The impact of AI on the legal profession: An overview“The reason we should not fear all-knowing; all-controlling machines lies in the current human-machine partnership, which requires both a definable problem and a measurable goal; such inventions remain the subject of science fiction.” (The age of AI: And our Human Future-Eric Schmidt; Daniel Huttenlocher; Henry Kissinger) From game theory, machine learning, big data processing, intelligent search engines, pattern recognition, healthcare, e-banking, up to mass surveillance that surpasses Bentham’s Panopticon, predictive policing and warfare (as we all witnessed and still witness in Gaza). The influence of AI has been growing like never before, penetrating every field and almost encompassing all the facets of our lives, its impact and crucial importance has never been more felt and witnessed all over the globe.The reliance on AI has dominated all business sectors in the developing world, to the point raising the hair of every paranoid alarmist, screaming and screeching: AI IS GOING TO REPLACE US! Fear not dear reader, we don’t live in a Phillip K. Dick novel. For all for all intents and purposes, the advent of AI and its integration in day-to-day human activity and business, is more or less a harbinger of hope, not doom. For what is really happening is a partnership between humans and machines. AI would never replace us, for the simple fact that it cannot do anything on its own, its ‘cognition” is surface level. Even in the application of AI-tools, the human factor is key. Soberly put: AI is a tool for efficient human performance. Indeed, AI is increasingly being integrated into various sectors, including the legal industry. Today, most law firms employ AI-tools and software for performing special tasks such as legal research, contract analysis, and document review. However, the accuracy of AI in legal analysis remains a nuanced issue, influenced by factors such as the quality of training data, the complexity of legal reasoning, and the limitations inherent in machine learning algorithms.But still, we should ask: to what extent will AI-tools be helpful in its direct application by lawyers? for in the grand scheme of things, the legal profession is one of those professions that could be describe as “human, all too human” à la Friedrich Nitzsche. The legal profession in its core relies on human capabilities: legal reasoning is not just a set of deductive reasoning and a cold application of legislation to a set of interchangeable facts. On the contrary, every lawyer has to tread a fine line, where they must not only master the basic and essential elements of the legal texts, but also a sharp sense of what it is possible to do, for the best interest of their clients, in cases where the odds are stacked against them. This fine line could not be crossed by AI, which, in the grand schemes of thing, is just a tool for efficiency. Therefore, we should reformulate our problem thusly: What are the pros and cons of integrating AI as an essential tool in every lawyer’s arsenal in the actual exercise of the legal profession? To find a comprehensive answer to this question, we will be tackling AI application in legal analysis, dispute resolution and confidentiality. Understanding AI in legal analysisLegal analysis is key to every practicing lawyer. Lawyers apply on a daily basis legal analysis for interpreting legislative texts (laws and regulations); dissecting case laws; identifying legal principles; or for the simple task of providing legal advice to clients. AI systems, particularly those using machine learning (ML) and natural language processing (NLP), have been designed to assist in these tasks. The two most prominent types of AI, commonly relied upon by law firms in legal analysis are:• Predictive Analytics: This involves using AI to predict outcomes of legal cases based on historical data and patterns. For instance, tools like Lex Machina use data from court decisions to predict how a case may evolve or how a judge may rule on a particular issue.• Natural Language Processing (NLP): NLP tools analyze large volumes of legal text to extract relevant information, such as identifying precedents, analyzing contracts, or even detecting inconsistencies in legal documents. In application with respect to dispute resolution, AI has proved to be of utmost importance in Online Dispute Resolution (ODR) platforms. ODR tools are applied in the procedural administration of Arbitration, conciliation and mediation. It allows its users to integrate advanced tools like machine learning, smart negotiation in conducting the procedures of their ODR. It should be noted though that ODR is still in its embryonic stage. Strengths of AI in Legal Analysisa. Speed and EfficiencyAI’s primary advantage lies in its ability to process and analyze vast amounts of legal data (and data in general) in a very short time, far outpacing human attorneys by order of magnitude. For instance, AI-powered platforms like Relativity and Everlaw can scan through thousands of documents in a matter of hours, which would otherwise take human lawyers’ days or even weeks to keenly and precisely analyze, for we must not forget the arduous task of leafing through hundreds of pages of documents of all sorts. Some law firm even divide the task on multiple junior lawyers to economize time. With a platform such as Everlaw, all this headache is dispended with. This is particularly useful in large-scale litigation where document review is a major part of the process.b. Reducing Human Error and bias Misinterpretation, misreadings and sometime even overlooking key details are common errors lawyers fall into while reviewing and analyzing large volumes of documents, legal or otherwise. AI-tools significantly reduce these errors by performing what could be described as repetitive tasks with a high degree of accuracy. AI systems like Kira Systems and Luminance are used for contract review, where the software identifies key clauses, inconsistencies, and missing provisions, and highlights potential risks, all from analyzing the contents of a scanned contract. This key feature appears mostly in the use of AI in dispute resolution, known as ODR (Online Dispute Resolution) and ADR (Alternative Dispute Resolution) platforms. This new approach to dispute resolution has a significant benefit, which is the reduction of human biases in the analysis of the cases. c. Cost-EffectivenessAI reduces the costs associated with legal research, document review, and due diligence. By automating routine tasks, lawyers can focus on higher-value work, thus enhancing productivity while lowering the costs for clients.The use of AI in legal document review can reduce the need for large teams of paralegals and junior associates, allowing law firms to offer more affordable services. In a McKinsey and Company 2017 report, it has been estimated that AI could save the legal industry between $2 billion and $4 billion annually in document review alone.II)AI Error and Limits in Legal AnalysisWhile AI has the potential to reduce human error in legal analysis, it is not immune to its own set of limitations and errors. These limitations primarily stem from the constraints of current AI technology, which is heavily reliant on the data it’s trained on, the algorithms that drive it, and the complexity of legal reasoning. Understanding these potential sources of error in AI legal tools is crucial for assessing their reliability and mitigating their risks.In this section, we will discuss AI errors in legal analysis, the limits of AI in the legal field, and explore strategies to improve AI systems for more accurate legal decision-making.1. Types of AI Errors in Legal Analysisa. Data Quality and Bias ErrorsAI systems are only as good as the data on which they are trained. If the dataset is biased, incomplete, or unrepresentative, the AI’s outputs can be flawed. In the context of legal analysis, this is particularly concerning as biased decisions can perpetuate inequalities and unfair outcomes, particularly in sentencing, risk assessment, and hiring.For example, machine learning algorithms used in criminal justice (e.g., risk assessment tools) may produce biased results because they rely on historical data that may reflect systemic biases in law enforcement, prosecution, or sentencing. These biases could lead to wrongful predictions about an individual’s likelihood of reoffending, depending on their race, gender, or socioeconomic background.• Example: The COMPAS (Correctional Offender Management Profiling for Alternative Sanctions) algorithm has been criticized for disproportionately flagging Black defendants as higher risk for recidivism than white defendants with similar criminal histories. A 2016 ProPublica investigation found that the COMPAS tool’s algorithm was more likely to misclassify Black defendants as high risk and white defendants as low risk, raising concerns about its fairness and accuracy in predicting recidivism (check supra).b. Algorithmic ErrorsAI algorithms, especially those based on deep learning and neural networks, may not always make correct predictions, especially in complex legal cases where nuances and contradictions exist. Legal issues often require sophisticated understanding of statutes, precedents, legal principles, and moral or ethical considerations—areas where AI is limited. An AI tool might fail to interpret the meaning of a legal principle in the way a human expert would, leading to incorrect conclusions or recommendations.• Example: In contract analysis, an AI system might misinterpret a contract’s intent due to failure to consider specific language nuances or legal terminology. An AI might flag a provision as irrelevant when in fact, it is crucial to the case, or it might miss a subtle legal issue in the way a clause is framed, leading to a flawed analysis.c. Lack of Contextual UnderstandingLegal analysis often requires understanding the broader context surrounding laws and cases. For example, in interpreting a statute, the historical background, legislative intent, and societal context are often crucial. AI, particularly rule-based systems, can miss these subtleties, as it is primarily focused on identifying patterns from past data without truly “understanding” the law in a human sense.d. Overfitting or UnderfittingIn machine learning, overfitting occurs when a model becomes too tailored to its training data, losing its ability to generalize to new, unseen situations. Conversely, underfitting happens when the model is too simplistic and fails to capture important patterns in the data. Both issues can affect the accuracy of AI in legal contexts.• Example: An AI model trained exclusively on corporate contracts might struggle when applied to labor contracts or governmental agreements because the model has become too focused on one domain, failing to account for the different legal principles that govern these other types of contracts.e. Lack of Transparency and ExplainabilityAI systems, especially deep learning models, are often “black boxes,” meaning it can be difficult to understand how they arrive at specific conclusions. This lack of transparency is problematic in legal settings, where the reasoning behind decisions must be clear, explainable, and justifiable. If an AI tool suggests a legal course of action or makes a recommendation, it is crucial that lawyers and judges understand how the AI arrived at that recommendation.• Example: If an AI system advises a lawyer on the likely outcome of a case based on past precedents, and the outcome turns out to be incorrect, the inability to trace how the AI made its decision can create issues in accountability and trust. The need for explainability in AI is essential in ensuring legal professionals can rely on AI tools responsibly.f. The elephant in the room: breach of confidentiality: This risk appears the most in ODR platforms. The digital processing of a huge amount of data, in which reside a lot of sensitive and personal ones begs the question of the risk of such systems to confidentiality. Especially in world where cyber threats and terrorism are on the rise. And aside from the direct cybernetic threat, which necessitates the involvement of a human agent, there are risks to confidentiality that stems from algorithmic glitches, like for example the unintended data exposure due to a defect in constant AI data training, as is shown in predictive analytics’ revealing sensitive information, if