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How does algorithmic information processing affect the thoughts and behavior of artificial intelligence (AI) users? In this study, the authors address this question by focusing on algorithm-based chatbots and examine the influence of culture on algorithms as a form of digital intermediation.

The authors conducted a study comparing the United States (US) and Japan to examine how users in the two countries perceive the features of chatbot services and how the perceived features affect user trust and emotion.

Clear differences emerged after comparing algorithmic information processes involved in using and interacting with chatbots. Major attitudes toward chatbots are similar between the two cultures, although the weights placed on qualities differ. Japanese users put more weight on the functional qualities of chatbots, and US users place greater emphasis on non-functional qualities of algorithms in chatbots. US users appear more likely to anthropomorphize and accept explanations of algorithmic features than Japanese users.

Different patterns of chatbot news adoption reveal that the acceptance of chatbots involves a cultural dimension as the algorithms reflect the values and interests of their constituencies. How users perceive chatbots and how they consume and interact with the chatbots depends on the cultural context in which the experience is situated.

A comparative juxtaposition of cultural-algorithmic interactions offers a useful way to examine how cultural values influence user behaviors and identify factors that influence attitude and user acceptance. Results imply that chatbots can be a cultural artifact, and chatbot journalism (CJ) can be a socially contextualized practice that is driven by the user's input and behavior, which are reflections of cultural values and practices.

The purpose of this paper is to propose that in order to tackle the question of bias in algorithms, a systemic, sociotechnical and holistic perspective is needed. With reference to the term “algorithmic culture,” the interconnectedness and mutual shaping of society and technology are postulated. A sociotechnical approach requires translational work between and across disciplines. This conceptual paper undertakes such translational work. It exemplifies how gender and diversity studies, by bringing in expertise on addressing bias and structural inequalities, provide a crucial source for analyzing and mitigating bias in algorithmic systems.

After introducing the sociotechnical context, an overview is provided regarding the contemporary discourse around bias in algorithms, debates around algorithmic culture, knowledge production and bias identification as well as common solutions. The key concepts of gender studies (situated knowledges and strong objectivity) and concrete examples of gender bias then serve as a backdrop for revisiting contemporary debates.

The key concepts reframe the discourse on bias and concepts such as algorithmic fairness and transparency by contextualizing and situating them. The paper includes specific suggestions for researchers and practitioners on how to account for social inequalities in the design of algorithmic systems.

A systemic, gender-informed approach for addressing the issue is provided, and a concrete, applicable methodology toward a situated understanding of algorithmic bias is laid out, providing an important contribution for an urgent multidisciplinary dialogue.

The purpose of this paper is to find a novel optimization selection method for hyper‐parameter of support vector classification (SVC), responsible for the classification of datasets from the UCI machine learning database repository.

A novel two‐stage optimization selection method for hyper‐parameters is proposed. It makes use of explicit information derived from issues and implicit knowledge extracted from the evolution process so as to improve the performance of classifier. In the first stage, the search extent of each hyper‐parameter is determined according to the requirements of issues. In the second stage, optimal hyper‐parameters are obtained by adaptive chaotic culture algorithm in the above search extent. Adaptive chaotic cultural algorithm uses implicit knowledge extracted from the evolution process to control mutation scale of chaotic mutation operator. This algorithm can ensure the diversity of population and exploitation in the latter evolution.

The rationality of the above optimization selection method is proved by the binary classification problem. Final confirmation of this approach is the classification results compared with other methods.

This optimization selection method can effectively avoid premature convergence and lead to better computation stability and precision. It is not related on the structure of functions. SVC model corresponding to optimal hyper‐parameters by this method has better generalization.

This study aims to present a method for the conceptual design and simulation of an aircraft flight control system.

The design methodology is based on particle swarm optimization (PSO). PSO can be used to improve the performance of conventional controllers. The aim of the present study is threefold. First, it attempts to detect and isolate faults in an aircraft model. Second, it is to design a proportional (P) controller, a proportional derivative (PD) controller, a proportional-integral (PI) controller and a fuzzy controller for an aircraft model. Third, it is to design a PD controller for an aircraft using a PSO algorithm.

Conventional controllers, an intelligent controller and a PD controller-based PSO were investigated for flight control. It was seen that the P controller, the PI controller and the PD controller-based PSO caused overshoot. These overshoots were 18.5, 87.7 and 2.6 per cent, respectively. Overshoot was not seen using the PD controller or fuzzy controller. Steady state errors were almost zero for all controllers. The PD controller had the best settling time. The fuzzy controller was second best. The PD controller-based PSO was the third best, but the result was close to the others.

This study shows the implementation of the present algorithm for a specified space mission and also for study regarding variation of performance parameters. This study shows fault detection and isolation procedures and also controller gain choice for a flight control system. A comparison between conventional controllers and PD-based PSO controllers is presented. In this study, sensor fault detection and isolation are carried out, and, also, root locus, time domain analysis and Routh–Hurwitz methods are used to find the conventional controller gains which differ from other studies. A fuzzy controller is created by the trial and error method. Integral of squared time multiplied by squared error is used as a performance function type in PSO.

The purpose of this paper is to survey the automobile insurance fraud detection literature in the past 31 years (1990–2021) and present a research agenda that addresses the challenges and opportunities artificial intelligence and machine learning bring to car insurance fraud detection.

