The legal framework of ai-generated derivative intellectual works: an analytical comparative study

This dialogue is from an early scene in the 2014 film Ex Machina, in which Nathan has invited Caleb to determine whether Nathan has succeeded in creating artificial intelligence.1 The achievement of powerful artificial general intelligence has long held a grip on our imagination not only for its exciting as well as worrisome possibilities, but also for its suggestion of a new, uncharted era for humanity. In opening his 2021 BBC Reith Lectures, titled "Living with Artificial Intelligence," Stuart Russell states that "the eventual emergence of general-purpose artificial intelligence [will be] the biggest event in human history."2Over the last decade, a rapid succession of impressive results has brought wider public attention to the possibilities of powerful artificial intelligence. In machine vision, researchers demonstrated systems that could recognize objects as well as, if not better than, humans in some situations. Then came the games. Complex games of strategy have long been associated with superior intelligence, and so when AI systems beat the best human players at chess, Atari games, Go, shogi, StarCraft, and Dota, the world took notice. It was not just that Als beat humans (although that was astounding when it first happened), but the escalating progression of how they did it: initially by learning from expert human play, then from self-play, then by teaching themselves the principles of the games from the ground up, eventually yielding single systems that could learn, play, and win at several structurally different games, hinting at the possibility of generally intelligent systems.3Speech recognition and natural language processing have also seen rapid and headline-grabbing advances. Most impressive has been the emergence recently of large language models capable of generating human-like outputs. Progress in language is of particular significance given the role language has always played in human notions of intelligence, reasoning, and understanding. While the advances mentioned thus far may seem abstract, those in driverless cars and robots have been more tangible given their embodied and often biomorphic forms. Demonstrations of such embodied systems exhibiting increasingly complex and autonomous behaviors in our physical world have captured public attention.Also in the headlines have been results in various branches of science in which AI and its related techniques have been used as tools to advance research from materials and environmental sciences to high energy physics and astronomy.4 A few highlights, such as the spectacular results on the fifty-year-old protein-folding problem by AlphaFold, suggest the possibility that AI could soon help tackle science's hardest problems, such as in health and the life sciences.5While the headlines tend to feature results and demonstrations of a future to come, AI and its associated technologies are already here and pervade our daily lives more than many realize. Examples include recommendation systems, search, language translators - now covering more than one hundred languages - facial recognition, speech to text (and back), digital assistants, chatbots for customer service, fraud detection, decision support systems, energy management systems, and tools for scientific research, to name a few. In all these examples and others, AI-related techniques have become components of other software and hardware systems as methods for learning from and incorporating messy real-world inputs into inferences, predictions, and, in some cases, actions. As director of the Future of Humanity Institute at the University of Oxford, Nick Bostrom noted back in 2006, "A lot of cutting-edge AI has filtered into general applications, often without being called AI because once something becomes useful enough and common enough it's not labeled AI anymore."6As the scope, use, and usefulness of these systems have grown for individual users, researchers in various fields, companies and other types of organizations, and governments, so too have concerns when the systems have not worked well (such as bias in facial recognition systems), or have been misused (as in deepfakes), or have resulted in harms to some (in predicting crime, for example), or have been associated with accidents (such as fatalities from self-driving cars).7Dædalus last devoted a volume to the topic of artificial intelligence in 1988, with contributions from several of the founders of the field, among others. Much of that issue was concerned with questions of whether research in AI was making progress, of whether AI was at a turning point, and of its foundations, mathematical, technical, and philosophical-with much disagreement. However, in that volume there was also a recognition, or perhaps a rediscovery, of an alternative path toward AI - the connectionist learning approach and the notion of neural nets-and a burgeoning optimism for this approach's potential. Since the 1960s, the learning approach had been relegated to the fringes in favor of the symbolic formalism for representing the world, our knowledge of it, and how machines can reason about it. Yet no essay captured some of the mood at the time better than Hilary Putnam's "Much Ado About Not Very Much." Putnam questioned the Dædalus issue itself: "Why a whole issue of Dædalus? Why don't we wait until AI achieves something and then have an issue?" He concluded:This volume of Dædalus is indeed the first since 1988 to be devoted to artificial intelligence. This volume does not rehash the same debates; much else has happened since, mostly as a result of the success of the machine learning approach that was being rediscovered and reimagined, as discussed in the 1988 volume. This issue aims to capture where we are in AI's development and how its growing uses impact society. The themes and concerns herein are colored by my own involvement with AI. Besides the television, films, and books that I grew up with, my interest in AI began in earnest in 1989 when, as an undergraduate at the University of Zimbabwe, I undertook a research project to model and train a neural network.9 I went on to do research on AI and robotics at Oxford. Over the years, I have been involved with researchers in academia and labs developing AI systems, studying AI's impact on the economy, tracking AI's progress, and working with others in business, policy, and labor grappling with its opportunities and challenges for society.10The authors of the twenty-five essays in this volume range from AI scientists and technologists at the frontier of many of AI's developments to social scientists at the forefront of analyzing AI's impacts on society. The volume is organized into ten sections. Half of the sections are focused on AI's development, the other half on its intersections with various aspects of society. In addition to the diversity in their topics, expertise, and vantage points, the authors bring a range of views on the possibilities, benefits, and concerns for society. I am grateful to the authors for accepting my invitation to write these essays.Before proceeding further, it may be useful to say what we mean by artificial intelligence. The headlines and increasing pervasiveness of AI and its associated technologies have led to some conflation and confusion about what exactly counts as AI. This has not been helped by the current trend-among researchers in science and the humanities, startups, established companies, and even governments-to associate anything involving not only machine learning, but data science, algorithms, robots, and automation of all sorts with AI. This could simply reflect the hype now associated with AI, but it could also be an acknowledgment of the success of the current wave of AI and its related techniques and their wide-ranging use and usefulness. I think both are true; but it has not always been like this. In the period now referred to as the AI winter, during which progress in AI did not live up to expectations, there was a reticence to associate most of what we now call AI with AI.Two types of definitions are typically given for AI. The first are those that suggest that it is the ability to artificially do what intelligent beings, usually human, can do. For example, artificial intelligence is:The human abilities invoked in such definitions include visual perception, speech recognition, the capacity to reason, solve problems, discover meaning, generalize, and learn from experience. Definitions of this type are considered by some to be limiting in their human-centricity as to what counts as intelligence and in the benchmarks for success they set for the development of AI (more on this later). The second type of definitions try to be free of human-centricity and define an intelligent agent or system, whatever its origin, makeup, or method, as:This type of definition also suggests the pursuit of goals, which could be given to the system, self-generated, or learned.13 That both types of definitions are employed throughout this volume yields insights of its own.These definitional distinctions notwithstanding, the term AI, much to the chagrin of some in the field, has come to be what cognitive and computer scientist Marvin Minsky called a "suitcase word."14 It is packed variously, depending on who you ask, with approaches for achieving intelligence, including those based on logic, probability, information and control theory, neural networks, and various other learning, inference, and planning methods, as well as their instantiations in software, hardware, and, in the case of embodied intelligence, systems that can perceive, move, and manipulate objects.Three questions cut through the discussions in this volume: 1) Where are we in AI's development? 2) What opportunities and challenges does AI pose for society? 3) How much about AI is really about us?Notions of intelligent machines date all the way back to antiquity.15 Philosophers, too, among them Hobbes, Leibnitz, and Descartes, have been dreaming about AI for a long time; Daniel Dennett suggests that Descartes may have even anticipated the Turing Test.16 The idea of computation-based machine intelligence traces to Alan Turing's invention of the universal Turing machine in the 1930s, and to the ideas of several of his contemporaries in the mid-twentieth century. But the birth of artificial intelligence as we know it and the use of the term is generally attributed to the now famed Dartmouth summer workshop of 1956. The workshop was the result of a proposal for a two-month summer project by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon whereby "An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves."17In their respective contributions to this volume, "From So Simple a Beginning: Species of Artificial Intelligence" and "If We Succeed," and in different but complementary ways, Nigel Shadbolt and Stuart Russell chart the key ideas and developments in AI, its periods of excitement as well as the aforementioned AI winters. The current AI spring has been underway since the 1990s, with headline-grabbing breakthroughs appearing in rapid succession over the last ten years or so: a period that Jeffrey Dean describes in the title of his essay as a "golden decade," not only for the pace of AI development but also its use in a wide range of sectors of society, as well as areas of scientific research.18 This period is best characterized by the approach to achieve artificial intelligence through learning from experience, and by the success of neural networks, deep learning, and reinforcement learning, together with methods from probability theory, as ways for machines to learn.19A