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Reviews have long been recognized as among the most important forms of scientific communication. The rapid growth of the primary literature has further increased the need for reviews to distill and interpret the literature. This review on Reviews and Reviewing: Approaches to Research Synthesis encompasses the evolution of the review literature, taxonomy of review literature, uses and users of reviews, the process of preparing reviews, assessment of review quality and impact, the impact of information technology on the preparation of reviews, and research opportunities for information science related to reviews and reviewing. In addition to providing a synthesis of prior research, this review seeks to identify gaps in the published research and to suggest possible future research directions. [ABSTRACT FROM AUTHOR]

Artificially intelligent systems (AISs) are being created by software developing companies (SDCs) to influence clinical decision-making. Historically, clinicians have led healthcare decision-making, and the introduction of AISs makes SDCs novel actors in the clinical decision-making space. Although these AISs are intended to influence a clinician's decision-making, SDCs have been clear that clinicians are in fact the final decision-makers in clinical care, and that AISs can only inform their decisions. As such, the default position is that clinicians should hold responsibility for the outcomes of the use of AISs. This is not the case when an AIS has influenced a clinician's judgement and their subsequent decision. In this paper, we argue that this is an imbalanced and unjust position, and that careful thought needs to go into how personal moral responsibility for the use of AISs in clinical decision-making should be attributed. This paper employs and examines the difference between prospective and retrospective responsibility and considers foreseeability as key in determining how personal moral responsibility can be justly attributed. This leads us to the view that moral responsibility for the outcomes of using AISs in healthcare ought to be shared by the clinical users and SDCs., (© 2023 The Authors. Bioethics published by John Wiley & Sons Ltd.)

With the continuous growth of electricity demand, the safe and stable operation of distribution lines is crucial for power transportation. Unmanned aerial vehicle (UAV) inspection has been widely used for the maintenance and repair of distribution lines. Due to the limitations of computational power and endurance, it is difficult for UAVs to independently complete data processing. Combined with mobile edge computing (MEC), this paper proposes a computing offloading strategy based on multi‐agent reinforcement learning and double‐layer offloading mechanism, which can further utilize the computing power of non‐task devices and edge servers. Firstly, three‐layer system architecture, named MEC‐U‐NTDC (MEC‐UAV‐Non‐task Device Cloud), is built. Secondly, double‐layer offloading mechanism is designed to comprehensively utilize the computing power of edge servers and neighbouring non‐task devices. Finally, a multi‐agent algorithm DLMQMIX is proposed to minimize the total cost for UAV inspection. Simulation experiments show that the proposed algorithm can effectively solve the task offloading problem of UAV‐aided distribution line inspection, and compared with algorithms such as PSO, GA, and QMIX, it performs better in terms of average delay, system cost, and load balancing, achieving a smaller total system cost. [ABSTRACT FROM AUTHOR]

The quality of management decisions is one of the main issues facing higher education institutions today. Strategic decisions taken by the higher education institutions affect policies, schemes, and actions that the institutions are considering. Machine learning is an emergent artificial intelligence field that utilizes different algorithms, analyses data, and delivers a better understanding of the data contained in a specific context. Hence, in this paper, data‐driven decision‐making model has been proposed based on artificial intelligence in colleges and universities. Student data, graduation rate and curriculum design have been analysed for administrative decision‐making in college or university based on the machine learning method. With the availability of huge quantities and high‐quality input training data, machine‐learning progressions can attain precise outcomes and enable informed decision‐making. The experimental findings show that the suggested model improves the outcome ratio of 90.72%, the performance ratio of 97.62%, prediction ratio of 96.35%, decision‐making level of 95.51%, accuracy ratio of 95.61%, an efficiency ratio of 98.14%, graduation rate of 85.86%, data security rate (95.61%) and error rate 33.21% compared to other methods. [ABSTRACT FROM AUTHOR]

Summary: Artificial Intelligence (AI) based cyber threat detection tools are widely used to process and analyze a large amount of data for improved intrusion detection performance. However, these models are often considered as black box by the cybersecurity experts due to their inability to comprehend or interpret the reasoning behind the decisions. Moreover, AI‐based threat hunting is data‐driven and is usually modeled using the data provided by multiple cloud vendors. This is another critical challenge, as a malicious cloud can provide false information (i.e., insider attacks) and can degrade the threat‐hunting capability. In this paper, we present a blockchain‐enabled eXplainable AI (XAI) for enhancing the decision‐making capability of cyber threat detection in the context of Smart Healthcare Systems. Specifically, first, we use blockchain to validate and store data between multiple cloud vendors by implementing a Clique Proof‐of‐Authority (C‐PoA) consensus. Second, a novel deep learning‐based threat‐hunting model is built by combining Parallel Stacked Long Short Term Memory (PSLSTM) networks with a multi‐head attention mechanism for improved attack detection. The extensive experiment confirms its potential to be used as an enhanced decision support system by cybersecurity analysts. [ABSTRACT FROM AUTHOR]

