivity, and client satisfaction.   Now is the time to act. Begin by assessing your current workflows, investing in the right tools and training, and fostering a culture that embraces continuous learning and innovation. The future of the legal profession is bright for those who are willing to adapt and evolve. Lead the charge in transforming your practice, and become a pioneer in the modern legal landscape. Your journey towards a more efficient and effective legal practice starts today.

How is AI augmenting more traditional automation technologies?

Background Generative Artificial intelligence has had a transformational impact on how quickly and effectively intelligent automation technology is deployed. What was once considered a potential replacement of automation technology has quickly become an enhancement to it. This article dives deeply into how this is unfolding in 2024. What is the point of AI? The purpose of artificial intelligence technology is to support people. This is what it is designed to do. Humans have things that they need to do and other things they like to do. The point is to use technologies like AI for what people do not want to do, yet need to do. The majority of individuals (working jobs that require a computer) tend to have a specialty that they must use their expertise for. Their tasks are typically divided into high-IQ tasks & low-IQ tasks. Leveraging AI & automation enables them to maximize their time on high-IQ activities, rather than the drudgery that can be taken over with technology. Now think about this replicated at scale. In a nutshell, this is the approach enterprises are taking on bringing in generative AI & large language models (LLMs) into their organizations. What are enterprises specifically using generative AI for? Now, there are some misconceptions of what generative AI can do vs. what it should do in 2024. These are two very different things. Initially, generative AI was scrutinized heavily on data security & potential for hallucination. However, this has slowly fallen by the wayside as enterprises have found clear ways to leverage LLMs, while ensuring their data is secure & setting up guard-rails + selecting specific use cases that eliminate most of the material potential for hallucination. For example, although enterprises can use generative AI to take in a host of information and make an underwriting decision on granting a loan to a particular individual, it should not be used for this as a person should be closely involved with any major decisions with implications of this magnitude. Enterprises should use generative AI to take over tasks that are not worth the time of a person, that take very little brainpower and are repetitious. An alternative here is to leverage generative AI to take in that host of information for that loan, organize it in a structured format, input the data into relevant systems and spit out necessary reports for a human to review and then make an underwriting decision on that loan. This is the same process yet reduces the risk of leaving the decision-making power in the hands of technology, while removing the drudgery around complex loan processing. How is this impacting established, well-known automation technologies? The two most well-known technologies across the intelligent automation space are robotic process automation (RPA) & intelligent document processing (IDP). RPA - In terms of enterprise process automation, RPA is being used in tandem with generative AI to fulfill complex needs. RPA cannot think nor execute tasks that have even the least bit of subjectivity. Yet, generative AI can take on the subjective portions of processes. For end-to-end enterprise use cases, generative AI adds very limited value by itself. However, when paired with RPA software bots that can take over data entry & data reconciliation tasks with 100% accuracy, it can add immense value. IDP - On the other hand - IDP is being impacted in that, historically, machine learning needed to be utilized within IDP engines to train semi-structured & unstructured document types like invoices, insurance claims, legal complaints, bank statements, patient prescription documents, etc. However, this was a very limited approach as one enterprise may have 300+ difference invoice types, which then requires 300+ different machine learning templates to create. This is cost-prohibitive, time-consuming and inefficient, along with being limited in accuracy of data when extracting via traditional machine learning. Instead, enterprises can now replace the traditional training via machine learning with LLMs that can take in unstructured & semi-structured documents without being trained. Pairing IDP engines with LLMs enable these engines to understand the context of data within a document immediately. With a small bit of configuration, fine-tuning and prompt-engineering, enterprises can achieve 95%+ accuracy on unstructured & even hand-written documents (assuming they're legible). This is a revolutionary change for intelligent document processing and optical character recognition (OCR) technology. It is akin to what's happening in current day with self-driving cars. In recent news, Tesla deprecated most of the original code and replaced it with AI-based technology for its self-driving features. RPA, IDP & LLMs - Enough acronyms for you? It may sound complex, but much more straightforward than it seems. This is because RPA & IDP have been used together in tandem for years. The only difference here is that now it's more like AI-based IDP is used in tandem with RPA. Nothing has changed on the RPA side, which is simply moving data around once it's been extracted. On the IDP side, it still takes care of the pre-processing & digitizing of documents, while LLMs are layered on top of IDP to bolster the accuracy and speed of data extracted from unstructured documents. This means implementation times are shorter and even business users can quickly and efficiently configure complex documents for high accuracy extraction of data. Previously, enterprises would entrust humans with the task of sifting through large sets of unstructured data. The issue is that the speed is limited to how quickly a human can read and understand this data so technology is ideal for this. Thankfully, generative AI is very good at understanding large sets of unstructured data. For example, in the legal space, LLMs are being used to summarize endless pages of legal contracts & documentation in seconds. Once data is summarized/extracted with LLM-powered IDP, RPA can be used to emulate any human actions needed on a computer screen. This includes inputting that data into a specific website, excel, word document, PowerPoint or system of record. This also includes using RPA to reconcile data from one location to another. For example, ensuring that the data on an invoice document is the same as the data that's been entered into an accounting system of record. In the case of making sense of a large amount of unstructured data… the impact that this will have on the legal, healthcare, insurance and financial services industries is high and still difficult to fully measure. Future of Automation The future of intelligent automation & generative AI is leveraging technologies like large action models (LAMs). A large action model is a model that takes actions based on the specific prompts it receives. You still must train it like RPA, but the training is much faster, yet it's output is more volatile/currently dangerous. It uses a bit of subjectivity & inference to conduct actions, rather than doing this in a purely linear fashion like RPA. This is the future of what can make waves in the intelligent automation space once this technology is able to be deployed in a manner with less risk. Gabriel Skelton is the Head of Artificial Intelligence Solutions at OpenBots. Gabriel assists firms in the selection & implementation of document processing automations involving unstructured & handwritten documents. Gabriel has a team of automation consultants throughout the healthcare, insurance and financial services industries that specialize in automations that involve the extraction of data from the most complex documents and porting that unstructured data directly into end system of records. Gabriel has a Master’s Degree in Entrepreneurial Leadership from Babson College’s Olin Graduate School of Business. Gabriel resides in Coral Springs, Florida with his wife and three children.