brief history may be useful here: In the 1950s, there were two dominant visions of how to achieve machine intelligence. One vision was to use computers to create a logic and symbolic representation of the world and our knowledge of it and, from there, create systems that could reason about the world, thus exhibiting intelligence akin to the mind. This vision was most espoused by Allen Newell and Hebert Simon, along with Marvin Minsky and others. Closely associated with it was the "heuristic search" approach that supposed intelligence was essentially a problem of exploring a space of possibilities for answers. The second vision was inspired by the brain, rather than the mind, and sought to achieve intelligence by learning. In what became known as the connectionist approach, units called perceptrons were connected in ways inspired by the connection of neurons in the brain. At the time, this approach was most associated with Frank Rosenblatt. While there was initial excitement about both visions, the first came to dominate, and did so for decades, with some successes, including so-called expert systems.Not only did this approach benefit from championing by its advocates and plentiful funding, it came with the suggested weight of a long intellectual tradition-exemplified by Descartes, Boole, Frege, Russell, and Church, among others-that sought to manipulate symbols and to formalize and axiomatize knowledge and reasoning. It was only in the late 1980s that interest began to grow again in the second vision, largely through the work of David Rumelhart, Geoffrey Hinton, James McClelland, and others. The history of these two visions and the associated philosophical ideas are discussed in Hubert Dreyfus and Stuart Dreyfus's 1988 Dædalus essay "Making a Mind Versus Modeling the Brain: Artificial Intelligence Back at a Branchpoint."20 Since then, the approach to intelligence based on learning, the use of statistical methods, back-propagation, and training (supervised and unsupervised) has come to characterize the current dominant approach.Kevin Scott, in his essay "I Do Not Think It Means What You Think It Means: Artificial Intelligence, Cognitive Work & Scale," reminds us of the work of Ray Solomonoff and others linking information and probability theory with the idea of machines that can not only learn, but compress and potentially generalize what they learn, and the emerging realization of this in the systems now being built and those to come. The success of the machine learning approach has benefited from the boon in the availability of data to train the algorithms thanks to the growth in the use of the Internet and other applications and services. In research, the data explosion has been the result of new scientific instruments and observation platforms and data-generating breakthroughs, for example, in astronomy and in genomics. Equally important has been the co-evolution of the software and hardware used, especially chip architectures better suited to the parallel computations involved in data- and compute-intensive neural networks and other machine learning approaches, as Dean discusses.Several authors delve into progress in key subfields of AI.21 In their essay, "Searching for Computer Vision North Stars," Fei-Fei Li and Ranjay Krishna chart developments in machine vision and the creation of standard data sets such as ImageNet that could be used for benchmarking performance. In their respective essays "Human Language Understanding & Reasoning" and "The Curious Case of Commonsense Intelligence," Chris Manning and Yejin Choi discuss different eras and ideas in natural language processing, including the recent emergence of large language models comprising hundreds of billions of parameters and that use transformer architectures and self-supervised learning on vast amounts of data.22 The resulting pretrained models are impressive in their capacity to take natural language prompts for which they have not been trained specifically and generate human-like outputs, not only in natural language, but also images, software code, and more, as Mira Murati discusses and illustrates in "Language & Coding Creativity." Some have started to refer to these large language models as foundational models in that once they are trained, they are adaptable to a wide range of tasks and outputs.23 But despite their unexpected performance, these large language models are still early in their development and have many shortcomings and limitations that are highlighted in this volume and elsewhere, including by some of their developers.24In "The Machines from Our Future," Daniela Rus discusses the progress in robotic systems, including advances in the underlying technologies, as well as in their integrated design that enables them to operate in the physical world. She highlights the limitations in the "industrial" approaches used thus far and suggests new ways of conceptualizing robots that draw on insights from biological systems. In robotics, as in AI more generally, there has always been a tension as to whether to copy or simply draw inspiration from how humans and other biological organisms achieve intelligent behavior. Elsewhere, AI researcher Demis Hassabis and colleagues have explored how neuroscience and AI learn from and inspire each other, although so far more in one than the other, as and have the success of the current approaches to AI, there are still many shortcomings and as well as problems in It is useful to on one such as when AI does not as or or or that can to or when it on or information about the world, or when it has such as of all of which can to a of public shortcomings have captured the attention of the wider public and as well as among there is an on AI and In recent years, there has been a of to principles and approaches to AI, as well as involving and such as the on AI, that to best important has been the of with to and - in the and developing AI in both and as has been well in recent This is an important in its own but also with to the of the resulting AI and, in its intersections with more the other there are limitations and problems associated with the that AI is not capable of if could to more more or more general AI. In their Turing deep learning and Geoffrey took of where deep learning and highlighted its current such as the with In the case of natural language processing, Manning and Choi the challenges in and despite the of large language Elsewhere, and have the notion that large language models do anything learning, or In & of in a and discuss the problems in systems, the as how to reason about other their systems, and well as challenges in both and especially when the include both humans and Elsewhere, and others a useful of the problems in there is a growing among many that we do not have for the of AI systems, especially as they become more capable and the of use although AI and its related techniques are to be powerful tools for research in science, as examples in this volume and recent examples in which AI not only help results but also by design and become what some have AI to science and and to and challenges for the possibility that more powerful AI could to new in science, as well as progress in some of challenges and has long been a key for many at the frontier of AI research to more capable the of each of AI, the of more general problems that to the possibility of more capable AI learning, reasoning, of and and of these and other problems that could to more capable systems the of whether current characterized by deep learning, the of and and more foundational and and reinforcement or whether different approaches are in such as cognitive agent approaches or or based on logic and probability theory, to name a few. whether and what of approaches be the AI is but many the current along with of and learning architectures have to their about the of the current approaches is associated with the of whether artificial general intelligence can be and if how and Artificial general intelligence is in to what is called that AI and for tasks and goals, such as The development of on the other aims for more powerful AI - at as powerful as is generally to problem or and, in some the capacity to and improve as well as set and its own and the of and when will be is a for most that its achievement have and as is often in and such as A through and The to Ex and it is or there is growing among many at the frontier of AI research that we for the possibility of powerful with to and and with humans, its and use, and the possibility that of could and that we these into how we approach the development of of the research and development, and in AI is of the AI and in its what Nigel Shadbolt the of AI. This is given the for useful and applications and the for in sectors of the However, a few have made the development of their the most of these are and each of which has demonstrated results of increasing still a long way from the most discussed impact of AI and automation is on and the future of This is not In in the of the excitement about AI and and concerns about their impact on a on and the was that such technologies were important for growth and and "the that but not Most recent of this including those I have been involved have and that over time, more are than are that it is the and the and the of will the In their essay AI & and John discuss these for work and further, in & the of & to discuss the with to and and as well as the opportunities that are especially in developing In "The Turing The & of Artificial Intelligence," discusses how the use of human benchmarks in the development of AI the of AI that rather than human He that the AI's development will take in this and resulting for will on the for companies, and a that the that more will be than too much from of the and does not far enough into the future and at what AI will be capable The for AI could from of that in the is and labor and ability to are and and until automation has mostly physical and but that AI will be on more cognitive and tasks based on and, if early examples are even tasks are not of the In other are now in the world machines that that learn and that their ability to do these is to a range of problems they can will be with the range to which the human has been This was and Allen Newell in that this time could be different usually two that new labor will in which will by other humans for their own even when machines may be capable of these as well as or even better than The other is that AI will create so much and all without the for human and the of will be to for when that will the that once the first time since his creation will be with his his to use his from how to the which science and interest will have for to live and and However, most researchers that we are not to a future in which the of will and that until then, there are other and that be in the labor now and in the such as and other and how humans work increasingly capable that and John and discuss in this are not the only of the by AI. Russell a of the potentially from artificial general intelligence, once a of or ten But even we to general-purpose AI, the opportunities for companies and, for the and growth as well as from AI and its related technologies are more than to pursuit and by companies and in the development, and use of AI. At the many the is it is generally that is a in AI, as by its growth in AI research, and as highlighted in several will have for companies and given the of such technologies as discussed by and others the may in the way of approaches to AI and (such as whether they are companies or as and have have the to to in AI. The role of AI in intelligence, systems, autonomous even and other of increasingly In &

