Preserving the rule of law in the era of artificial intelligence (AI)

The study of law and information technology comes with an inherent contradiction in that while technology develops rapidly and embraces notions such as internationalization and globalization, traditional law, for the most part, can be slow to react to technological developments and is also predominantly confined to national borders. However, the notion of the rule of law defies the phenomenon of law being bound to national borders and enjoys global recognition. However, a serious threat to the rule of law is looming in the form of an assault by technological developments within artificial intelligence (AI). As large strides are made in the academic discipline of AI, this technology is starting to make its way into digital decision-making systems and is in effect replacing human decision-makers. A prime example of this development is the use of AI to assist judges in making judicial decisions. However, in many circumstances this technology is a ‘black box’ due mainly to its complexity but also because it is protected by law. This lack of transparency and the diminished ability to understand the operation of these systems increasingly being used by the structures of governance is challenging traditional notions underpinning the rule of law. This is especially so in relation to concepts especially associated with the rule of law, such as transparency, fairness and explainability. This article examines the technology of AI in relation to the rule of law, highlighting the rule of law as a mechanism for human flourishing. It investigates the extent to which the rule of law is being diminished as AI is becoming entrenched within society and questions the extent to which it can survive in the technocratic society.

     

The intelligence left in AI

In its forty years of existence, Artificial Intelligence has suffered both from the exaggerated claims of those who saw it as the definitive solution of an ancestral dream — that of constructing an intelligent machine-and from its detractors, who described it as the latest fad worthy of quacks. Yet AI is still alive, well and blossoming, and has left a legacy of tools and applications almost unequalled by any other field-probably because, as the heir of Renaissance thought, it represents a possible bridge between the humanities and the natural sciences, philosophy and neurophysiology, psychology and integrated circuits-including systems that today are taken for granted, such as the computer interface with mouse pointer and windows. This writing describes a few results of AI that have modified the scientific world, as well as the way a layman sees computers: thetechnology of programming languages, such asLISP-witness the unique excellence of academic departments that have contributed to them-thecomputing workstations-of which our modern PC is but a vulgarised descendant-theapplications to the educational field-e.g., the realisation of some ideas of genetic epistemology-and tointerdisciplinary philosophy-such as Hofstadter's associations between the arts and mathematics-and the use ofAI techniques in music and musicology. All this has led to a generalisation of AI towards Negrotti's overallTheory of the Artificial, which encompasses further specialisation such asartificial reality, artificial life, and applications ofneural networks among others.     

Pathologies of AI: Responsible use of artificial intelligence in professional work

Although the AI paradigm is useful for building knowledge-based systems for the applied natural sciences, there are dangers when it is extended into the domains of business, law and other social systems. It is misleading to treat knowledge as a commodity that can be separated from the context in which it is regularly used. Especially when it relates to social behaviour, knowledge should be treated as socially constructed, interpreted and maintained through its practical use in context. The meanings of terms in a knowledge-base are assumed to be references to an objective reality whereas they are instruments for expressing values and exercising power. Expert systems that are not perspicuous to the expert community will lose their meanings and cease to contain genuine knowledge, as they will be divorced from the social processes essential for the maintenance of both meaning and knowledge. Perspicuity is usually sacrificed when knowledge is represented in a formalism, with the result that the original problem is compounded with a second problem of penetrating the representation language. Formalisms that make business and legal problems easier to understand are one essential research goal, not only in the quest for intelligent machines to replace intelligent human beings, but also in the wiser quest for computers to support collaborative work and other forms of social problem solving.     

Applications of artificial intelligence in intelligent manufacturing: a review

Based on research into the applications of artificial intelligence (AI) technology in the manufacturing industry in recent years, we analyze the rapid development of core technologies in the new era of ‘Internet plus AI’, which is triggering a great change in the models, means, and ecosystems of the manufacturing industry, as well as in the development of AI. We then propose new models, means, and forms of intelligent manufacturing, intelligent manufacturing system architecture, and intelligent manufacturing technology system, based on the integration of AI technology with information communications, manufacturing, and related product technology. Moreover, from the perspectives of intelligent manufacturing application technology, industry, and application demonstration, the current development in intelligent manufacturing is discussed. Finally, suggestions for the application of AI in intelligent manufacturing in China are presented.     