The Law Library of Babel: Exploring the Infinite Dimensions of Law and Technology

On a Saturday morning in 2016, I found myself sitting in a brightly lit auditorium in my role as an advisor for the Campbell Law School Law Review unaware that my life would take an unexpected turn. As a representative from Lex Machina, a legal analytics company, took the stage, I had no idea that the journey I was about to embark upon would lead me to the very frontiers of human knowledge and understanding. The platform itself was a marvel, a testament to the incredible power of artificial intelligence and machine learning to transform the way we approach the law. As I watched it process and analyze vast troves of legal documents with breathtaking speed and accuracy, identifying hidden patterns and insights that would have taken teams of human lawyers weeks or months to uncover, I felt a sense of exhilaration and wonder that I had never before experienced in my legal career.             I could imagine what would come—what did come—with predictive analysis platforms like Lex Machina that could equip attorneys with data-driven insights extracted from historical legal records, facilitating informed decision-making throughout various stages of litigation. They could gain strategic advantages by analyzing historical legal data,  helping them anticipate opposing counsel tactics, assess the likelihood of case outcomes, and refine their arguments for specific judges. But even in that moment of technological triumph, I could sense that there was something deeper at work, a fundamental shift in the very fabric of our legal universe. The old certainties and hierarchies that had governed the practice of law for centuries were beginning to crumble, giving way to a new reality shaped by the relentless flow of data and the ever-accelerating pace of change. It was as if a veil had been lifted, revealing a hidden dimension of the law that had always been there, but that we had lacked the tools and the imagination to perceive. As I delved into the labyrinthine world of legal technology, I couldn’t shake the feeling that I was grappling with something much larger than just a set of tools and techniques. It was as if I had stumbled upon a hidden chamber in the vast palace of human knowledge, a place where the very foundations of logic and reason seemed to shimmer and shift before my eyes. It seemed like Borges’s infinite library, containing all possible books, arranged in a vast and complex architecture that is at once wondrous and maddening. Looking for a foundation on which to build my understanding, I sought out the works of the great thinkers of the early twentieth century, the titans of mathematics and philosophy who had first begun to question the bedrock assumptions of their disciplines. Figures like David Hilbert, Alfred North Whitehead, Bertrand Russell, Gottlieb Frege, and Ludwig Wittgenstein had set out to build a grand edifice of logic and meaning, only to find that the ground beneath their feet was far less solid than they had imagined. In their quest for certainty and rigor, these thinkers had stumbled upon paradoxes and contradictions that threatened to undermine the very foundations of mathematics itself. The discovery of set-theoretic paradoxes, like Russell’s paradox of the set of all sets that do not contain themselves, had sent shockwaves through the intellectual world, casting doubt on the consistency and completeness of even the most basic mathematical systems. And, Kurt Gödel’s incompleteness theorems that demonstrate that any sufficiently complex formal system of mathematics will contain statements that are true but cannot be proven within that system. This fundamentally altered their understanding of mathematics, indicating that there are inherent limits to what can be definitively proven. Even as these thinkers grappled with the implications of their discoveries, they found themselves drawn deeper into a world of mystery and wonder. For the more they sought to pin down the nature of logic and meaning, the more elusive and paradoxical it seemed to become. It was as if they had stumbled upon a magic lamp, only to find that the genie inside was more powerful and unpredictable than they could ever have imagined. Mathematics would provide no foundation. Yet it  still was magical. It was the field of pioneers like Claude Shannon and Alan Turing, two towering figures whose work revolutionized our understanding of communication, computation, and the very nature of information itself. Shannon’s groundbreaking work on information theory introduced the concept of entropy as a measure of uncertainty in a message, drawing a powerful analogy between the flow of information and the laws of thermodynamics. It was a connection that had profound implications not just for communication engineering, but for our understanding