In agriculture, important unanswered questions about machine learning and artificial intelligence (ML/AI) include will ML/AI change how food is produced and will ML algorithms replace or partially replace farmers in the decision process. As ML/AI technologies become more accurate, they have the potential to improve profitability while reducing the impact of agriculture on the environment. However, despite these benefits, there are many adoption barriers including cost, and that farmers may be reluctant to adopt a decision tool they do not understand. The goal of this special issue is to discuss cutting‐edge research on the use of ML/AI technologies in agriculture, barriers to the adoption of these technologies, and how technologies can affect our current workforce. The papers are separated into three sections: Machine Learning within Crops, Pasture, and Irrigation; Machine Learning in Predicting Crop Disease; and Society and Policy of Machine Learning. Core Ideas: There are concerns that decision‐making tools using machine learning and artificial intelligence (ML/AI) in farming could replace human knowledge and labor.ML/AI technologies have the potential to improve precision farming using site‐specific algorithms.This special issue aims to open a dialogue about the role of ML/AI in agriculture and identify inequalities that may result from its use. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) based clinical decision support systems (CDSS) are becoming ever more widespread in healthcare and could play an important role in diagnostic and treatment processes. For this reason, AI‐based CDSS has an impact on the doctor–patient relationship, shaping their decisions with its suggestions. We may be on the verge of a paradigm shift, where the doctor–patient relationship is no longer a dual relationship, but a triad. This paper analyses the role of AI‐based CDSS for shared decision‐making to better comprehend its promises and associated ethical issues. Moreover, it investigates how certain AI implementations may instead foster the inappropriate paradigm of paternalism. Understanding how AI relates to doctors and influences doctor–patient communication is essential to promote more ethical medical practice. Both doctors' and patients' autonomy need to be considered in the light of AI. [ABSTRACT FROM AUTHOR]

This paper puts forth the foundation for a different type of negotiation that reflects in a more realistic way the behavior of human beings when making complex decisions, aligned with the cognitive process involved. The positive and the negative aspects of decisions reflect two components in the human brain: the nucleus accumbens and the amygdala. The first component deals with behavior related to rewards or potential gains, and the other with behavior related to risk aversion or potential losses. Negotiations require communication, learning, accommodation of positions, and development of alternatives and modification of constraints. Negotiation support systems help and advice negotiators; structure and analyze the problem; elicit preferences to construct a preference function; visualize different aspects of the problem and the process; and facilitate communication and learning. The methodology used is based on the Analytic Hierarchy Process previously employed in a retributive conflict in which each party calculates the incremental benefits it gets and the costs to its opponent. The Israeli–Palestinian conflict is used to show the viability of the approach and the type of inputs we need to study conflicts with this approach. The negotiation platform that we describe here help the actors to start and engage a negotiation from noncooperative parties. It can also be used in human– negotiations to expedite reaching an agreement if one exists. However, we are not seeking automated negotiations. Advantages and risks of Artificial Intelligence contribution in negotiation support systems for cognitive and retributive conflict resolution based on AHP (CRCR‐AHP) are also discussed. [ABSTRACT FROM AUTHOR]

To support artificial intelligence (AI)‐involved tasks offloaded from the mobile devices (MDs), it is necessary to equip the Unmanned Aerial Vehicle (UAV) with custom‐made co‐processor (CP) for handling AI workloads in multi‐UAV‐empowered Edge Intelligence. Existing CPU‐oriented task scheduling algorithm cannot apply to the CPU+CP heterogeneous architecture. In this backdrop, this paper first formulates the joint service function placement, collaborative task scheduling, UAV deployment, and MD position determination problem as a Mixed Integer Non‐Linear Programming problem. Then, an alternating optimization‐based algorithm is put forward to derive a sub‐optimal solution of the problem utilizing Differential Evolution and Greedy‐based Hungarian algorithms. A series of experiments are conducted to evaluate the performance of the proposal. Results show that authors' proposal can achieve an overall revenue that is roughly 50% higher than those of existing methods. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is increasingly being used in decision making across various industries, including the public health arena. Bias in any decision‐making process can significantly skew outcomes, and AI systems have been shown to exhibit biases at times. The potential for AI systems to perpetuate and even amplify biases is a growing concern. Bias, as used in this paper, refers to the tendency toward a particular characteristic or behavior, and thus, a biased AI system is one that shows biased associations entities. In this literature review, we examine the current state of research on AI bias, including its sources, as well as the methods for measuring, benchmarking, and mitigating it. We also examine the biases and methods of mitigation specifically relevant to the healthcare field and offer a perspective on bias measurement and mitigation in regulatory science decision making. [ABSTRACT FROM AUTHOR]