not controlled and managed well. However, such could be mitigated by the insurance of the compliance of such platforms with global standards of data protection, like the EU’s General Data Protection Regulation. In this regard, it should be noted that a law regarding personal data protection has been promulgated in Egypt, which is Law no. 151/2020. Such law could help as a guide to set a standard for data protection to be implemented in ODR in Egypt. g. Risks pertaining to IP infringement: This is highly noticed in the performance of generative AI platforms. The reason is tied to GAI’s mechanism in processing a huge amount of data, visual and written, in order to recognize patterns which are then used to generate opinion, predictions, and answers to their users. Of course, such modus operandi increases the risk of IP infringement. It also begs the question: to what extent copyright, patent, trademark could apply to AI creations, let alone their infringement by said creations? In other words, who owns the content created by generative AI? Is it the user? Or the platform? Or the client? This problem is highly exemplified in the case of Art. Now, generative AI platforms like Chatgpt can generate art. Not only that, it can generate the artistic insignia and style of a particular artist. In last April, the addition of the “Ghibli effect” in Chatgpt, letting its users to create art in the drawing style of the anime studio Ghibli, a stylistic brain child of the late manga artist and film director Hayao Miyazaki, has stirred controversy among artists, most of whom found it a blatant and unashamed violation of IP. Of course, it might be argued that a particular style cannot be regarded as an Intellectual Property proper. But when the case is a blind mimicking of a very stylized and personal art related to a certain artist, this could be construed as a direct violation on IP. If the IP infringement could be pinned down on this trend, then we will have to face other concerns: Is it a problem of derivative and blind copying without permission and fair use? Or are we here in the face of a transformative work that changes the content? And there is also the problem of using these generated images without the consent of the artist. All these are legal concerns that lawyers and Courts will have to find answers to. 2. Limits of AI in Legal Analysisa. Complexity and Creativity of Legal ReasoningLegal analysis often requires sophisticated reasoning, creativity, and judgment that AI cannot replicate. Legal professionals not only interpret existing laws but also creatively apply legal principles to novel situations. AI systems are good at pattern recognition, but they struggle when the situation deviates from the patterns they were trained on.For instance, legal interpretation in the context of constitutional law can involve competing interpretations of ambiguous terms or evolving standards of justice. While AI may excel at analyzing specific statutes or case law, it would struggle to deal with complex issues such as balancing competing rights or interpreting principles in light of contemporary social values.b. Ethical and Moral JudgmentLegal professionals often make decisions that require ethical considerations, such as determining whether a legal outcome might cause harm or if a particular legal standard should evolve. AI systems, however, lack an innate understanding of ethical and moral dilemmas. They rely on historical data, which may not be sufficient to account for evolving social norms or values.• Example: A law firm might use AI to assess the ethical implications of a potential merger, but AI cannot assess whether a merger might harm workers or impact local communities, considerations that go beyond the data and require moral reasoning.3. How to Improve AI in Legal AnalysisTo reduce errors and improve AI’s effectiveness in legal analysis, several strategies can be adopted:a. Better Data CurationEnsuring that the training data used to build AI models is comprehensive, diverse, and free of bias is critical. AI tools used in legal analysis should be trained on datasets that represent a broad spectrum of case law, statutory provisions, and diverse viewpoints. This helps avoid systemic biases, particularly when algorithms are used in sensitive contexts like criminal justice or civil rights.• Solution: Legal institutions and AI developers should collaborate to create large, diverse datasets that reflect the complexity of real-world legal situations, incorporating diverse demographics, contexts, and jurisdictions.b. Human-AI Collaboration (Synthesis)Rather than fully replacing human lawyers or judges, AI should be seen as a tool that augments human decision-making. Legal professionals should use AI tools to assist in routine tasks (e.g., document review, contract analysis, legal research) while retaining ultimate responsibility for complex legal decisions that require judgment, ethics, and contextual understanding.• Solution: Law firms and courts can foster a collaborative approach, where AI handles data-intensive, repetitive tasks, while human legal experts focus on the aspects requiring creative and ethical judgment.c. Continuous Monitoring and FeedbackAI models must be continuously monitored, tested, and updated to adapt to new legal developments, case law, and societal changes. Regular feedback loops from legal professionals can help refine AI systems to better address new legal challenges.• Solution: Establish feedback mechanisms where legal professionals provide input on AI-generated recommendations and use this feedback to improve the models over time. Additionally, constant retraining with updated legal data and case law ensures the tool stays current.ConclusionWhile AI has the potential to reduce human error and bias in legal analysis, its own limitations must be carefully managed. Biases in training data, a lack of context or understanding, and the inability to handle complex ethical reasoning are all significant challenges. However, through better data curation, more transparent algorithms, and effective human-AI collaboration via prompting, training, management, control and the strict compliance with data protection standards, these issues can be mitigated.