of the fundamental principles that govern the universe itself. Perhaps empirical sciences, especially physics would provide the foundations to clarify the mysteries that plagued me. But, what do these mathematical formula represent? I grappled with these ideas, eventually finding myself drawn to the work of Norbert Wiener, the brilliant mathematician and philosopher who had coined the term “cybernetics” to describe the study of the structures of communication and control in machines and living organisms. Wiener’s vision of a world in which feedback loops and information processing played a central role in the functioning of all systems, from the biological to the social, influenced my understanding of the interconnectedness of knowledge and the importance of interdisciplinary thinking. It was a vision that also influenced Claude Lévi-Strauss, the French anthropologist whose structuralist approach to the study of culture and society revolutionized the field of anthropology. Lévi-Strauss drew heavily on the concepts of cybernetics and information theory in his analysis of the deep structures that underlie human thought and behavior, arguing that the human mind itself could be understood as a kind of information processing machine. This idea of the mind as a computational engine seemed appealing, It was one that had also captured the imagination of Neal Stephenson in his novel Cryptonomicon , which I read at around that time. Stephenson’s tale wove together the threads of mathematics, cryptography, and the history of computing in a sprawling, multi-layered narrative that had left a deep impression on me. His vision of a world in which information was the ultimate currency, and in which the power to control and manipulate that information was the key to shaping the future, seemed to resonate with the insights of thinkers like Shannon, Turing, Wiener and Lévi-Straus. As I reflected on these ideas, I found myself returning again and again to the words of the anthropologist, Clifford Geertz, who argued that law was not simply a set of rules or principles, but a way of imagining the real. In a world increasingly shaped by the flow of information and the power of algorithms, it seemed to me that the legal imagination would need to evolve in profound ways to keep pace with the changing nature of reality itself. This realization led me to begin exploring the emerging field of legal informatics, which sought to apply the tools and methods of information science to the study and practice of law. It was a field that was still in its infancy, but one that held enormous promise for unlocking new insights into the complex interplay of law, technology, and society. Now, it seemed, I was making progress. I traced the ripple effects of the foundational crisis of mathematics through the decades that followed, I saw how it had shaped the course of intellectual history in profound and often unexpected ways. By representing the structures of reality, foundations could be validated for coherence. This seemed promising.  I learned of the Vienna Circle’s attempt to build a “unified science” on the basis of logical positivism. This was the dream of Rudolph Carnap: to create a formal language that could capture the logical structure of the world in its grammar and syntax. All of these were in some sense responses to the challenges posed by the foundational crisis, attempts to find a new basis for knowledge in a world where the old certainties had crumbled away. This seemed to be a light of clarity for understanding legal technology! If formal languages, like Carnap imagined, could be used to capture legal reasoning, then computational law would have a clear and certain foundation! This met the world I witnessed evolving around me. Computational contracts, rule-based systems to represent laws as a set of rules, could make law relatively easy to understand! Legal programs were using languages like Prolog   to express the law as logical statements, and law could be tagged with keywords to create what the law librarians were calling “ontologies” of legal knowledge that define and structure legal concepts and their relationships, creating formalized knowledge bases. Lawyers were using tech to tackle legal complexity.  Computational contracts turn agreements into code, enabling clear and automatic execution.  