Research suggests that algorithms—based on artificial intelligence or linear regression models—make better predictions than humans in a wide range of domains. Several studies have examined the degree to which people use algorithms. However, these studies have been mostly cross‐sectional and thus have failed to address the dynamic nature of algorithm use. In the present paper, we examined algorithm use with a novel longitudinal approach outside the lab. Specifically, we conducted two ecological momentary assessment studies in which 401 participants made financial predictions for 18 days in two tasks. Relying on the judge‐advisor system framework, we examined how time interacted with advice source (human vs. algorithm) and advisor accuracy to predict advice taking. Our results showed that when the advice was inaccurate, people tended to use algorithm advice less than human advice across the period studied. Inaccurate algorithms were penalized logarithmically; the effect was initially strong but tended to fade over time. This suggests that first impressions are crucial and produce significant changes in advice taking at the beginning of the interaction, which later tends to stabilize as days go by. Therefore, inaccurate algorithms are more likely to accrue a negative reputation than inaccurate humans, even when having the same level of performance. [ABSTRACT FROM AUTHOR]

Prior research in judgment and decision making (JDM) has investigated the effect of problem framing on human preferences. Furthermore, research in JDM documented the absence of such reversal of preferences when making decisions from experience. However, little is known about the effect of context on preferences under the combined influence of problem framing and problem format. Also, little is known about how cognitive models would account for human choices in different problem frames and types (general/specific) in the experience format. One of the primary objectives of this research is to investigate the presence of preference reversals under the influence of problem framing (gain/loss), problem format (experience/description), and problem type (general/specific). Another objective of this research is to develop cognitive models to account for human choices across different problem frames and types in the experience format. A total of 320 participants from India were randomly assigned to one of eight between‐subjects conditions that differed in problem frame, format, and type. Results revealed preference reversals in the description condition; however, they were absent in the experience condition. Moreover, preference reversals were less pronounced in the general problem framing compared to the specific problem framing. Furthermore, specific problems influenced risk‐seeking behavior among participants. We developed cognitive and heuristics models using instance‐based learning theory and natural mean heuristic. Results reveal models' dependency on recent and frequent observations during information sampling. These experience‐based cognitive models could help build artificial intelligence models with fewer preference reversals. This paper focuses on investigating the presence of preference reversals under the influence of problem framing (gain/loss), problem format (experience/description), and problem type (general/specific). Furthermore, Uttrani et al. develop cognitive models using instance‐based learning theory and natural mean heuristic to account for human choices across different problem frames and types in the experience format. [ABSTRACT FROM AUTHOR]

To date, many AI initiatives (eg, AI4K12, CS for All) developed standards and frameworks as guidance for educators to create accessible and engaging Artificial Intelligence (AI) learning experiences for K‐12 students. These efforts revealed a significant need to prepare youth to gain a fundamental understanding of how intelligence is created, applied, and its potential to perpetuate bias and unfairness. This study contributes to the growing interest in K‐12 AI education by examining student learning of modelling real‐world text data. Four students from an Advanced Placement computer science classroom at a public high school participated in this study. Our qualitative analysis reveals that the students developed nuanced and in‐depth understandings of how text classification models—a type of AI application—are trained. Specifically, we found that in modelling texts, students: (1) drew on their social experiences and cultural knowledge to create predictive features, (2) engineered predictive features to address model errors, (3) described model learning patterns from training data and (4) reasoned about noisy features when comparing models. This study contributes to an initial understanding of student learning of modelling unstructured data and offers implications for scaffolding in‐depth reasoning about model decision making. Practitioner notes: What is already known about this topic Scholarly attention has turned to examining Artificial Intelligence (AI) literacy in K‐12 to help students understand the working mechanism of AI technologies and critically evaluate automated decisions made by computer models.While efforts have been made to engage students in understanding AI through building machine learning models with data, few of them go in‐depth into teaching and learning of feature engineering, a critical concept in modelling data.There is a need for research to examine students' data modelling processes, particularly in the little‐researched realm of unstructured data. What this paper adds Results show that students developed nuanced understandings of models learning patterns in data for automated decision making.Results demonstrate that students drew on prior experience and knowledge in creating features from unstructured data in the learning task of building text classification models.Students needed support in performing feature engineering practices, reasoning about noisy features and exploring features in rich social contexts that the data set is situated in. Implications for practice and/or policy It is important for schools to provide hands‐on model building experiences for students to understand and evaluate automated decisions from AI technologies.Students should be empowered to draw on their cultural and social backgrounds as they create models and evaluate data sources.To extend this work, educators should consider opportunities to integrate AI learning in other disciplinary subjects (ie, outside of computer science classes). [ABSTRACT FROM AUTHOR]