Hybrid artificial intelligence architecture for diagnosis and decision-making in manufacturing

This paper presents an architecture which combines artificial neural networks (ANNs) and an expert system (ES) into a hybrid, self-improving artificial intelligence (AI) system. The purpose of this project is to explore methods of combining multiple AI technologies into a hybrid intelligent diagnostic and advisory system. ANNs and ESs have different strengths and weaknesses, which can be exploited in such a way that they are complementary to each other: strengths in one system make up for weaknesses in the other, andvice versa. There is, presently, considerable interest in ways to exploit the strengths of these methodologies to produce an intelligent system which is more robust and flexible than one using either technology alone. Any process which involves both data-driven (bottom-up) and concept-driven (top-down) processing is especially well suited to displaying the capabilities of such a hybrid system. The system can take an incoming pattern of signals, as from various points in an automated manufacturing process, and make intelligent process control decisions on the basis of the pattern as preprocessed by the ANNs, with rule-based heuristic help or corroboration from the ES. Patterns of data from the environment which can be classified by either the ES or a human consultant can result in a high-level ANN being created and trained to recognize that pattern on future occurrences. In subsequent cases in which the ANN and the ES fail to agree on a decision concerning the environmental situation, the system can resolve those differences and retrain the networks and/or modify the models of the environment stored in the ES. Work on a hybrid system for perception in machine vision has been funded initially by an Oak Ridge National Laboratory seed grant, and most of the system components are operating presently in a parallel distributed computer environment.     

Artificial intelligence in manufacturing: Forecasts for the use of artificial intelligence in the USA

The use of artificial intelligence in manufacturing has finally emerged as a reality in the United States. Buoyed by the general growth of trained computer scientists, the lower cost hardware and software designed for easier use input, a number of experimental programs are under way to exploit artificial intelligence for manufacturing purposes.     

An artificial intelligence planning approach to manufacturing feature recognition

Within manufacturing, features have been widely accepted as useful concepts, and in particular they are used as an interface between CAD and CAPP systems. Previous research on feature recognition focus on the issues of intersecting features and multiple interpretations, but do not address the problem of custom features representation. Representation of features is an important aspect for making feature recognition more applicable in practice. In this paper a hybrid procedural and knowledge-based approach based on artificial intelligence planning is presented, which addresses both classic feature interpretation and also feature representation problems. STEP designs are presented as case studies in order to demonstrate the effectiveness of the model.     

The application of artificial intelligence in auditing: Looking back to the future

ICT-based decision aids are currently making waves in the modern business world simultaneously with increased pressure on auditors to play a more effective role in the governance and control of corporate entities. This paper aims to review the main research efforts and current debates on auditors’ use of artificial intelligent systems, with a view to predicting future directions of research and software development in the area. The paper maps the development process of artificial intelligent systems in auditing in the light of their identified benefits and drawbacks. It also reviews previous research efforts on the use of expert systems and neural networks in auditing and the implications thereof. The synthesis of these previous studies revealed certain research vacuum which future studies in the area could fill. Such areas include matching the benefits of adopting these intelligent agents with their costs, assessing the impact of artificial intelligence on internal control systems’ design and monitoring as well as audit committees’ effectiveness, and implications of using such systems for small and medium audit firms’ operations and survival, audit education, public sector organisations’ audit, auditor independence and audit expectations-performance gap.     

Managing artificial intelligence projects: Key insights from an AI consulting firm

While organisations are increasingly interested in artificial intelligence (AI), many AI projects encounter significant issues or even fail. To gain a deeper understanding of the issues that arise during these projects and the practices that contribute to addressing them, we study the case of Consult, a North American AI consulting firm that helps organisations leverage the power of AI by providing custom solutions. The management of AI projects at Consult is a multi-method approach that draws on elements from traditional project management, agile practices, and AI workflow practices. While the combination of these elements enables Consult to be effective in delivering AI projects to their customers, our analysis reveals that managing AI projects in this way draw upon three core logics, that is, commonly shared norms, values, and prescribed behaviours which influence actors' understanding of how work should be done. We identify that the simultaneous presence of these three logics—a traditional project management logic, an agile logic, and an AI workflow logic—gives rise to conflicts and issues in managing AI projects at Consult, and successfully managing these AI projects involves resolving conflicts that arise between them. From our case findings, we derive four strategies to help organisations better manage their AI projects.     

Applications of artificial intelligence in engineering and manufacturing: a systematic review

Journal of Intelligent Manufacturing - Engineering and manufacturing processes and systems designs involve many challenges, such as dynamism, chaotic behaviours, and complexity. Of late, the...     

Evolution, sociobiology, and the future of artificial intelligence

What will AI systems be like in the near and long terms? Basically, we'll get the AI that people are willing to pay for. Consequently, many specialized applications will appear long before AI demonstrates its "Manifest Destiny" of human-level general intelligence. The AI demonstrations and applications we're going to see in the near future will trend strongly toward "cognitive prostheses" - systems that do well things that humans do poorly or don't like to do. Both near-term and far-future systems will need to interact smoothly with humans, which will put special constraints on them. In particular, to build systems that we'll trust and want to use, we'll need to carefully consider and craft their implicit and explicit values.     