Rule-based systems analyze situations and suggest legal options, promoting consistent decision-making. Legal ontologies promised a structured vocabulary and framework for representing legal concepts, making the law more machine-readable. These tools claim to make law more understandable and interoperable. They make claims like Carnap, who also hoped to remove the ambiguity and subjectivity of natural language through formalization. Perhaps Carnap’s attempts to formalize language showed the way forward! But, then, alas, I read of the fate of logical positivism, and again my heart faded. There would be no foundation for knowledge here as well. Two towering figures had brought it to a halt: Kurt Gödel, the mathematician and friend to Einstein, and Willard van Ormand Quine, the dean of American philosophy for half a century. Gödel developed a precise and cutting analysis of logical systems like the one that Carnap sought to create. And in his two “incompleteness theorems” he demonstrated that in any sufficiently expressive formal system (e.g., one capable of arithmetic), there will always be true statements that cannot be proven within that system. It exposed inherent limitations of purely axiomatic, formal approaches. This implies that no matter how sophisticated a formalization of law is, there will always be legal propositions or interpretations that fall outside its deductive capabilities. It suggests that a formal legal system will  always require human input and  oversight beyond pure computation. Quine, a longtime friend and correspondent with Carnap, advanced a radical critique of the foundational syntax of Carnap’s formal language, which undermined the idea of a sharp boundary between empirical fact and logical necessity, and opened up new vistas of uncertainty. Quine’s approach viewed knowledge as webs of signification. I traced these ideas forward into the realms of jurisprudence and legal theory, I saw how they had shaped the course of twentieth-century legal thought in profound and often counterintuitive ways. The naturalized epistemology of Quine and Brian Leiter, which sought to ground legal reasoning in the methods and findings of empirical science, was in some sense an attempt to find a new foundation for law in the wake of the foundational crisis. And yet, even as these thinkers sought to build a more rigorous and scientific approach to legal theory, they found themselves grappling with the same paradoxes and mysteries that had haunted the thinkers of the early twentieth century. For if the law was indeed a mirror of the deepest structures of reality, then any attempt to reduce it to a set of logical rules or empirical facts was ultimately doomed to incompleteness. In the midst of this swirling vortex of ideas and intuitions, I began to glimpse the true power and potential of legal technology. For if the foundational crisis had taught us anything, it was that the world was far more complex and mysterious than any logical system or formal language could ever hope to capture. And yet, at the same time, it was precisely this complexity and mystery that gave rise to the incredible richness and diversity of human experience, the endless possibilities for creativity and innovation that made the law such a vital and dynamic force in the world. And so, in the end, the law was not just a set of rules or procedures, but a living, breathing, evolving expression of the deepest values and aspirations of the human spirit. And if we could harness the power of technology to serve those values and aspirations, to create new tools and platforms that empowered people to participate more fully in the process of justice, then we would be fulfilling the true promise of the legal profession. But even as I threw myself into this new endeavor with all the passion and energy I could muster, I couldn’t shake the feeling that I was still only scratching the surface of a much deeper truth. The more I learned about the cutting-edge developments in fields like machine learning, natural language processing, and knowledge representation, the more I realized that the traditional tools and methods of legal analysis were woefully inadequate to the task of making sense of this brave new world. I learned that machine learning can analyze historical case data to predict the likely outcomes of new legal cases, considering factors like legal issues, precedent, and history. And, natural language processing (NLP) can automate the classification of legal documents (contracts, briefs, etc.) and extracts key information like dates, entities, and legal provisions. But, at what cost? What insights into law and language were revealed by these developments? Striving for more insights, I explored the work of philosophers like Edmund Husserl, and his students, Martin Heidegger and Jacques Derrida, whose radical critiques of Western metaphysics and language had shaken the foundations of modern thought. Heidegger’s notion of “being-in-the-world,” with its emphasis on the primacy of lived experience and the inextricable entanglement of subject and object, seemed to offer a way out of the dualistic thinking that had long dominated legal theory. And Derrida's concept of “ différance , with its playful deconstruction of the binary oppositions that structure our language and thought, opened up new possibilities for understanding the law as a fluid and