Our research reveals the continued and evolving role of the human factor in decision making in digitalized retail supply chains. We compare managerial roles in a pre‐ and post‐COVID era through conducting in‐depth interviews of 25 executives spanning the retail supply chain ecosystem. We use grounded theory to develop four main contributions. First, we find that the involvement of managerial judgment is found to be progressively greater moving up the retail supply chain, away from the customer and the demand signal. Second, integration of analytics and judgment is now the primary method of decision making, and we identify elements needed for success. Third, we develop an essential framework for a successful integration process. Fourth, we isolate the necessary components of a successful process for analytics/artificial intelligence (AI) implementation. Our paper offers important insights into how analytics and AI are—and should be used—in judgment and decision making and opportunities for researchers to understand the changing role of the human factor in digitalized retail supply chains. [ABSTRACT FROM AUTHOR]

Multi‐agent reinforcement learning relies on reward signals to guide the policy networks of individual agents. However, in high‐dimensional continuous spaces, the non‐stationary environment can provide outdated experiences that hinder convergence, resulting in ineffective training performance for multi‐agent systems. To tackle this issue, a novel reinforcement learning scheme, Mutual Information Oriented Deep Skill Chaining (MioDSC), is proposed that generates an optimised cooperative policy by incorporating intrinsic rewards based on mutual information to improve exploration efficiency. These rewards encourage agents to diversify their learning process by engaging in actions that increase the mutual information between their actions and the environment state. In addition, MioDSC can generate cooperative policies using the options framework, allowing agents to learn and reuse complex action sequences and accelerating the convergence speed of multi‐agent learning. MioDSC was evaluated in the multi‐agent particle environment and the StarCraft multi‐agent challenge at varying difficulty levels. The experimental results demonstrate that MioDSC outperforms state‐of‐the‐art methods and is robust across various multi‐agent system tasks with high stability. [ABSTRACT FROM AUTHOR]

Mahjong, a complex game with hidden information and sparse rewards, poses significant challenges. Existing Mahjong AIs require substantial hardware resources and extensive datasets to enhance AI capabilities. The authors propose a transformer‐based Mahjong AI (Tjong) via hierarchical decision‐making. By utilising self‐attention mechanisms, Tjong effectively captures tile patterns and game dynamics, and it decouples the decision process into two distinct stages: action decision and tile decision. This design reduces decision complexity considerably. Additionally, a fan backward technique is proposed to address the sparse rewards by allocating reversed rewards for actions based on winning hands. Tjong consists of 15M parameters and is trained using approximately 0.5 M data over 7 days of supervised learning on a single server with 2 GPUs. The action decision achieved an accuracy of 94.63%, while the claim decision attained 98.55% and the discard decision reached 81.51%. In a tournament format, Tjong outperformed AIs (CNN, MLP, RNN, ResNet, VIT), achieving scores up to 230% higher than its opponents. Furthermore, after 3 days of reinforcement learning training, it ranked within the top 1% on the leaderboard on the Botzone platform. [ABSTRACT FROM AUTHOR]

Surgical robot systems (SRS) represent an innovative cross‐disciplinary research field using robotic technology to assist surgeons in operations. Current bottlenecks in SRS, such as the limited ability to process complex information and make surgical decisions, have not been effectively solved. Artificial intelligence (AI) is a valuable technique for simulating and extending human intelligence. AI offers a new direction and impetus for SRS by enhancing performance in areas such as perception, navigation, surgical planning, and control strategies. This review introduces the developmental history of AI‐aided SRS, summarizes the basic SRS architecture, and analyzes how AI can improve SRS performance. Classical cases of AI‐aided SRS, the impact of evidence in clinical settings, and associated ethical and legal considerations are explored. Finally, the challenges in AI‐aided SRS are discussed, including algorithm development, surgical data science, human–robot coordination, and trust building between humans and robots. [ABSTRACT FROM AUTHOR]

Nowadays, cybersecurity is an emerging research area in the automotive industry, and it is investigated by many different perspectives. Our article is a review of existing vehicular security solutions that covers the state‐of‐the‐art and future research directions. This article is a new contribution to tutorials/surveys related to the vehicular cybersecurity domain with the latest details. We developed a database from 140 articles from the field of automotive security. In the database, we assigned specific attributes to every article (such as Web of Science Impact Factor or the number of citations). The data set was analyzed by the K‐means clustering and decision tree analysis methods to identify and characterize the generated groups of papers. Following this, the article highlights the research areas that might receive more attention in the future. Accordingly, the result of the current research can be applied by the decision‐makers, researchers, and Original Equipment Manufacturers to allocate additional resources to those domains, which is expected to shape the future of vehicular security. [ABSTRACT FROM AUTHOR]