Integrating scheduling and control functions in computer integrated manufacturing using artificial intelligence

Proper integration of scheduling and control in Flexible Manufacturing Systems will make available the required level of decision-making capacity to provide a flexibly-automated, efficient, and quality manufacturing process. To achieve this level of integration, the developments in computer technology and sophisticated techniques of artificial intelligence (AI) should be applied to such FMS functions as scheduling. In this paper, we present an Intelligent Scheduling System for FMS under development that makes use of the integration of two AI technologies. These two AI technologies — Neural Networks and Expert Systems — provide the intelligence that the scheduling function requires in order to generate goodschedules within the restrictions imposed by real-time problems. Because the system has the ability to plan ahead and learn, it has a higher probability of success than conventional approaches. The adaptive behavior that will be achieved contribute to the integration of scheduling and control in FMS.     

VIS+AI: integrating visualization with artificial intelligence for efficient data analysis

Visualization and artificial intelligence (AI) are well-applied approaches to data analysis. On one hand, visualization can facilitate humans in data understanding through intuitive visual representation and interactive exploration. On the other hand, AI is able to learn from data and implement bulky tasks for humans. In complex data analysis scenarios, like epidemic traceability and city planning, humans need to understand large-scale data and make decisions, which requires complementing the strengths of both visualization and AI. Existing studies have introduced AI-assisted visualization as AI4VIS and visualization-assisted AI as VIS4AI. However, how can AI and visualization complement each other and be integrated into data analysis processes are still missing. In this paper, we define three integration levels of visualization and AI. The highest integration level is described as the framework of VIS+AI, which allows AI to learn human intelligence from interactions and communicate with humans through visual interfaces. We also summarize future directions of VIS+AI to inspire related studies.     

Artificial intelligence (AI) and robotics in travel,hospitality and leisure

Electronic Markets -     

A hybrid artificial intelligence approach for improving yield in precious stone manufacturing

Reducing wastage from the unnecessary cutting of raw material is a key issue in the manufacture of diamonds and gemstones. The accuracy with which stones are graded prior to their being processed through the various manufacturing stages of cutting and finishing is a key determinant of yield and so profit. This presently manual activity requires a skilled craftsman to assess the grade and spot opportunities for upgrading through the judicious cutting away of imperfections in the raw material. There is however a balance to be struck between raising quality and lowering wastage. This paper describes iGem, an artificial intelligence tool that integrates rule-based knowledge representation, fuzzy logic and genetic algorithms to produce a system for automating, and introducing consistency into, the grading of diamonds and gemstones. In this paper we show how iGem derives its knowledge from repeated examples of previously correctly graded stones and can improve its performance by learning from experience. The industrial benefit of iGem extends beyond simply improving grading but also to the introduction of consistency and so greater control into the overall manufacturing process. We believe the approach described has application in other situations where overall yield and manufacturing efficiency depends on trade-off decisions between removal of imperfections and loss of material as well as consistency in quality assessment. A further noteworthy aspect of the iGem project is its development of an objective quality assessment technique out of a hitherto substantially subjective one.     

The challenge of artificial intelligence

Artificial intelligence (AI) is a relatively young discipline, yet it has already led to general-purpose problem-solving methods and novel applications. Ultimately, AI's goals of creating models and mechanisms of intelligent action can be realized only in the broader context of computer science. Creating mechanisms for sharing of knowledge, knowhow, and literacy is the challenge. The great Chinese philosopher Kuan-Tzu once said: “If you give a fish to a man, you will feed him for a day. If you give him a fishing rod, you will feed him for life.” We can go one step further: If we can provide him with the knowledge and the know-how for making that fishing rod, we can feed the whole village. Therein lies the promise-and the challenge-of AI     

Crowd intelligence in AI 2.0 era

The Internet based cyber-physical world has profoundly changed the information environment for the development of artificial intelligence (AI), bringing a new wave of AI research and promoting it into the new era of AI 2.0. As one of the most prominent characteristics of research in AI 2.0 era, crowd intelligence has attracted much attention from both industry and research communities. Specifically, crowd intelligence provides a novel problem-solving paradigm through gathering the intelligence of crowds to address challenges. In particular, due to the rapid development of the sharing economy, crowd intelligence not only becomes a new approach to solving scientific challenges, but has also been integrated into all kinds of application scenarios in daily life, e.g., online-to-offline (O2O) application, real-time traffic monitoring, and logistics management. In this paper, we survey existing studies of crowd intelligence. First, we describe the concept of crowd intelligence, and explain its relationship to the existing related concepts, e.g., crowdsourcing and human computation. Then, we introduce four categories of representative crowd intelligence platforms. We summarize three core research problems and the state-of-the-art techniques of crowd intelligence. Finally, we discuss promising future research directions of crowd intelligence.     

Artificial intelligence (AI) for medical imaging to combat coronavirus disease (COVID-19): a detailed review with direction for future research

Artificial Intelligence Review - Since early 2020, the whole world has been facing the deadly and highly contagious disease named coronavirus disease (COVID-19) and the World Health Organization...