dynamic system, always in the process of becoming. As I grappled with these ideas, I found myself exploring the work of contemporary feminist thinkers like Karen Barad and Rosi Braidotti, whose “new materialist” philosophies sought to bridge the gap between the natural and the social sciences, the human and the nonhuman. Barad's notion of “agential realism,” with its emphasis on the inseparability of matter and meaning, challenged me to think in new ways about the relationship between law and the material world. And Braidotti's vision of a “posthuman” future, in which the boundaries between the human and the technological become increasingly blurred, seemed to offer a glimpse of a new kind of legal order, one in which the old distinctions between mind and body, reason and emotion, fact and value, were giving way to a more fluid and dynamic understanding of the world. As I delved deeper into this new intellectual landscape, I began to see connections and resonances that I had never noticed before. The insights of quantum physics, with its strange and paradoxical world of entanglement and uncertainty, seemed to echo the insights of Buddhist and Hindu philosophy, with their emphasis on the fundamental interconnectedness of all things. Luhmann’s concept of “ autopoiesis ” (self-organization), which had emerged from Francisco Varela’s study of living systems, seemed to offer a new way of understanding the dynamics of legal systems, with their complex feedback loops and emergent properties. As I continued my journey through the rich and nuanced landscape of legal technology, I found myself increasingly drawn to the work of contemporary philosophers who were grappling with the deep implications of the information age for our understanding of knowledge, reality, and the human condition. Chief among these was Luciano Floridi, whose groundbreaking work in the philosophy of information had opened up new vistas of insight and understanding. Floridi’s vision of the world as a complex web of informational structures and processes resonated deeply with my own experiences in the realm of legal technology, where the flow of data and the processing of information were rapidly becoming the key drivers of innovation and change. But it was not just Floridi’s technical insights that captured my imagination. It was also his profound ethical and humanistic vision, his belief that the information age presented us with both incredible opportunities and daunting challenges for the future of human knowledge and flourishing. In a world where the boundaries between the virtual and the real were becoming increasingly blurred, Floridi argued, we needed to develop new ways of thinking about the nature of the self, the value of privacy, and the meaning of intellectual property. Alongside Floridi, I found myself deeply influenced by the work of James Ladyman and his collaborator Don Ross, whose structural realist approach to the philosophy of science seemed to offer a powerful new framework for understanding the nature of scientific knowledge in the age of big data and complex systems. Ladyman’s and Ross’s magnum opus, Everything Must Go , was a tour de force of philosophical argumentation, a sweeping critique of the traditional metaphysical assumptions that had long dominated Western thought. In place of the old dichotomies between mind and matter, subject and object, they proposed a new vision of reality as a vast and interconnected web of mathematical structures and informational processes. For Ladyman and Ross, the task of science was not to uncover the hidden essence or intrinsic nature of things, but rather to map out the complex patterns of relations and dependencies that gave rise to the observable phenomena of the world. And in this view, the traditional boundaries between fields like physics, chemistry, biology, and even the social sciences began to dissolve, revealing a deeper unity and interconnectedness that had long been obscured by the silos of academic specialization. As I reflected on these ideas, I began to see how they might transform our understanding of the law and legal reasoning itself. If the world was indeed a vast and interconnected web of informational structures, as Floridi and Ladyman suggested, then the law could no longer be seen as a static set of rules or principles, but rather as a dynamic and evolving system that was constantly processing and responding to new flows of data and information. And if the task of science was to map out the complex patterns of relations and dependencies that gave rise to the observable phenomena of the world, then the task of legal reasoning must be to map out the complex patterns of rights, obligations, and liabilities that gave rise to the social phenomena of justice and fairness. In this view, the power of legal technology lay not just in its ability to automate