Challenges of the Application of Emerging Neuroscience Technologies in Courts.

The Role of Neuroscience and Psychology in the Criminal Justice System

Background: Artificial Intelligence (AI) is increasingly being integrated into the criminal justice system, promising to enhance efficiency, accuracy, and decision-making. However, the use of AI also raises significant ethical and legal challenges, including concerns about bias, fairness, transparency, and accountability. These challenges necessitate a thorough examination of AI's impact on the criminal justice system to ensure its benefits are realized without compromising ethical and legal standards. Objective: This study aims to evaluate the impact of AI on the criminal justice system, focusing on the ethical and legal challenges it presents. The research seeks to understand how AI technologies are being implemented, their effects on decision-making processes, and the extent to which they adhere to ethical and legal principles. The goal is to identify best practices and propose solutions to mitigate potential risks. Methods: A mixed-methods approach was employed, combining quantitative surveys and qualitative interviews. Quantitative data were collected from 250 criminal justice professionals, measuring their perceptions of AI's impact on various aspects of the justice system. Qualitative interviews with 40 key stakeholders provided deeper insights into the ethical and legal challenges associated with AI integration. Data were analyzed using statistical methods for the surveys and thematic analysis for the interviews. Results: Findings indicate that AI can significantly enhance the efficiency and accuracy of the criminal justice system but also presents substantial ethical and legal challenges. Issues such as algorithmic bias, lack of transparency, and accountability were frequently highlighted. Best practices identified include implementing rigorous validation processes, ensuring transparency in AI decision-making, and establishing clear accountability frameworks. Conclusion: While AI holds significant promise for improving the criminal justice system, addressing ethical and legal challenges is crucial for its successful integration. Implementing best practices can mitigate risks and ensure that AI technologies are used responsibly. Future research should focus on developing comprehensive guidelines and policies to govern the use of AI in the criminal justice system.

Maximising the Opportunities of Artificial Intelligence for People Living With Cancer

Toward Successful Postbooking Diversion: What Are the Next Steps?

In criminal cases involving minority defendants, some minority legal scholars argue that despite the overwhelming evidence of guilt, racial minority jurors should possess the moral obligation to acquit ‘'guilty'’ defendants as a protest against racial discrimination in the criminal justice and court systems. While the rate of racial acquittals is on the rise in criminal courts in large metropolitan jurisdictions, the present analysis shows that in the O.J Simpson trial involving a number of racial and ethnic minorities, minority jurors are more likely to adhere to the strict application of criminal legal standards —presumed innocence, burden of proof, and reasonable doubt — in their deliberative process. Our empirical analysis reveals that while the presence of biases in law enforcement raised the ‘'reasonable doubt'’ and ‘'proof beyond a reasonable doubt'’ standards among white jurors, none of the three legal standards had statistically significant relations with their determination of the trial outcome. For racial minorities, however, all three legal concepts and racial biases in the criminal justice system show statistically significant impacts on their determination of the Simpson verdict. While there is the greater scrutiny of both presumed innocence and reasonable doubt among racial minority jurors, the concept of the government's burden of proof negatively affected minorities’ views in the Simpson acquittal. This suggests that the government's superior positions and prosecutorial resources may be too much to overcome in order to win an acquittal. Thus the burden of proof standard may measure racial minorities’ sense of powerlessness in obtaining a fair trial and securing an acquittal. Similarly our findings show that racial minorities who believe there are racial biases and prejudices held and used by law enforcement authorities also feel that O.J. Simpson would be adjudicated guilty of murder, suggesting that the government which relies on evidence collected by discriminatory law enforcement agencies might still be too powerful to enable Simpson to win an acquittal verdict. While advocates for racially based jury nullification reinforce the image of lawlessness of minority jurors in America's criminal courts, the present analysis show that, at least in a highly publicized criminal trial involving a prominent minority defendant, minority jurors show the opposite, suggesting that racial minority jurors are indeed law abiding participants in the administration of justice.