New developments in technology have increased the computing power available for problem solving and have made technology and algorithms much more accessible by providing new ways of communicating with models and tools. Supporting decision makers in complex problems involving multiple stakeholders, multiple criteria, uncertainty and risk or other factors has been an important goal of Operations Research since the field emerged. [Extracted from the article]

To fully utilize the voltage regulation capacity and interaction characteristics of the Transmission and Distribution (T&D) system, a novel Modified Partially Observed Markov Decision Process (MPOMDP)‐based Reinforcement Learning (RL) scheme for Autonomous Voltage Control is proposed, which incorporates Demand Response (DR) and cooperation with the Transmission Network. The proposed scheme consists of two vital components: an MPOMDP block and a Modified Asynchronous Advantage Actor‐Critic‐based RL block. The MPOMDP block innovatively exploits the confidence interval of the observed state to make a better perception of the precise system state by introducing two new probability vectors. Then the MPOMDP block is fed into the underlying architecture of the RL block for asynchronously capturing features and optimal decision‐making, where the solving framework additionally brings in a public data buffer to realize boundary information sharing. Case studies are conducted on a modified T&D system considering N‐1 contingencies, with a training dataset from a district in Suzhou, China. Simulation results demonstrate that the proposed scheme can achieve significant voltage optimization while ensuring fast convergence speed. [ABSTRACT FROM AUTHOR]

The incentive mechanism of federated learning has been a hot topic, but little research has been done on the compensation of privacy loss. To this end, this study uses the Local SGD federal learning framework and gives a theoretical analysis under the use of differential privacy protection. Based on the analysis, a multi‐attribute reverse auction model is proposed to be used for user selection as well as payment calculation for participation in federal learning. The model uses a mixture of economic and non‐economic attributes in making choices for users and is transformed into an optimisation equation to solve the user choice problem. In addition, a post‐auction negotiation model that uses the Rubinstein bargaining model as well as optimisation equations to describe the negotiation process and theoretically demonstrate the improvement of social welfare is proposed. In the experimental part, the authors find that their algorithm improves both the model accuracy and the F1‐score values relative to the comparison algorithms to varying degrees. [ABSTRACT FROM AUTHOR]

Purpose: We aimed to evaluate the usability of a smartphone application consisting of standardized nursing language (SNL) using NANDA, Nursing Intervention Classification, and Nursing Outcome Classification for nursing home nurses. Data sources: Applying convenience sampling, a total of 14 experts and 15 real users were invited to test and evaluate the smartphone application independently. For the usability evaluation of the developed application, the Korean version of the Mobile Application Rating Scale for experts and Mobile Application Rating Scale: User Version developed by Stoyanov et al. were used. Data synthesis: Both groups determined that the SNL application was quite informative about SNL and efficient function; however, the engagement was quite lower than other categories. Conclusions: Although SNLs were scientifically developed for several decades, the widely available technological application for registered nurses in different languages is urgently needed to improve quality of nursing care. Implications for nursing practice: The identified problems and recommendations by users and experts using heuristic evaluation will be reflected in the application's final version to be used for research. [ABSTRACT FROM AUTHOR]

Regulators are faced with many challenges surrounding health data usage, including privacy, fragmentation, validity, and generalizability, especially in the European Union, for which synthetic data may provide innovative solutions. Synthetic data, defined as data artificially generated rather than captured in the real world, are increasingly being used for healthcare research purposes as a proxy to real‐world data (RWD). Currently, there are barriers particularly challenging in Europe, where sharing patient's data is strictly regulated, costly, and time‐consuming, causing delays in evidence generation and regulatory approvals. Recent initiatives are encouraging the use of synthetic data in regulatory decision making and health technology assessment to overcome these challenges, but synthetic data have still to overcome realistic obstacles before their adoption by researchers and regulators in Europe. Thus, the emerging use of RWD and synthetic data by pharmaceutical and medical device industries calls regulatory bodies to provide a framework for proper evidence generation and informed regulatory decision making. As the provision of data becomes more ubiquitous in scientific research, so will innovations in artificial intelligence, machine learning, and generation of synthetic data, making the exploration and intricacies of this topic all the more important and timely. In this review, we discuss the potential merits and challenges of synthetic data in the context of decision making in the European regulatory environment. We explore the current uses of synthetic data and ongoing initiatives, the value of synthetic data for regulatory purposes, and realistic barriers to the adoption of synthetic data in healthcare. [ABSTRACT FROM AUTHOR]