or streamline the tasks of legal analysis and prediction, but in its ability to help us navigate the vast and complex informational landscape of the law itself. By using tools like machine learning, natural language processing, and network analysis, we could begin to uncover the deep structures and patterns that underlie the surface complexity of legal doctrine and precedent. And in doing so, we could begin to develop a new kind of legal reasoning, one that was more responsive to the dynamic and evolving nature of the social world, more attuned to the complex interdependencies and feedback loops that shape the behavior of individuals and institutions alike. Of course, as with any powerful new technology, the rise of legal informatics also posed daunting challenges and risks. As Floridi himself had warned, the proliferation of digital information and the increasing power of algorithmic decision-making raised profound questions about the nature of privacy, autonomy, and human agency in the age of big data. For a moment, I felt like I was standing on the edge of a new frontier, a vast and uncharted territory that stretched out before me in every direction. And though I knew that the journey ahead would be long and difficult, full of twists and turns and unexpected obstacles, I also knew that I had no choice but to keep pushing forward, to keep exploring the boundaries of the possible and the impossible, the known and the unknown. For in the end, the quest for a new understanding of law and reality was not just an intellectual exercise, a game of abstraction and theory. It was a deeply personal journey, a search for meaning and purpose in a world that often seemed chaotic and indifferent, a world in which the old certainties and verities were crumbling away, leaving us to confront the raw and unmediated mystery of existence itself. And so, I continue to press forward, guided by the conviction that in the mysterious depths of computational law and legal informatics, there lie secrets and wonders yet to be discovered, insights that could transform not only the law, but the very fabric of human society. It is a journey that has taken me from the heights of philosophical speculation to the cutting edge of technological innovation, and one that I know will continue to unfold in strange and unpredictable ways in the years to come. But I am sustained by the knowledge that I am not alone on this path, that there are others who share my fascination and my commitment, and who are working tirelessly to bring about a future in which the law is a powerful instrument of social justice and human flourishing. Together, we press on into the unknown, driven by a shared sense of purpose and a deep faith in the transformative power of human reason and creativity. As I reflect on my journey through the infinite halls of this Library of Legal Babel, I am struck by the realization that, like the eternal traveler in Borges’ story, I am destined to wander forever through its labyrinthine depths. The secrets and wonders that lie hidden within its shelves are not meant to be fully grasped or possessed, but rather to be continually sought and marveled at, in a never-ending cycle of discovery and revelation. And yet, far from being a source of despair or frustration, this realization fills me with a sense of profound hope and purpose. For just as the traveler’s solitude is gladdened by the elegant hope of the Library’s underlying Order, so too am I sustained by the conviction that, beneath the seeming chaos and complexity of the legal-informational landscape, there lies a deeper pattern and meaning waiting to be uncovered. It is this hope that drives me forward, even as the path ahead stretches out into the unknown. Like the explorers and adventurers of old, I am drawn onward by the lure of the horizon, the promise of new vistas and uncharted territories waiting to be mapped and understood. And though I know that the journey will be long and arduous, filled with twists and turns and unexpected challenges, I am comforted by the knowledge that I am part of a larger community of seekers and dreamers, all striving to push the boundaries of what is possible and to build a better, more just world. In the end, then, my wanderings through the Library of Legal Babel are not a solitary quest, but a shared endeavor, a collaborative effort to shine the light of reason and understanding into the darkest and most obscure corners of the legal universe. It is a task that will require all of our creativity, ingenuity, and determination, but one that holds the promise of unlocking a new era of justice and flourishing for all humanity. And so, like Borges’ eternal traveler, I press on, forever seeking, forever learning, forever marveling at the wonders and mysteries that lie waiting to be discovered in the infinite stacks of the Library. It is a journey that has no end, but one that is all the more glorious and worthwhile because of it. For in the end, the true measure of our success will not be the destination we reach, but the knowledge and wisdom we gather along the way, and the lives we touch and transform through our ceaseless pursuit of truth and justice. Reading List Here are some books to stimulate conversation and raise questions. I have tried to avoid overly technical books, either philosophically or technologically. Philosophy of Mathematics: 1. Doxiadis, Apostolos, and Christos Papadimitriou. Logicomix: An Epic Search for Truth . Bloomsbury, 2009. 