Background: This study explores the integration of digital forensic technologies – including online trial broadcasting – into Kazakhstan’s criminal justice system as a key driver of legality, rights protection, and judicial transparency. Drawing on Kazakhstan’s criminal procedure legislation and recent digital transformation initiatives, the authors examine the procedural framework governing digital evidence and prosecutorial responsibilities in a digital environment. Through comparative analysis with advanced international practices, the study identifies effective models for managing electronic evidence, incorporating artificial intelligence (AI), and conducting online trial broadcasts. The article reviews scholarly literature on digital forensics, AI applications in criminal justice, and the challenges of live-streaming court proceedings. Empirical data from Kazakhstan’s judiciary highlight both achievements and persistent legal gaps, particularly in regulating online broadcasts of high-profile criminal cases. Special attention is given to international approaches that successfully balance transparency, data privacy, and procedural fairness – serving as benchmarks for Kazakhstan’s ongoing reforms. From the analyzed sources, the authors extract key theoretical and practical insights to shape a comprehensive prosecutorial model tailored to Kazakhstan’s digital realities. This model aims to safeguard individual rights and public interests, ensure the admissibility and integrity of digital evidence, enhance prosecutorial decision-making through AI tools, and promote judicial openness via regulated online broadcasting. Strategic recommendations are proposed for legislative reform, technological integration, and capacity-building to strengthen prosecutorial effectiveness in Kazakhstan’s digital judicial transformation. Methods: To achieve the research objectives, the authors employed a multi-methodological approach, including: − Descriptive legal analysis to examine Kazakhstan’s legislative framework on digital and forensic technologies in criminal procedure, with emphasis on prosecutorial roles and the regulation of electronic evidence and online broadcasting. − Comparative legal analysis to identify and assess international best practices in digital evidence management, AI integration, and online trial broadcasting, evaluating their relevance to Kazakhstan’s legal and technological context. − Legal modeling to propose scenarios for integrating advanced forensic tools and AI systems into prosecutorial procedures, aiming to optimize evidence handling, enhance transparency, and uphold legal safeguards. − Empirical case studies of Kazakhstan’s judicial practices involving digital evidence and online broadcasts, revealing legislative gaps, procedural challenges, and technological limitations. This comprehensive methodology supports the development of targeted recommendations for reform and modernization within Kazakhstan’s criminal justice system. Results and Conclusion: the study recommends establishing a national digital evidence management platform incorporating AI and blockchain verification, alongside legal procedures for their use and unified cybersecurity standards. Forecasts suggest these measures could reduce criminal case durations by 20–25%, improve evidence analysis accuracy to 90%, and enhance public trust in the justice system. Adapting global best practices to Kazakhstan’s context provides a strategic foundation for modernizing criminal justice and reinforcing the role of public prosecutors.

IntroductionThe scientific literature recognizes the Gut-brain axis (GBA) as a crucial connection between gastrointestinal health and mental well-being. Patients with inflammatory bowel disease (IBD) are at a disproportionately higher risk of developing psychiatric disorders due to factors including gut dysbiosis and chronic inflammatory changes. Recent developments in artificial intelligence (AI) and machine learning, provide novel opportunities to predict the comorbid psychiatric outcomes in patients with IBD by analyzing complex datasets including but not limited to the gut microbiome and neuroimaging data.ObjectivesThis systematic review discusses the current evidence for AI-driven models to aid in the prediction of psychiatric disorders in IBD patients, with a focus on their performance and potential challenges around their clinical implementation.MethodsA systematic search on PubMed, EMBASE, Scopus, and Cochrane databases, identified 28 studies utilizing AI-based models to examine gut microbiota and neuroimaging data in patients with IBD. Data extraction illuminated the following artifacts: classification thresholds (i.e. predictive), relevant supervised learning or deep learning modeling (e.g. random forest classifiers, convolutional neural networks, and unsupervised models like attention-based learning), sensitivity, specificity, accuracy, and both accuracy measures and AUC-ROC curve values.ResultsA pooled analysis of the included studies demonstrated an estimated sensitivity of 81% (95% CI: 77-85%) and specificity of 78% (95% CI: 73-82%) to predict psychiatric disorders in patients with IBD with the highest predictive accuracy elicited by studies based on microbiome and neuroimaging data. Yun et al. (2024), for instance, demonstrated a predictive accuracy of 86% using microbiome profiles and structural brain imaging data while Fil et al. (2024) elucidated the positive correlation between gut dysbiosis and psychiatric symptoms based on microbial signature models. Additionally, the variability noted in the predictive performance of the models was found to be based on the patient population, quality of data, and machine learning strategy.ConclusionsAI models present promising evidence in predicting psychiatric disorders in IBD patients by leveraging microbiome and neuroimaging datasets. Overall, the meta-analysis reports strong predictive strength with high sensitivity and specificity. Future work in this field should focus on the validation of these prediction models in various clinical populations, improving their generalizability and standardization to enable widespread use and integration in the field of personalized psychiatry, especially in patients with IBD.Disclosure of InterestNone Declared