This article presents approaches used to solve the problem of selecting AI technologies and tools to obtain the creativity fostering functionalities of an innovative knowledge platform. The aforementioned selection problem has been lagging behind other software‐specific aspects of online knowledge platform and learning platform development so far. We linked technological recommendations from group decision support exercises to the platform design aims and constraints using an expert Delphi survey and multicriteria analysis methods. The links between expected advantages of using selected AI building tools, AI‐related system functionalities, and their ongoing relevance until 2030 were assessed and used to optimize the learning scenarios and in planning the future development of the platform. The selected technologies allowed the platform management to implement the desired functionalities, thus harnessing the potential of open innovation platforms more effectively and delivering a model for the development of a relevant class of advanced open‐access knowledge provision systems. Additionally, our approach is an essential part of digital sustainability and AI‐alignment strategy for the aforementioned class of systems. The knowledge platform, which serves as a case study for our methodology has been developed within an EU Horizon 2020 research project. [ABSTRACT FROM AUTHOR]

Research in moral psychology has found that robots, more than humans, are expected to make utilitarian decisions. This expectation is found specifically when contrasting utilitarian action to deontological inaction. In a series of eight experiments (total N = 3752), we compared judgments about robots' and humans' decisions in a rescue dilemma with no possibility of deontological inaction. A robot's decision to rescue an innocent victim of an accident was judged more positively than the decision to rescue two people culpable for the accident (Studies 1–2b). This pattern repeated in a large‐scale web survey (Study 3, N = ∼19,000) and reversed when all victims were equally culpable/innocent (Study 5). Differences in judgments about humans' and robots' decisions were largest for norm‐violating decisions. In sum, robots are not always expected to make utilitarian decisions, and their decisions are judged differently from those of humans based on other moral standards as well. [ABSTRACT FROM AUTHOR]

Objectives: This study aimed to develop a diagnostic support tool using pretrained models for classifying panoramic images of the temporomandibular joint (TMJ) into normal and osteoarthritis (OA) cases. Subjects and Methods: A total of 858 panoramic images of the TMJ (395 normal and 463 TMJ‐OA) were obtained from 518 individuals from January 2015 to December 2018. The data were randomly divided into training, validation, and testing sets (6:2:2). We used pretrained Resnet152 and EfficientNet‐B7 as transfer learning models. The accuracy, specificity, sensitivity, area under the curve, and gradient‐weighted class activation mapping (grad‐CAM) of both trained models were evaluated. The performances of the trained models were compared to that of dentists (both TMD specialists and general dentists). Results: The classification accuracies of ResNet‐152 and EfficientNet‐B7 were 0.87 and 0.88, respectively. The trained models exhibited the highest accuracy in OA classification. In the grad‐CAM analysis, the trained models focused on specific areas in osteoarthritis images where erosion or osteophyte were observed. Conclusions: The artificial intelligence model improved the diagnostic power of TMJ‐OA when trained with two‐dimensional panoramic condyle images and can be effectively applied by dentists as a screening diagnostic tool for TMJ‐OA. [ABSTRACT FROM AUTHOR]

Algorithms based on Artificial Intelligence technologies are slowly transforming street‐level bureaucracies, yet a lack of algorithmic transparency may jeopardize citizen trust. Based on procedural fairness theory, this article hypothesizes that two core elements of algorithmic transparency (accessibility and explainability) are crucial to strengthening the perceived trustworthiness of street‐level decision‐making. This is tested in one experimental scenario with low discretion (a denied visa application) and one scenario with high discretion (a suspicion of welfare fraud). The results show that: (1) explainability has a more pronounced effect on trust than the accessibility of the algorithm; (2) the effect of algorithmic transparency not only pertains to trust in the algorithm itself but also—partially—to trust in the human decision‐maker; (3) the effects of algorithmic transparency are not robust across decision context. These findings imply that transparency‐as‐accessibility is insufficient to foster citizen trust. Algorithmic explainability must be addressed to maintain and foster trustworthiness algorithmic decision‐making. [ABSTRACT FROM AUTHOR]

Machine learning (ML) and artificial intelligence (AI) have had a profound impact on our lives. Domains like health and learning are naturally helped by human–AI interactions and decision making. In these areas, as ML algorithms prove their value in making important decisions, humans add their distinctive expertise and judgment on social and interpersonal issues that need to be considered in tandem with algorithmic inputs of information. Some questions naturally arise. What rules and regulations should be invoked on the employment of AI, and what protocols should be in place to evaluate available AI resources? What are the forms of effective communication and coordination with AI that best promote effective human–AI teamwork? In this review, we highlight factors that we believe are especially important in assembling and managing human–AI decision making in a group setting. [ABSTRACT FROM AUTHOR]