2. Sigmund, Karl. Exact Thinking in Demented Times: The Vienna Circle and the Epic Question for the Foundations of Science . Basic Books, 2017. Information and Computation: 3. Gleick, James. The Information: A History, A Theory, A Flood . Vintage, 2012. 4. Bernhardt, Chris. Turing’s Vision: The Birth of Computer Science . MIT Press, 2016. Cybernetics and Structuralism: 5. Geoghegan, Bernard Dionysius. Code: From Information Theory to French Theory . Duke University Press Books, 2022. Anthropology 6. Geertz, Clifford. The Interpretation of Cultures . Basic Books, 2017. Logical Positivism: 7. Sigmund, Karl. Exact Thinking in Demented Times: The Vienna Circle and the Epic Quest for the Foundations of Science . Basic Books, 2017. 8. Leiter, Brian. Naturalizing Jurisprudence: Essays on American Legal Realism and Naturalism in Legal Philosophy . Oxford University Press, 2007. Phenomenology: 9. Zahavi, Dan. Phenomenology: The Basics . Routledge, 2018. 10. Dreyfus, Hubert L. Skillful Coping: Essays on the Phenomenology of Everyday Perception and Action . Oxford University Press, 2014. 11. Clark, Andy. Natural-Born Cyborgs: Minds, Technologies, and the Future of Human Intelligence . Oxford University Press, 2003. New Materialisms: 12. Coole, Diana, and Samantha Frost, editors. New Materialisms: Ontology, Agency, and Politics . Duke University Press Books, 2010. 13. Barad, Karen. Meeting the Universe Half-way: Quantum Physics and the Entanglement of Matter and Meaning . Duke University Press Books, 2007. 14. Braidotti, Rosi. Posthuman Feminism . Polity, 2022. Eastern Thought: 15. Siderits, Mark, Evan Thompson, and Dan Zahavi, editors. Self, No Self?: Perspectives from Analytical, Phenomenological, and Indian Traditions . Oxford University Press, 2013. 16. Hinton, David. Existence: A Story . Shambhala, 2016. Philosophy of Information: 17. Floridi, Luciano. Information: A Very Short Introduction . Oxford University Press, 2010. 18. Floridi, Luciano. The Fourth Revolution: How the Infosphere is Reshaping Human Reality . Oxford University Press, 2014. Metaphysics: 19. Ladyman, James, and Don Ross. Every Thing Must Go: Metaphysics Naturalized . Clarendon Press, 2007. Complexity: 20. Mitchell, Melanie. Complexity: A Guided Tour . Oxford University Press, 2009. 21. Parisi, Giorgio. In a Flight of Starlings: The Wonders of Complex Systems . Penguin Press, 2023. Fiction: 22. Stephenson, Neal. Cryptonomicon . William Morrow Press, 2009. 23. Borges, Jorge Luis. Labyrinths: Selected Stories & Other Writings . New Directions, 1962. 24. Chiang, Ted. Exaltation: Stories . Vintage, 2019. 25. Robinson, Kim Stanley. Aurora . Orbit Press, 2015. 26. Goldstein, Rebecca. Incompleteness: The Proof and Paradox of Kurt Gödel . W. W. Norton & Co., 2005. Kevin P. Lee is a Professor of Law at North Carolina Central University , known for his insights into the philosophical and social implications of law and technology. His research seeks to unravel the intricate relationships between legal frameworks, artificial intelligence, and societal values. His academic expertise spans jurisprudence, AI ethics, and law, positioning him as a pioneer in examining how cutting-edge technologies alter legal norms and human rights. Beyond the classroom, Professor Lee plays a pivotal role in shaping policies and advocating for social justice and equity. As the Intel Social Justice and Racial Equity Professor of Law, he works to foster inclusivity within legal education and the profession, championing a legal landscape that mirrors the diversity of the community it represents. His scholarship weaves together in-depth philosophical knowledge with a passionate appeal for leveraging technology to bolster human dignity and societal welfare. His innovative courses and public lectures emphasize the transformative power of education in engaging with contemporary challenges and opportunities. Professor Lee is most known for his intellectual rigor, ethical integrity, and visionary outlook. He is a leading authority in discussions on the future of law, technology, and social equity. His dynamic teaching approach and scholarly work inspire future legal minds to pursue their careers with purpose, integrity, and a commitment to societal good.