Copyright Law and Science

Artificial intelligence has revolutionized the world of creativity, as it has in many other fields. This has also raised several legal issues related to intellectual property and copyright, due to the ability of AI systems to generate original content, sometimes without any human intervention. Can creations and innovations generated by generative artificial intelligence be classified as "intellectual works" subject to legal protection? Who is entitled to the title of "author" for content created using artificial intelligence, or even for content created through a combination of human work and artificial intelligence techniques? These are the most important questions raised by the theme of artificial intelligence in the field of intellectual property, which we will attempt to discuss in this research paper, drawing on the perspectives of comparative jurisprudence in the absence of legal texts detailing these issues.

Technological progress has led to powerful computers and communication technologies that penetrate nowadays all areas of science, industry and our private lives. As a consequence, all these areas are generating digital traces of data amounting to big data resources. This opens unprecedented opportunities but also challenges toward the analysis, management, interpretation and responsible usage of such data. In this paper, we discuss these developments and the fields that have been particularly effected by the digital revolution. Our discussion is AI-centered showing domain-specific prospects but also intricacies for the method development in artificial intelligence. For instance, we discuss recent breakthroughs in deep learning algorithms and artificial intelligence as well as advances in text mining and natural language processing, e.g., word-embedding methods that enable the processing of large amounts of text data from diverse sources such as governmental reports, blog entries in social media or clinical health records of patients. Furthermore, we discuss the necessity of further improving general artificial intelligence approaches and for utilizing advanced learning paradigms. This leads to arguments for the establishment of statistical artificial intelligence. Finally, we provide an outlook on important aspects of future challenges that are of crucial importance for the development of all fields, including ethical AI and the influence of bias on AI systems. As potential end-point of this development, we define digital society as the asymptotic limiting state of digital economy that emerges from fully connected information and communication technologies enabling the pervasiveness of AI. Overall, our discussion provides a perspective on the elaborate relatedness of digital data and AI systems.