The honey bee swarm carried out its best‐of‐N nest site computations by operating more than a dozen different information processing loops in parallel and by recruiting more resources to provide greater precision in loops evaluating the better quality sites. The positive feedback effects of recurrent recruitment by means of waggle dance signalling amplified the utilisation of the swarm's energy, memory and carrier resources. The relatively strong negative feedback effects of various attenuation mechanisms tended to reduce resource use and therefore counter‐balanced amplification for long enough for a meaningful nest site survey and quorum decision to be made. Some information processing mechanisms such as exploration tendency, waggle dance signalling, site non‐specific attenuation, noise reduction, independent site evaluation, energy efficient coding, mixed precision processing, self‐organising computation and quorum decision making were found to profoundly influence the efficiency of resource use. Significant insights were also gained into extended cognition, dark data processing, information quality and resource leakage. Finally, the energy cost of acquiring and processing sensory information was estimated. [ABSTRACT FROM AUTHOR]

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In many complex, real‐world situations, problem solving and decision making require effective reasoning about causation and uncertainty. However, human reasoning in these cases is prone to confusion and error. Bayesian networks (BNs) are an artificial intelligence technology that models uncertain situations, supporting better probabilistic and causal reasoning and decision making. However, to date, BN methodologies and software require (but do not include) substantial upfront training, do not provide much guidance on either the model building process or on using the model for reasoning and reporting, and provide no support for building BNs collaboratively. Here, we contribute a detailed description and motivation for our new methodology and application, Bayesian ARgumentation via Delphi (BARD). BARD utilizes BNs and addresses these shortcomings by integrating (1) short, high‐quality e‐courses, tips, and help on demand; (2) a stepwise, iterative, and incremental BN construction process; (3) report templates and an automated explanation tool; and (4) a multiuser web‐based software platform and Delphi‐style social processes. The result is an end‐to‐end online platform, with associated online training, for groups without prior BN expertise to understand and analyze a problem, build a model of its underlying probabilistic causal structure, validate and reason with the causal model, and (optionally) use it to produce a written analytic report. Initial experiments demonstrate that, for suitable problems, BARD aids in reasoning and reporting. Comparing their effect sizes also suggests BARD's BN‐building and collaboration combine beneficially and cumulatively. [ABSTRACT FROM AUTHOR]

An intelligent decision support information system is applied to develop an artificial intelligence (AI)‐driven solution to reduce patient waiting time in a hospital without an appointment system to improve healthcare service quality. The bottlenecks were identified, and then the healthcare service process was reengineered through the proposed shortest‐consultation time (SCT) model to increase the capacity for serving patients and the synchronization between capacity and patient arrival patterns. The study made several meaningful observations: (1) It is feasible to apply an AI‐driven solution to reduce patient waiting time in a hospital with no appointment system to improve healthcare service quality; (2) analytical models are helpful in identifying characteristics of patient flow problems; (3) the implementation of a few performance factors gives most of the improvements, and (4) theory of constraint (TOC) is a method that can be applied to improve patient service qualification. [ABSTRACT FROM AUTHOR]

This study explores the impact of artificial intelligence (AI) on user entrepreneurs, driving factors and user entrepreneurship process combined with its characteristics and development trends. Moreover, this study sorts out the logic of user entrepreneurship change in the AI era. This study achieves four findings. First, AI contributes to entrepreneurs by collecting large user data and intelligently analysing them to obtain optimal entrepreneurial judgements and decisions. Second, entrepreneurs may use AI systems to understand users' potential needs and get user demand information (e.g. more accurate and more major shortcomings of the products). Third, AI assists entrepreneurs in obtaining robust data of product users, including leading and ordinary users, for a wider audience. Fourth, AI replaces the original intergenerational product replacement model with intermittent and periodic characteristics for entrepreneurial activities and reforms the three‐stage user entrepreneurial process: product element deconstruction, product verification matching and innovative product commercialization. This study provides a feasible direction for the key research issues. [ABSTRACT FROM AUTHOR]

This study is the first to provide an integrated view on the body of knowledge of artificial intelligence (AI) published in the marketing, consumer research, and psychology literature. By leveraging a systematic literature review using a data‐driven approach and quantitative methodology (including bibliographic coupling), this study provides an overview of the emerging intellectual structure of AI research in the three bodies of literature examined. We identified eight topical clusters: (1) memory and computational logic; (2) decision making and cognitive processes; (3) neural networks; (4) machine learning and linguistic analysis; (5) social media and text mining; (6) social media content analytics; (7) technology acceptance and adoption; and (8) big data and robots. Furthermore, we identified a total of 412 theoretical lenses used in these studies with the most frequently used being: (1) the unified theory of acceptance and use of technology; (2) game theory; (3) theory of mind; (4) theory of planned behavior; (5) computational theories; (6) behavioral reasoning theory; (7) decision theories; and (8) evolutionary theory. Finally, we propose a research agenda to advance the scholarly debate on AI in the three literatures studied with an emphasis on cross‐fertilization of theories used across fields, and neglected research topics. [ABSTRACT FROM AUTHOR]