Violence, Criminalization & Punitive Excess

Artificial intelligence (AI), machine learning (ML) and deep learning (DL) have had a profound impact on various sectors including Banking (Fin Tech), Health (HealthTech) and Charitable Fundraising (Charity Tech). The ‘natural’ ability of an AI system to independently perform and, often, outthink its human-counter parts by developing ‘intelligence’(simulating human intelligence) through its own experiences and processing deep layers of information i.e., complex representations of data, and learn has resulted in astounding improvements in the completion of tasks that are complex and technical, time-consuming.AI, with the ease of working with the most granular level of detail, can identify people and objects, recognise voices, uncover patterns and, in advance, screen for problems. Yet, RegTech (or LawTech/LegalTech) has not seen the same level of advancement. AI can provide solutions and enormous economic, political, and social benefits – in terms of public service administration. The purpose of this article is to explore advents in AI (ML and DL) and whether the criminal justice system, in the United Kingdom (UK), which is heavily overburdened, could benefit from some of the advances that have taken place in other sectors and jurisdictions, and whether automation and algorithmic decision making could be used to modernise it. This research draws on domestic and international published law, regulation, and literature, and isset out in six parts, the first partre views the position of the criminal justice system i.e., issues, part two then looks at relative technological advancements in AI, and the Metaverse. Part three explores current advents in AI relating to RegTech (LawTech/LegalTech) and how, if at all, the CJS can use this technology. Part four explores what aspects of the U.K.’s CJS would be fit for automation. Part five focuses on those matters pertaining to AI that pose problems in relation to matters in part 4 i.e., AI discrimination and bias, and explores safeguarding and mitigation including the requirement for explanation as set out in the GDPR. Part six concludes the discussion with some recommendations, as at, January 2024. It is suggested that AI and algorithmic decision making, with the correct legal framework and safeguards in place, could assist in modernising the CJS focussed legal functions, services in law firms, innovating for the next decade. This work is original and timely given the increased debate relating to how AI can assist in modernising the U.K.’s CJS, the global criminal justice challenges, solutions, and what, if any, role the Metaverse can play.

Focus issue: Artificial intelligence in medical physics.

Although the research areas applying artificial intelligence in the field of magnetic resonance imaging (MRI) have been expanding rapidly in recent years, the means to comprehensively understand these research areas have been limited. The purpose of this study was to visualize the research areas related to artificial intelligence in the field of MRI, and to understand the trend of research. Using PubMed database, we extracted article titles applying artificial intelligence in the MRI field from January 1, 1989 to December 31, 2023, created an extracted word list, graphs showing the relative frequency of occurrences of words, and drew a co-occurrence network diagram to investigate the frequency of appearance of words and changes in frequency and characteristic words over time. The number of extracted titles was 2870. The most frequently appearing word was "deep learning" (1170 times from 2019 to 2023). Furthermore, deep learning was the word with the strongest co-occurrence (Jaccard coefficient 0.48 from 2019 to 2023). Regarding words related to organs, there was an increasing trend in the appearance frequency of the brain, prostate, and breast. In recent years, the research area related to artificial intelligence in the field of MRI has become a thriving area involving deep learning. In addition, there were many studies in the diagnostic area throughout the period.