The discussion of AI copyright infringement or fair use often skips over all the required steps of the infringement analysis in order to focus on the most intriguing question, “Could a visual generative AI generate a work that potentially infringes a preexisting copyrighted work?” and then the discussion skips further ahead to, “Would the AI have a fair use defense, most likely under the transformative test?” These are relevant questions, but without considering the actual steps of the copyright infringement analysis, the discussion is misleading or even irrelevant. This neglecting of topics and stages of the infringement analysis fails to direct our attention to a properly accused party or entity whose actions prompt the question. Making a sudden transition from a question of infringement in the creation of training datasets to the creation of foundation models that draw from the training data to the actual operation of the generative AI system to produce images makes a false equivalency regarding the processes themselves and the persons responsible for them. The questions ought to shift focus from the persons compiling the training dataset used to train the AI system and the designers and creators of the AI system itself to the end users of the AI system who conceive of and cause the creation of images. The analysis of infringement or fair use in the generative AI context has suffered from widespread misunderstanding concerning the generative AI processes and the control and authorship of the end-user. Claimants, commentators, and regulators have made incorrect assumptions and inaccurate simplifications concerning the process, which I refer to as the Magic File Drawer theory, the Magic Copy Machine theory, and the Magic Box Artist theory. These theories, if they were true, would be much easier to envision and understand than the actual science and technology that goes into the creation and operation of a contemporary visual generative AI system. Throughout this Article, I will attempt to clarify and correct the understanding of the science and technology of the generative AI processes and explain the different roles of the training dataset designers, the generative AI system designers, and the end-users in the rendering of visual works by a generative AI system. Part II will discuss the requirements of a claim of copyright infringement including each step from the copyrightability of the claimant’s work, the doctrines that limit copyrightability, the requirement of an act of copying, and the infringement elements. Part III will summarize the copyright fair use test paying particular attention to the purpose and character of the use analysis, 17 U.S.C. § 107(1), and the current interpretation of the “transformative” test after Andy Warhol Foundation v. Goldsmith, particularly in circumstances relating to technology and the use of copyrighted or copyrightable data sources. Part IV will analyze potential infringement or fair use by the creators of generative AI training datasets. Part V will analyze potential infringement or fair use by the creators of visual generative AI systems. Part VI will analyze potential infringement or fair use by the end-users of visual generative AI systems. For all their complexity, visual generative AI systems are tools that depend on an end-user who conceives of and designs the image and provides the system with a prompt to set the generative process in motion. The end-users are responsible for crafting the prompt or series of prompts used, for evaluating the outputs of the generative AI, for adjusting and editing the iterations of images offered by the AI system, and ultimately for selecting and adopting one of the images generated by the AI as the final image. The end-users then make further decisions about the actual use and its function and purpose for the images the end-users selected and adopted from the outputs of the AI. While working with the AI tool to try to produce a certain image, an end-user might steer the system to produce a work that could, under an infringement analysis, be regarded as potentially infringing, which would lead us again to the fair use analysis based on the end-user’s use of the image.

Many evidences suggest that: 1) Our sensory organs give a poor representation of the world around us; 2) Free will (FW) is an illusion of the mind, so, we cannot decide our actions at will; moreover, we become aware of our actions late; 3) We cannot objectively/scientifically define what is our consciousness for an obvious conflict of interest (“The hard question of consciousness”). Yet, it is interesting to note that the activity of our mind is compatible with proactive cognitive processes, i.e. with individual “Autopoiesis”. “The Bignetti Model” (TBM) explains that Cognition (i.e. “Autopoiesis”) is a thermodynamically-driven computational process that elaborates the so-called “voluntary” reactions against perturbing stimuli. This is a statistical process carried out by a physiological dual state of the mind, i.e. the Unconscious mind (UM) (with a biochemical/biophysical language) and the Conscious Mind (CM) (with a spoken language). UM and CM guide the individual mind in Cognition (Autopoiesis), like in a virtual game in which UM and CM play the roles of the Avatar and of the Player, respectively. In summary, our mind behaves like a Virtual Intelligence (VI) that adjusts the expectations to match reality, though in a virtual context. According to TBM, VI is committed to achieving individual “Autopoiesis” by means of the ability of problem solving, on the one hand, by collecting the relevant data and, on the other one, by improving scientific and technological tools. The most promising tool with a wide application is Artificial Intelligence (AI). The credit to AI is so much inflating that someone might fear that it might substitute for VI in creative and intellectual works. However, this hypothesis is scientifically unattainable. In fact, we know that both UM and CM play a crucial role in cognition, but we don’t know how they reciprocally translate their languages to get this target (see point 3, above); so that VI cannot artificially reproduce UM and CM in AI. While VI is a dual state of the mind that is genetically encoded to carry out Cognition (Autopoiesis) by itself, AI is a product elaborated by VI and not a clone of it. We confirmed this conclusion by interviewing Chatgpt on the meaning of TBM. Though TBM is exhaustively reported in the current, scientific literature, AI could not define it, by itself. AI could approach the solution by fitting a sort of template, only with the aid of the clues and the relative “Data” given by us.