Content analysis methodology is used to analyze 46 peer-reviewed academic papers from 31 journals plus eight conference proceedings to identify their research themes and detect trends and changes in the automobile insurance fraud detection literature according to content characteristics.

This study found that automobile insurance fraud detection is going through a transformation, where traditional statistics-based detection methods are replaced by data mining- and artificial intelligence-based approaches. In this study, it was also noticed that cost-sensitive and hybrid approaches are the up-and-coming avenues for further research.

This paper’s findings not only highlight the rise and benefits of data mining- and artificial intelligence-based automobile insurance fraud detection but also highlight the deficiencies observable in this field such as the lack of cost-sensitive approaches or the absence of reliable data sets.

This paper offers greater insight into how artificial intelligence and data mining challenges traditional automobile insurance fraud detection models and addresses the need to develop new cost-sensitive fraud detection methods that identify new real-world data sets.

The purpose of this paper is to develop a flexible design-oriented development cost method for commercial aviation aircraft based on small sample and poor information.

To predict the development cost of commercial aviation aircraft accurately, the methodology is based on the collected cost data and actual technical, and then the cost prediction relationships derived from an exhaustive statistical and filtered from regression analysis are incorporated. A series of regression equations with high regression coefficient are yielded after the cost driving factors of the development cost are fixed. Next, several sets of equations with high regression coefficient are selected for final integration. It is a flexible method that can be used efficiently to predict the cost of commercial aviation aircraft.

The development of commercial aviation aircraft has relatively a late start and no cost prediction model has been suitable for small sample, the proposed method is expected and is rather desirable.

By comparing the approach with the ordinary regression model and back propagation (BP) neural network, the scheme in this work is more efficient and convenient.

The results obtained in this paper show that the proposed method not only has a certain degree of versatility, but also can provide a preliminary prediction of the development cost of commercial aviation aircraft.

The purpose of this study is to offer a roadmap for work on the ethical and societal implications of algorithms and AI. Based on an analysis of the social, technical and regulatory challenges posed by algorithmic systems in Korea, this work conducts socioecological evaluations of the governance of algorithmic transparency and accountability.

This paper analyzes algorithm design and development from critical socioecological angles: social, technological, cultural and industrial phenomena that represent the strategic interaction among people, technology and society, touching on sensitive issues of a legal, a cultural and an ethical nature.

Algorithm technologies are a part of a social ecosystem, and its development should be based on user interests and rights within a social and cultural milieu. An algorithm represents an interrelated, multilayered ecosystem of networks, protocols, applications, services, practices and users.

Value-sensitive algorithm design is proposed as a novel approach for designing algorithms. As algorithms have become a constitutive technology that shapes human life, it is essential to be aware of the value-ladenness of algorithm development. Human values and social issues can be reflected in an algorithm design.

The arguments in this study help ensure the legitimacy and effectiveness of algorithms. This study provides insight into the challenges and opportunities of algorithms through the lens of a socioecological analysis: political discourse, social dynamics and technological choices inherent in the development of algorithm-based ecology.

The purpose of this paper is to introduce and examine algorithmic culture and consider the implications of algorithms for information literacy practice. The questions for information literacy scholars and educators are how can one understand the impact of algorithms on agency and performativity, and how can one address and plan for it in their educational and instructional practices?

In this study, algorithmic culture and implications for information literacy are conceptualised from a sociocultural perspective.

To understand the multiplicity and entanglement of algorithmic culture in everyday lives requires information literacy practice that encourages deeper examination of the relationship among the epistemic views, practical usages and performative consequences of algorithmic culture. Without trying to conflate the role of the information sciences, this approach opens new avenues of research, teaching and more focused attention on information literacy as a sustainable practice.

The concept of algorithmic culture is introduced and explored in relation to information literacy and its literacies.

The purpose of this paper is to introduce the notion of a social fabric (SF) in which the expression of knowledge sources (KS) in cultural algorithms (CA) can be distributed through the population. The SF influence function is applied to the solution of selected complex engineering problems and it is shown that different parameter combinations for the SF influence function can affect the rate of solution. This enhanced approach is compared with previous approaches.

KS are allowed to influence individuals through a network. From a theoretical perspective, individuals in the real world are viewed as participating in a variety of different networks. Several layers of such networks can be supported within a population. The interplay of these various network computations is designated as the “social fabric.” Using this new influence function, when an individual is to be modified, one KS is selected to perform the modification at each generation. The selection process is done via weaving the SF, hence changing the number of individuals that follow a certain KS.

Simulation experiments show that the choice of influence function has a great impact on the problem‐solving phase. For some problems, a social network is not necessary to produce frequent convergence to an optimum. On the other hand, it is observed that the social network can help to focus search by allowing a KS to influence groups of individuals within a network rather than single unrelated individuals. The new approach shows a more focused convergence to optimal values in complex engineering problems with numerous constraints. Also, it is suggested that a SF configuration can be robust in the sense that a configuration that works well for one problem can also perform well in a more complex but unrelated problem. This suggests that a configuration can be evolved to solve suites of problems.

The introduced approach is interesting for the optimization of problems of a non‐linear complex nature.