The swift proliferation of Artificial Intelligence (AI) and algorithmic technologies in the criminal justice system and legal profession is apparent worldwide. Machine learning algorithms are currently impacting sentencing determinations in multiple jurisdictions globally. Artificial intelligence, in contrast to the organic intelligence of humans and animals, possesses the ability to learn, adapt, and evolve through the assimilation of knowledge and new information. AI software can independently learn and enhance its performance based on the data it acquires. These algorithms can identify trends, elaborate on them, and discover more effective methods for executing specific activities via automation. Human fallibility necessitates the integration of AI as an essential instrument in judicial decision-making, especially in criminal sentencing, thereby aiding judges in achieving more precise and efficient outcomes, which permits them to concentrate on other matters. This paper assesses the application of artificial intelligence in criminal sentencing within the judicial system from a comparative standpoint. A mixed-method approach was utilised, integrating qualitative and quantitative techniques for data collecting, encompassing in-person interviews and surveys. The research demonstrated that AI systems can swiftly analyse extensive amounts of legal language, facilitating tasks such as transcription, document summarisation, and legal research. AI employs diverse algorithms to address a multitude of problems through pattern recognition and the execution of precise instructions. Through the analysis of huge datasets, AI can deliver impartial recommendations, resulting in more uniform sentence outcomes for analogous situations. Artificial intelligence can evaluate an offender's likelihood of recidivism by analysing variables such as age and educational background, thereby aiding courts in making educated sentence decisions. The study indicated that the risks and obstacles linked to AI in sentencing encompass prejudice amplification, potential coercion of judges to conform to AI suggestions, and ethical issues related to transparency and human rights. To alleviate these hazards, transparency mandates are crucial for enabling judges to comprehend the elements evaluated by AI systems. Stringent data-quality criteria must be established to avert the recurrence of inequitable sentencing practices. Proactive supervision and regulation of AI are essential, accompanied by rigorous legislation overseeing the design and development of algorithms. Consistent auditing, targeted AI training, and instruction for judges and legal practitioners are essential for the proper integration of AI in the criminal justice system..

Invention of big data, machine learning, and artificial intelligence systems has opened the Pandora’s box of opportunities and challenges to the world, and the criminal justice system cannot go untouched by these innovations. The criminal justice system has to assess all pro and cons of implementing the AI system in criminal justice. The first challenge that we face with artificial intelligence in any sector is its sophisticated technological development and the slow pace of development of the legal regime in comparison to artificial technology. The research article concludes by offering solutions for remedying the risks posed by using artificial intelligence in the criminal justice system. A lot of serious concerns and questions including whether the process of adopting judgment is transparent, can we call this as a fair trial, and is it not a violation of due process of law when we use AI in the criminal justice system comes up by the use of AI in the criminal justice system. AI in the criminal justice system will not be able to explain the reasons behind a particular judgment or inquiry, and AI will also have issue in evaluating transparency of the reasoning. Specially, in the criminal justice system, a decision-making process which is deficient in transparency and comprehensibility may not be seen as fair trial and legitimate. Accountability of these AI systems is another important concern that can prove to be a hurdle in implementing AI in the criminal justice system. The main goal of the criminal justice system is to decrease crime, but this is not the only goal as fairness of the procedure is also equally important. In this article, we would like to specify our attention to the main challenges in the sphere of criminal law which are mostly connected with artificial intelligence. The main purpose of this article is to determine the legal definition of artificial intelligence, describe the crucial use of AI in the criminal justice system, and discuss its main challenges in criminal law. The research is conducted with the dialectical method, the systematic analysis of the comparative legal method, the method of formal logic. The interrelated application of the above scientific methods of research helped to achieve the main goals of the present research.

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 &

This informative book is an update of The Report of the LEAA [Law Enforcement Assistance Administration] Task Force on Women, published in October 1975. It evaluates the 1975 recommendations made on issues that the criminal justice field should examine to ensure that women and girls are treated fairly in the criminal justice system. Female offenders, female crime victims, and female criminal justice professionals remain substantially neglected populations in the criminal and juvenile justice systems. Despite the gains made by women since 1975, current evidence shows that: Although the nature and composition of female offenders have changed, the special needs of the burgeoning adult and juvenile offender populations often remain overlooked; Although assistance to crime victims has improved, the need remains for a firm commitment from the criminal justice and juvenile justice systems to change the way these systems respond to women and girls who have been, or potentially could be, victims of crime; Although opportunities for female criminal justice professionals have improved, gender bias and inequality still exist within the criminal justice field and women's progress through the ranks continues to be slow.