This paper systematically analyzes documented cases and examines human strategic innovation against artificial intelligence systems. Drawing from peer-reviewed research and verified instances in strategic domains including complex games such as Go (Wang et al. in: Proceedings of the 40th international conference on machine learning, 2023), chess (McIlroy-Young et al. in Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, 2020), Dota 2 (Berner et al.. Dota 2 with large-scale deep reinforcement learning. arXiv preprint arXiv:19106680, 2019), and poker (Brown and Sandholm in Science 359:418–424, 2017), as well as real-world applications including cybersecurity (Comiter Attacking artificial intelligence: AI's security vulnerability and what policymakers can do about it. Belfer Center for Science and International Affairs, Harvard Kennedy School, 2019) and finance (Zhang et al., 2024), we identify patterns in human innovation when confronting AI opponents. Our analysis reveals that humans can achieve notable successes by developing novel strategies operating outside AI training distributions, exploiting specific AI limitations (Gleave et al. in International Conference on Machine Learning, 2020). Key findings demonstrate several critical mechanisms. First, pattern-breaking innovations enable humans to force AI systems into unfamiliar decision spaces where their training becomes insufficient (Comiter Attacking artificial intelligence: AI's security vulnerability and what policymakers can do about it. Belfer Center for Science and International Affairs, Harvard Kennedy School, 2019). Second, exploiting AI's bounded rationality allows strategic actors to leverage artificial systems' inherent computational and representational limitations (Simon, 1972). Third, adaptive resource distribution strategies permit dynamic capabilities reallocation based on real-time AI behavioral pattern assessment (Fatima and Wooldridge. in Proceedings of the Fifth International Conference on Autonomous Agents, 2001). In Go, adversarial policies have achieved win rates exceeding 97% against superhuman AI by forcing the system into unfamiliar game states it cannot correctly evaluate (Wang et al. in Proceedings of the 40th International Conference on Machine Learning, 2023). These attacks succeed not through superior Go play but by exploiting fundamental vulnerabilities in how AI systems process information outside their training distributions. Chess analysis indicates that human strategic choices often diverge from AI preferences, with models like Maia specifically designed to predict human moves achieving accuracies of 46–52% for targeted skill levels, highlighting fundamental differences in strategic evaluation between human and artificial intelligence (McIlroy-Young et al. in Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, 2020). While AI systems like OpenAI Five have demonstrated overwhelming dominance in Dota 2, achieving a 99.4% win rate in public games under restricted rule sets (Berner et al. Dota 2 with large-scale deep reinforcement learning. arXiv preprint arXiv:19106680, 2019), and Libratus significantly outperformed top poker professionals in heads-up no-limit Texas Hold'em (Brown and Sandholm in Science 359:418–424, 2017), human approaches in these contexts reveal ongoing attempts to identify and exploit AI behavioral patterns. These efforts demonstrate the persistent potential for strategic innovation even against seemingly dominant artificial systems. The implications of these findings extend beyond gaming applications to broader strategic contexts. They suggest fundamental considerations for AI system design, particularly regarding the need for enhanced strategic flexibility and adaptation capabilities when facing novel adversarial approaches (Wang et al. in Proceedings of the 40th international conference on machine learning, 2023). We propose that these insights should inform next-generation AI system development, emphasizing robust strategic frameworks that can better anticipate and respond to human innovations that operate outside conventional training paradigms. Our research contributes to the theoretical understanding of human-AI strategic interaction and provides practical frameworks for developing more resilient AI systems. The broader implications span multiple domains, including AI safety research (Russell in Human compatible: Artificial intelligence and the problem of control, Viking Press, 2019), human-AI collaboration frameworks (Vaccaro et al. in Nat Hum Behav 8:1869–1886, 2024), and strategic decision-making system design (Chen and Kumar in J Artif Intel Res 79:245–278, 2024).

