AI & SOCIETY

The public release of datasets on the internet by government agencies, environmental scientists, political groups and many other organizations has fostered a social practice of data visualization. The audiences have expectations of production values commensurate with their daily experience of professional visual media. At the same time, access to this data has allowed visual designers and artists to apply their skills to what was previously a field dominated by scientists and engineers. The ‘aesthetic turn’ in data visualization has sparked debates between the new wave and older more scientifically grounded schools of thought on the topic. Sonification is not as well known or commonly practiced as visualization. But sound is a naturally affective, aesthetic and cultural medium. The extension of the aesthetic turn to sonification could transform this field from a scientific curiosity and engineering instrument into a popular mass medium. This paper proposes that a design approach can facilitate an aesthetic turn in sonification that integrates aesthetics and functionality by dissolving divisions between scientific and artistic methods. The first section applies the design perspective to the definition of sonification by replacing the linguistic concept of representation with non-verbal concept of functionality. The next section describes applications of the TaDa design method that raised aesthetic issues particular to sonification practice. The final section proposes a pragmatic aesthetics that distinguishes sonification from the auditory sciences and sonic arts. A design perspective may lead to a future where the general public tunes into pop sonifications for listening enjoyment as well as useful information about the world.

Traditional notions of crime control often position the police against an individual, known or not yet known, who is responsible for the commission of a crime. However, with increasingly sophisticated technology, policing increasingly prioritizes the prevention of crime, making it necessary to ascertain who, or what class of persons, may be the next likely criminal before a crime can be committed, termed predictive policing. This causes a shift from individualized suspicion toward predictive profiling that may sway the expectations of a police patrol. Classically, where a patrol officer forms reasonable suspicion prior to a stop, it is based upon his/her analysis of the situation taken as a whole in context. However, where a predictive profile is employed, information available to the officer accordingly adjusts his/her perception of context and affects the application of the reasonable suspicion standard. This article addresses the way in which new approaches to forming reasonable suspicion affect the due process protection of individuals’ fundamental rights. It argues that while an officer still operates with good faith discretion, using predictive profiling causes reasonable suspicion to be based on an augmented understanding of reality and as a result, due process guarantees are weakened. The rights to non-discrimination and the presumption of innocence are assessed and argued as illustrative of this weakening and shift in policing standards. The article ultimately argues that while predictive policing cannot be categorically labeled as inconsistent with criminal justice, changes as seemingly moderate as the manner in which discretion operates have larger effects on individuals’ rights.

During the past decade teamwork in manufacturing, as in other sectors, has become the organisational form of choice. In contrast to earlier manifestations such as autonomous workgroups some 30 years earlier, this appears to have been largely for business and production reasons rather than being directly aimed at improving the quality of work life. Taken from part of a larger study of teamworking in several different manufacturing companies this paper draws upon a retrospective analysis of cases of self-managed team implementation in UK manufacturing. Examples of good and bad practice and consequent guidance have been identified where the weight of evidence gives confidence in their general applicability. A basis for benchmarking is provided, related to preparation, implementation and maintenance of self-managed teams in manufacturing. Key gaps identified are for tools to assist in early functional analysis, leadership decisions and boundary setting, and appropriate team performance measurement and reward systems.

Expert systems provide new languages and a new methodology for automating knowledge-intensive processes. Whilst the benefits expected are ubiquitously stated, probable negative impacts are seldom admitted by the dominant actors in the field. We deal with probable problematic impacts on employment as well as contents and structure of work both in production and the service and administration areas and make some suggestions concerning measures to be taken to account for these impacts assuming no radical change as to the prevailing ideology of working organization.

Legal structures may form barriers to, or enablers of, adoption of precision agriculture management with small autonomous agricultural robots. This article develops a conceptual regulatory framework for small autonomous agricultural robots, from a practical, self-contained engineering guide perspective, sufficient to get working research and commercial agricultural roboticists quickly and easily up and running within the law. The article examines the liability framework, or rather lack of it, for agricultural robotics in EU, and their transpositions to UK law, as a case study illustrating general international legal concepts and issues. It examines how the law may provide mitigating effects on the liability regime, and how contracts can be developed between agents within it to enable smooth operation. It covers other legal aspects of operation such as the use of shared communications resources and privacy in the reuse of robot-collected data. Where there are some grey areas in current law, it argues that new proposals could be developed to reform these to promote further innovation and investment in agricultural robots.

Do những tiến bộ công nghệ liên tục, sự hợp tác giữa con người và kỹ thuật xã hội ngày càng trở nên phổ biến. Điều này tạo ra những thách thức về quản trị và trách nhiệm, cũng như những vấn đề trong nhiều lĩnh vực khác nhau. Do đó, việc làm quen cho các nhà ra quyết định và các nhà nghiên cứu về cốt lõi của sự hợp tác giữa con người và máy móc là rất quan trọng. Nghiên cứu này giới thiệu một phân loại cho phép xác định bản chất của sự tương tác giữa con người và máy móc. Một cuộc tổng quan tài liệu đã chỉ ra rằng tự động hóa và tính tự chủ kỹ thuật là các tham số chính để mô tả và hiểu sự tương tác này. Cả hai khía cạnh này cần được đánh giá cẩn thận, vì sự gia tăng của chúng có thể có những hậu quả sâu rộng. Do đó, hai khái niệm này cấu thành các trục của phân loại. Năm cấp độ tự động hóa và năm cấp độ tính tự chủ kỹ thuật được giới thiệu phía dưới, dựa trên giả định rằng cả tự động hóa và tính tự chủ đều mang tính chất dần dần. Các cấp độ tự động hóa được phát triển từ các phương pháp hiện có; các cấp độ tự chủ được suy ra cẩn thận từ một cuộc tổng quan văn liệu. Việc sử dụng phân loại cũng được giải thích, cũng như những hạn chế và hướng đi cho nghiên cứu tiếp theo.

Systematic research in the wide field of practical knowledge is a recent phenomenon. In this paper, the approaches which have been developed in the main centres of research into practical knowledge in Norway and Sweden are compared with an emphasis on their potential for revitalizing the study of ethics. The focus on narratives and reflection based on the researcher’s own professional experience which is the distinguishing feature of the centre for practical knowledge at the University of Nordland is seen as a very promising addition to the traditional repertory of ethical studies.

AI systems play an increasingly important role in shaping and regulating the lives of millions of human beings across the world. Calls for greater transparency from such systems have been widespread. However, there is considerable ambiguity concerning what “transparency” actually means, and therefore, what greater transparency might entail. While, according to some debates, transparency requires seeing through the artefact or device, widespread calls for transparency imply seeing into different aspects of AI systems. These two notions are in apparent tension with each other, and they are present in two lively but largely disconnected debates. In this paper, we aim to further analyse what these calls for transparency entail, and in so doing, clarify the sorts of transparency that we should want from AI systems. We do so by offering a taxonomy that classifies different notions of transparency. After a careful exploration of the different varieties of transparency, we show how this taxonomy can help us to navigate various domains of human–technology interactions, and more usefully discuss the relationship between technological transparency and human agency. We conclude by arguing that all of these different notions of transparency should be taken into account when designing more ethically adequate AI systems.