AI In Law

Artificial intelligence (AI) refers to the use of computer systems to perform tasks that would normally require human intelligence, such as learning, problem-solving, and decision-making. In the practice of law, AI is being used to automate and streamline various legal processes, including legal research, document review, and prediction of case outcomes.

AI is being used in several legal tech solutions to improve efficiency, reduce costs, and enhance the quality of legal services.

Some examples of how AI is being used in legal tech include:

• Legal research: AI can be used to search through large volumes of legal documents quickly and accurately, saving lawyers and paralegals time and effort.

• Document review: AI can be used to review and analyze large volumes of documents, such as contracts or discovery materials, to identify relevant information or patterns.

• Predictive analytics: AI can be used to analyze data and predict the outcomes of legal cases or disputes, helping lawyers and clients to make informed decisions.

• Legal writing: AI can be used to generate legal documents, such as contracts or pleadings, by combining standard language with specific input provided by the user. • Client intake and triage: AI can be used to process client intake forms and triage cases, routing them to the appropriate lawyer or team for further review.

Machine Learning

Machine learning is a subfield of artificial intelligence focused on the development and use of algorithms and statistical models enabling computers to learn from data and improve their performance on a specific task.

In machine learning, a computer is fed a large dataset and uses that data to train a model to perform a specific task. The model is then tested on a separate dataset to evaluate its performance. If the model performs well, it can be deployed in a real-world application. If the model does not perform well, it can be adjusted and retrained using additional data or different algorithms until it performs satisfactorily.

There are several types of machine learning, including supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.

• In supervised learning, the model is trained on a labeled dataset, meaning that the input data accompanies the correct output. The model makes predictions based on this labeled data and is then tested on a separate dataset to evaluate its accuracy.

• In unsupervised learning, the model is not provided with labeled data and must find patterns in the input data on its own.

• In semi-supervised learning, the model is provided with some labeled data and some unlabeled data and must use the labeled data to make predictions about the unlabeled data.

• In reinforcement learning, the model is trained to take actions in an environment to maximize a reward.

Client and Tech

Clients are aware of technology’s role in today’s world.

To meet your clients where they are demands several things. It demands recognizing that you don’t know everything and be willing to seek out support. It demands using your existing technological tools more productively and learning more about their capabilities. It demands developing an awareness of other technologies that you could be using to benefit your clients.

It’s up to you to decide how you want to make your away in an ever-more connected and yet seemingly chaotic and dynamic world. My simple advice is this: Don’t live in an alternative reality. Accept the reality that exists and learn to succeed in it. Your clients expect you to.

Tech Inflection

The legal and tech worlds are nearing an inflection point. I call it the tech inflection. Lawyers have a bad habit of thinking that something is hard or complex because it needs to be that way. Lawyers often make things more complex than they need to be.

The tech inflection describes the moment when you acknowledge that something challenging need not be so challenging. For example, before services like Uber, people just accepted that getting from A to B was challenging. After Uber, people understood that getting from A to B could be and should be easier.

The tech The same concept applies to the legal industry and the delivery of legal services. Time consuming tasks traditionally done by humans can now be automated and as a result be done be performed more easily and more accurately such as creating a new client intake form and processing that form, creating a basic will or trust, creating a new contract, or automating the review of a contract.

COLIN'S INSIGHTS

Colin's Insights on legal tech cover a wide range of topics in a succinct form. Anything and everything goes here at the intersection of technology and law, from the use of artificial intelligence in the legal field to the effect of emerging technologies on the practice of law.

 

Enjoy these valuable quick reads by joining Colin's website and get notified when new posts are uploaded. Sign-up is easy and free to all.

bottom of page