Summary of Defense Advanced Research Projects Agency's (DARPA) explainable artificial intelligence (XAI) program from the program managers' and evaluator's perspective. Defense Advanced Research Projects Agency (DARPA) formulated the explainable artificial intelligence (XAI) program in 2015 with the goal to enable end users to better understand, trust, and effectively manage artificially intelligent systems. In 2017, the 4-year XAI research program began. Now, as XAI comes to an end in 2021, it is time to reflect on what succeeded, what failed, and what was learned. This article summarizes the goals, organization, and research progress of the XAI program. Dramatic success in machine learning has created an explosion of new AI capabilities. Continued advances promise to produce autonomous systems that perceive, learn, decide, and act on their own. These systems offer tremendous benefits, but their effectiveness will be limited by the machine's inability to explain its decisions and actions to human users. This issue is especially important for the United States Department of Defense (DoD), which faces challenges that require the development of more intelligent, autonomous, and reliable systems. XAI will be essential for users to understand, appropriately trust, and effectively manage this emerging generation of artificially intelligent partners. The problem of explainability is, to some extent, the result of AI's success. In the early days of AI, the predominant reasoning methods were logical and symbolic. These early systems reasoned by performing some form of logical inference on (somewhat) human readable symbols. Early systems could generate a trace of their inference steps, which could then become the basis for explanation. As a result, there was significant work on how to make these systems explainable.1-5 Yet, these early AI systems were ineffective; they proved too expensive to build and too brittle against the complexities of the real world. Success in AI came as researchers developed new machine learning techniques that could construct models of the world using their own internal representations (eg, support vectors, random forests, probabilistic models, and neural networks). These new models were much more effective, but necessarily more opaque and less explainable. The year 2015 was an inflection point in the need for XAI. Data analytics and machine learning had just experienced a decade of rapid progress.6 The deep learning revolution had just begun, following the breakthrough ImageNet demonstration in 2012.6, 7 The popular press was alive with animated speculation about Superintelligence8 and the coming AI Apocalypse.9, 10 Everyone wanted to know how to understand, trust, and manage these mysterious, seemingly inscrutable, AI systems. 2015 also saw the emergence of initial ideas for providing explainability. Some researchers were exploring deep learning techniques, such as the use of deconvolutional networks to visualize the layers of convolutional networks.11 Other researchers were pursuing techniques to learn more interpretable models, such as Bayesian Rule Lists.12 Others were developing model-agnostic techniques that could experiment with a machine learning model—as a black box—to infer an approximate, explainable model, such as LIME.13 Yet, others were evaluating the psychological and human-computer interaction aspects of the explanation interface.13, 14 DARPA spent a year surveying researchers, analyzing possible research strategies, and formulating the goals and structure of the program. In August 2016, DARPA released DARPA-BAA-16-53 to call for proposals. The stated goal of explainable artificial intelligence (XAI) was to create a suite of new or modified machine learning techniques that produce explainable models that, when combined with effective explanation techniques, enable end users to understand, appropriately trust, and effectively manage the emerging generation of AI systems. The target of XAI was an end user who depends on decisions or recommendations produced by an AI system, or actions taken by it, and therefore needs to understand the system's rationale. For example, an intelligence analyst who receives recommendations from a big data analytics system needs to understand why it recommended certain activity for further investigation. Similarly, an operator who tasks an autonomous system needs to understand the system's decision-making model to appropriately use it in future missions. The XAI concept was to provide users with explanations that enable them to understand the system's overall strengths and weaknesses; convey an understanding of how it will behave in future/different situations; and perhaps permit users to correct the system's mistakes. The XAI program assumed an inherent tension between machine learning performance (eg, predictive accuracy) and explainability, a concern that was consistent with the research results at the time. Often the highest performing methods (eg, deep learning) were the least explainable and the most explainable (eg, decision trees) were the least accurate. The program hoped to create a portfolio of new machine learning and explanation techniques to provide future practitioners with a wider range of design options covering the performance-explainability trade space. If an application required higher performance, the XAI portfolio would include more explainable, high-performing, deep learning techniques. If an application required more explainability, XAI would include higher performing, interpretable models. The program was organized into three major technical areas (TAs), as illustrated in Figure 1: (a) the development of new XAI machine learning and explanation techniques for generating effective explanations; (b) understanding the psychology of explanation by summarizing, extending and applying psychological theories of explanation; and (c) evaluation of the new XAI techniques in two challenge problem areas: data analytics and autonomy. The original program schedule consisted of two phases: phase 1, Technology Demonstrations (18 months); and phase 2, Comparative Evaluations (30 months). During phase 1, developers were asked to demonstrate their technology against their own test problems. During phase 2, the original plan was to have developers test their technology against one of two common problems (Figure 2) defined by the government evaluator. At the end of phase 2, the developers were expected to contribute prototype software to an open source XAI toolkit. In May 2017, XAI development began. Eleven research teams were selected to develop the Explainable Learners (TA1) and one team was selected to develop the Psychological Models of Explanation. Evaluation was provided by the Naval Research Lab. The following summarizes those developments and the final state of this work at the end of the program. An interim summary of the XAI developments at the end of 2018 is given in Gunning and Aha.15 The program anticipated that researchers would examine the training process, model representations, and, importantly, explanation interfaces. Three general approaches were envisioned for model representations. Interpretable model approaches would seek to develop ML models that were inherently more explainable and more introspectable for machine learning experts. Deep explanation approaches would leverage deep learning or hybrid deep learning approaches to produce explanations in addition to predictions. Finally, model induction techniques would create approximate explainable models from more opaque, black-box models. Explanation interfaces were expected to be a critical element of XAI, connecting a user to the model to enable them to understand and interact with the decision making process. As the research progressed, 11 XAI teams explored a number of machine learning approaches, such as tractable probabilistic models16 and causal models and explanation techniques such as state machines generated by reinforcement learning algorithms,17 Bayesian teaching,18 visual saliency maps,19-24 and network and GAN dissection.24-26 Perhaps the most challenging and most unique contributions came from the combination of machine learning and explanation techniques27 to conduct well-designed psychological experiments to evaluate explanation effectiveness.28-31 As the program progressed, we also gained a more refined understanding of the spectrum of users and development timeline (Figure 3). The program structure anticipated the need for a grounded psychological understanding of explanation. One team was selected to summarize current psychological theories of explanation to assist the XAI developers and the evaluation team. This work began with an extensive literature survey on the psychology of explanation and previous work on explainability in AI.32 Originally, this team was asked to (a) produce a summary of current theories of explanation, (b) develop a computational model of explanation from those theories, and (c) validate the computational model against the evaluation results from the XAI developers. Developing computational models proved to be a bridge too far, but the team did gain a deep understanding of the area and successfully produced descriptive models. These descriptive models were critical to supporting the effective evaluation approaches, which involved carefully designed user studies, carried out in accordance with DoD human subject research guidelines. Figure 4 illustrates a top-level descriptive model of the XAI explanation process. Evaluation was originally envisioned to be based on a common set of problems, within the data analytics and autonomy domains. However, it quickly became clear that it would be more valuable to explore a variety of approaches across a breadth of problem domains. In order to evaluate the performance in the final year of the program, the evaluation team, led by Eric Vorm, PhD, of the US Naval Research Laboratory (NRL), developed an explanation scoring system (ESS). This scoring system provided a quantitative mechanism for assessing the designs of XAI user studies in terms of technical and methodological appropriateness and robustness. The ESS enabled the assessments of multiple elements of each user study, including the task, domain, explanations, explanation interface, users, hypothesis, data collection, and analysis to ensure that each study met the high standards of human subject research. XAI evaluation measures are shown in Figure 5, and include functional measures, learning performance measures, and explanation effectiveness measures. The DARPA XAI program demonstrated definitively the importance of carefully designing user studies in order to accurately evaluate the effectiveness of explanations in ways that directly enhance appropriate use and trust by human users, and appropriately support human-machine teaming. Often times, multiple types of measures (ie, performance, functionality, and explanation effectiveness) will be necessary to evaluate the performance of an XAI algorithm. XAI user study design can be tricky and the DARPA XAI program discovered that the most effective research teams were ones that featured diverse teams with cross-disciplinary expertise (ie, computer science combined with human-computer interaction and/or experimental psychology, etc.). The XAI program explored many approaches, as shown in Table 1. Interactive debugger interface for visualizing poisoned training datasets. Work is applied on the IARPA TrojAI dataset.33 Establishing objective/quantitative criteria to assess value of explanations for ML models34 CNN-based one-shot detector, using network dissection to identify the most salient features41 Explanations produced by heat maps and text explanations42 Human-machine common ground modeling Indoor navigation with a robot (in collaboration with GA Tech) Video Q&A Human-assisted one-shot classification system by identifying the most salient features Three major evaluations were conducted during the program: one during phase 1 and two during phase 2. In order to evaluate the effectiveness of XAI techniques, researchers on the program designed and executed user studies. User studies are still the gold standard for assessing explanations. There were approximately 12 700 participants in user studies carried out by XAI researchers, including approximately 1900 supervised participants, where the individual was guided through the experiment by the research team (eg, in person or on Zoom) and 10 800 unsupervised participants, where the individual self-guided through the experiment and was not actively guided by the research team (eg, Amazon Mechanical Turk). In accordance with policy for all US DoD funded human subjects research, each research protocol was reviewed by a local Institutional Review Board (IRB) and then a DoD human research protection office reviewed the protocol and the local IRB findings. As mentioned earlier, there seemed to be a natural tension between learning performance and explainability. However, throughout the course of the program, we found evidence that explainability can improve performance. From an intuitive perspective, training a system to produce explanations provides additional supervision, via additional loss functions, training data, or other mechanisms, that encourages a system to learn more effective representations of the world. While this may not be true in all cases and significant work remains to characterize when explainable techniques will be more performant, it provides hope that future XAI systems can be more performant than current systems while meeting user needs for explanations. There currently is no universal solution to XAI. As discussed earlier, different user types require different types of explanations. This is no different from what we face interacting with other humans. Consider, for example, a doctor needing to explain a diagnosis to a fellow doctor, a patient, or a medical review board. Perhaps future XAI systems will be able to automatically calibrate and communicate explanations to a specific user within a large range of user types, but that is still significantly beyond the current state of the art. One of the challenges in developing XAI is measuring the effectiveness of an explanation. DARPA's XAI effort has helped develop foundational technology in this area, but much more needs to be done, including drawing more from the human factors and psychology communities. Measures of explanation effectiveness need to be well established, well understood, and easily implemented by the developer community in order for effective explanations to become a core capability of ML systems. UC Berkeley's result21 demonstrating that advisability, the ability for an AI system to take advice from a user, improves user trust beyond explanations is intriguing. Certainly, users will likely prefer systems where they can quickly correct the behavior of a system in the same ways that humans can provide feedback to each other. Such advisable AI systems that can both produce and consume explanations will be key to enabling closer collaborations between humans and AI systems. Close collaboration is required across multiple disciplines including computer science, machine learning, artificial intelligence, human factors, and psychology, among others, in order to effectively develop XAI techniques. This can be particularly challenging, as researchers tend to focus on a single domain and often need to be pushed to work across domains. Perhaps in the future a XAI-specific research discipline will be created at the intersection of multiple current disciplines. Toward this end, we have worked to create an XAI Toolkit, which collects the various program artifacts (eg, code, papers, reports, etc.) and lessons learned from the 4-year DARPA XAI program into a central, publicly accessible location (https://xaitk.org/).48 We believe the toolkit will be of broad interest to anyone who deploys AI capabilities in operational settings and needs to validate, characterize, and trust AI performance across a wide range of real-world conditions and application areas. Today, we have a more nuanced, less dramatic, and, perhaps, more accurate understanding of AI than we had in 2015. We certainly have a more accurate understanding of the possibilities and the limitations of deep learning. The AI apocalypse has faded from an imminent danger to a distant curiosity. Similarly, the XAI program has produced a more nuanced, less dramatic, and, perhaps, more accurate understanding of XAI. The program certainly acted as a catalyst to stimulate XAI research (both inside and outside of the program). The results have produced a more nuanced understanding of XAI uses and users, the psychology of XAI, the challenges of measuring explanation effectiveness, as well as producing a new portfolio of XAI ML and HCI techniques. There is certainly more work to be done, especially as new AI techniques are developed that will continue to need explanation. XAI will continue as an active research area for some time. The authors believe that the XAI program has made a significant contribution by providing the foundation to launch that endeavor. David Gunning (now retired) is a three-time DARPA program manager, who created and managed the XAI program from its inception in 2016 to its mid-point in 2019. His portfolio of DARPA research programs made significant contributions to the development of AI over the past 25 years. He led the Personalized Assistant that Learns (PAL) program, which produced the technologies behind Apple's Siri. His Command Post of the Future (CPoF) program was later adopted by the US Army as their Command and Control system for use during the Iraq and Afghanistan conflicts. Between DARPA tours, David served in senior positions at Facebook AI, Palo Alto Research Center, Vulcan Inc, Cycorp and co-founded SET Corp. Eric Vorm, PhD, is a cognitive systems engineer and serves as the Deputy Director for the Laboratory for Autonomous Systems Research at the US Naval Research Laboratory in Washington, DC. Dr Vorm led the evaluation team for the DARPA Explainable AI program, and led the development of the first comprehensive criteria for the evaluation of explanations generated by machine learning. Dr Vorm's research focuses on the design of intelligent systems to achieve ideal human-machine teaming, with special emphasis on the role of transparency and explainability in supporting appropriate trust, safety, and reliability in high-risk, time-sensitive operational domains. Jennifer Yunyan Wang, PhD, is a computational neuroscientist with a special focus on AI. As Systems, Engineering and technical Assistance (SETA) contractor to DARPA, she provided technical support and expertise to several programs including XAI, L2M, GARD, and AIE RED. After finishing postdoctoral fellowships in experimental neuroscience at Johns Hopkins University and the Food and Drug Administration, Jennifer joined Quantitative Scientific Solutions in 2018 as a consultant for government R&D and think tanks including IARPA and Center for Security and Emerging Technology. Matt Turek, PhD, joined DARPA's Information Innovation Office (I2O) as a program manager in July 2018 and took over as program manager of the XAI program in 2019. His portfolio also includes the Media Forensics (MediFor), Semantic Forensics (SemaFor), and Machine Common Sense (MCS) programs, as well as the Reverse Engineering of Deceptions (RED) AI Exploration. His research interests include computer vision, machine learning, artificial intelligence, and their application to problems with significant societal impact. Prior to his position at DARPA, Turek led a team at Kitware Inc developing computer vision technologies including large scale behavior recognition and modeling, object detection and tracking, activity recognition, normalcy modeling, and anomaly detection. Data sharing is not applicable to this article as no new data were created or analyzed in this editorial.

When Artificial Intelligence Systems Produce Inventions: The 3A Era and an Alternative Model for Patent Law

The ubiquity of systems using artificial intelligence or has brought increasing attention to how those systems should be regulated. The choice of how to regulate AI systems will require care. AI systems have the potential to synthesize large amounts of data, allowing for greater levels of personalization and precision than ever before|applications range from clinical decision support to autonomous driving and predictive policing. That said, common sense reasoning [McCarthy, 1960] remains one of the holy grails of AI, and there exist legitimate concerns about the intentional and unintentional negative consequences of AI systems [Bostrom, 2003, Amodei et al., 2016, Sculley et al., 2014]. There are many ways to hold AI systems accountable. In this work, we focus on one: explanation. Questions about a legal right to explanation from AI systems was recently debated in the EU General Data Protection Regulation [Goodman and Flaxman, 2016, Wachter et al., 2017], and thus thinking carefully about when and how explanation from AI systems might improve accountability is timely. Good choices about when to demand explanation can help prevent negative consequences from AI systems, while poor choices may not only fail to hold AI systems accountable but also hamper the development of much-needed beneficial AI systems. Below, we briefly review current societal, moral, and legal norms around explanation, and then focus on the different contexts under which explanation is currently required under the law. We find that there exists great variation around when explanation is demanded, but there also exists important consistencies: when demanding explanation from humans, what we typically want to know is how and whether certain input factors affected the final decision or outcome. These consistencies allow us to list the technical considerations that must be considered if we desired AI systems that could provide kinds of explanations that are currently required of humans under the law. Contrary to popular wisdom of AI systems as indecipherable black boxes, we find that this level of explanation should often be technically feasible but may sometimes be practically onerous|there are certain aspects of explanation that may be simple for humans to provide but challenging for AI systems, and vice versa. As an interdisciplinary team of legal scholars, computer scientists, and cognitive scientists, we recommend that for the present, AI systems can and should be held to a similar standard of explanation as humans currently are; in the future we may wish to hold an AI to a different standard.

The academic library community has been at the center of the copyright wars, advancing the interests of students and faculty. Digital and network technologies, the licensing of electronic content, and the globalization of copyright have combined to challenge our traditional views of intellectual property. New laws and legislation over the past decade have threatened the sustenance of fair use and key exceptions to copyright. We must re-commit to the education of our campuses, to political advocacy, and to collective

AI is one of the most debated subjects of today and there seems little common understanding concerning the differences and similarities of human intelligence and artificial intelligence. Discussions on many relevant topics, such as trustworthiness, explainability, and ethics are characterized by implicit anthropocentric and anthropomorphistic conceptions and, for instance, the pursuit of human-like intelligence as the golden standard for Artificial Intelligence. In order to provide more agreement and to substantiate possible future research objectives, this paper presents three notions on the similarities and differences between human- and artificial intelligence: 1) the fundamental constraints of human (and artificial) intelligence, 2) human intelligence as one of many possible forms of general intelligence, and 3) the high potential impact of multiple (integrated) forms of narrow-hybrid AI applications. For the time being, AI systems will have fundamentally different cognitive qualities and abilities than biological systems. For this reason, a most prominent issue is how we can use (and “collaborate” with) these systems as effectively as possible? For what tasks and under what conditions, decisions are safe to leave to AI and when is human judgment required? How can we capitalize on the specific strengths of human- and artificial intelligence? How to deploy AI systems effectively to complement and compensate for the inherent constraints of human cognition (and vice versa)? Should we pursue the development of AI “partners” with human (-level) intelligence or should we focus more at supplementing human limitations? In order to answer these questions, humans working with AI systems in the workplace or in policy making have to develop an adequate mental model of the underlying ‘psychological’ mechanisms of AI. So, in order to obtain well-functioning human-AI systems, Intelligence Awareness in humans should be addressed more vigorously. For this purpose a first framework for educational content is proposed.

Over the last few years, the world has deliberated and developed numerous ethical principles and frameworks. It is the general opinion that the time has arrived to move from principles and to operationalize on the ethical practice of AI. It is now recognized that principles and standards can play a universal harmonizing role for the development of AI-related legal norms across the globe. However, how do we translate and embrace these articulated values, principles and actions to guide Nation States around the world to formulate their regulatory systems, policies or other legal instruments regarding AI? Our regulatory systems have attempted to keep abreast of new technologies by recalibrating and adapting our regulatory frameworks to provide for new opportunities and risks, to confer rights and duties, safety and liability frameworks, and to ensure legal certainty for businesses. These past adaptations have been reactive and sometimes piecemeal, often with artificial delineation on rights and responsibilities and with unintended flow-on consequences. Previously, technologies have been deployed more like tools, but as autonomy and self-learning capabilities increase, robots and intelligent AI systems will feel less and less like machines and tools. There is now a significant difference, because machine learning AI systems have the ability ‘to learn’, adapt their performances and ‘make decisions’ from data and ‘life experiences’. This paper presented at the International Joint Conference on Artificial Intelligence - Pacific Rim International Conference on Artificial Intelligence in 2021 provides brief insights on some selected topical developments in ethical principles and frameworks, our regulatory systems and the current debates on some of the risks and challenges from the use and actions of AI, autonomous and intelligent systems [1].KeywordsAIRobotsAutomationRegulationEthicsLawLiabilityTransparencyExplainabilityData protectionPrivacyLegal personhoodJob transitionEmployment

Intellectual property rights are related to human creativity which is the result of the work of creativity, human sense and intention. In Intellectual Property Rights that are protected is the right of the owner of Intellectual Property Rights, not the final product produced by the owner of Intellectual Property Rights. In the global economic order, Intellectual Property Rights are seen as a trade problem that includes interactions of three main aspects, namely intellectual property, commercialization and legal protection. This means that Intellectual Property Rights become important when there are intellectual works that will be commercialized so that the owners of intellectual works need formal legal protection to protect their interests in obtaining benefits from the commercialization of their intellectual work. This study explains the role of how the decision of the Patent Appeal Commission on patent applications, especially on patent applications number W00200903691 submitted by Repros Therapeutics Inc. through Intellectual Property Consultants in Indonesia who represent it (in this case AMR Partnership) with the title of the invention "Antiprogestin Dosage Regimen" and how to analyze the decision.

Three AI systems were used and analyzed on their ability to create farm emergency plans. AI were presented with three farm emergency scenarios to access their completeness and accuracy of response. AI was not able to present complete farm emergency plans, as human intervention was needed for a complete FEP. AI responded well for individual emergency scenarios presented, containing key safety points. The ability of three artificial intelligence systems (ChatGPT, Microsoft Copilot, and Google Gemini) to generate functional Farm Emergency Plans (FEP) for a typical Midwestern row crop grain farm was evaluated. Four prompts, each of increasing levels of specificity, were used with the three AI systems, yielding twelve distinct FEPs. A rubric was developed to evaluate each of the twelve AI products against the findings of a review of relevant current literature including academic, government, not-for-profit, and insurance sources to identify essential and consistent components of a FEP. Both ChatGPT and Microsoft Copilot were found to provide valuable starting points for developing FEPs when detailed prompts were provided, while Google Gemini results were less useful. However, none of the systems were capable of independently generating FEPs at the time of this study. Plans that were deemed as unreliable or incomplete enough for application were primarily due to the diverse nature of agricultural operations, limited resources on agricultural emergency preparedness, and the lack of maturity of current AI systems. Findings showed the essential need of using AI systems in collaboration with human guidance and input from other evidence-based sources to create effective FEPs. Similar results were confirmed in which the AI systems were prompted for emergency responses to three specific farm-related emergencies as part of the FEP: (1) flowing grain entrapment, (2) hazardous agricultural chemical spills, and (3) anhydrous ammonia exposure. The need for additional input was found to be essential. Outcomes were limited in scope to the particular type of farm selected for testing and the ability of the AI systems when they were queried on 30 September 2024; 12 February 2025; and 7 March 2025. Since AI systems rapidly continue to mature as they are "exercised," further inquiries will, therefore, yield different outcomes, because AI has become more sophisticated and developed every day. It should also be noted that for "best practices," the inquirer should provide AI with any resources that they have found and provide multiple inquiries to gain the best and most accurate results. This study demonstrated the potential that AI offers to agricultural producers, specifically in emergency preparedness and response, while emphasizing prompt development and user competency to verify AI outputs.