Context Clinical supervisors make judgements about how much to trust learners with critical activities in patient care. Such decisions mediate trainees' opportunities for learning and competency development and thus are a critical component of education. As educators apply entrustment frameworks to assessment, it is important to determine how narrative feedback reflecting entrustment may also address learners' educational needs. Methods: In this study, we used artificial intelligence (AI) and natural language processing (NLP) to identify characteristics of feedback tied to supervisors' entrustment decisions during direct observation encounters of clerkship medical students (3328 unique observations). Supervisors conducted observations of students and collaborated with them to complete an entrustment‐based assessment in which they documented narrative feedback and assigned an entrustment rating. We trained a deep neural network (DNN) to predict entrustment levels from the narrative data and developed an explainable AI protocol to uncover the latent thematic features the DNN used to make its prediction. Results: We found that entrustment levels were associated with level of detail (specific steps for performing clinical tasks), feedback type (constructive versus reinforcing) and task type (procedural versus cognitive). In justifying both high and low levels of entrustment, supervisors detailed concrete steps that trainees performed (or did not yet perform) competently. Conclusions: Framing our results in the factors previously identified as influencing entrustment, we find a focus on performance details related to trainees' clinical competency as opposed to nonspecific feedback on trainee qualities. The entrustment framework reflected in feedback appeared to guide specific goal‐setting, combined with details necessary to reach those goals. Our NLP methodology can also serve as a starting point for future work on entrustment and feedback as similar assessment datasets accumulate. Using natural language processing, Gin et al demonstrate how narrative feedback and entrustment decisions are intertwined, providing empirical evidence regarding how formative processes can help trainees achieve competencies. [ABSTRACT FROM AUTHOR]

The goal of continual learning (CL) is to learn a sequence of tasks without suffering from the phenomenon of catastrophic forgetting. Previous work has shown that leveraging memory in the form of a replay buffer can reduce performance degradation on prior tasks. We hypothesize that forgetting can be further reduced when the model is encouraged to remember the evidence for previously made decisions. As a first step towards exploring this hypothesis, we propose a simple novel training paradigm, called Remembering for the Right Reasons (RRR), that additionally stores visual model explanations for each example in the buffer and ensures the model has "the right reasons" for its predictions by encouraging its explanations to remain consistent with those used to make decisions at training time. Without this constraint, there is a drift in explanations and increase in forgetting as conventional continual learning algorithms learn new tasks. We demonstrate how RRR can be easily added to any memory or regularization-based approach and results in reduced forgetting, and more importantly, improved model explanations. We have evaluated our approach in the standard and fewshot settings and observed a consistent improvement across various CL approaches using different architectures and techniques to generate model explanations and demonstrated our approach showing a promising connection between explainability and continual learning. Our code is available at https://github.com/SaynaEbrahimi/Remembering-for-the-Right-Reasons. [ABSTRACT FROM AUTHOR]

Neural network architectures are achieving superhuman performance on an expanding range of tasks. To effectively and safely deploy these systems, their decision-making must be understandable to a wide range of stakeholders. Methods to explain artificial intelligence (AI) have been proposed to answer this challenge, but a lack of theory impedes the development of systematic abstractions, which are necessary for cumulative knowledge gains. We propose Bayesian Teaching as a framework for unifying explainable AI (XAI) by integrating machine learning and human learning. Bayesian Teaching formalizes explanation as a communication act of an explainer to shift the beliefs of an explainee. This formalization decomposes a wide range of XAI methods into four components: (a) the target inference, (b) the explanation, (c) the explainee model, and (d) the explainer model. The abstraction afforded by Bayesian Teaching to decompose XAI methods elucidates the invariances among them. The decomposition of XAI systems enables modular validation, as each of the first three components listed can be tested semi-independently. This decomposition also promotes generalization through recombination of components from different XAI systems, which facilitates the generation of novel variants. These new variants need not be evaluated one by one provided that each component has been validated, leading to an exponential decrease in development time. Finally, by making the goal of explanation explicit, Bayesian Teaching helps developers to assess how suitable an XAI system is for its intended real-world use case. Thus, Bayesian Teaching provides a theoretical framework that encourages systematic, scientific investigation of XAI. [ABSTRACT FROM AUTHOR]

This study investigated the resource budget available to a typical honey bee swarm as it gathered information and decided which was the best of some 13–34 potential nest sites to move into and become its new home. After a brief description of the decision‐making mechanisms involved, the utilization of the swarm's finite energy, memory and carrier resources is described. It was found that each individual scout decision maker only carried enough energy reserves to provide for 2.6 days of visits to evaluate distant sites and, effectively, only enough memory space to remember the location and quality of approximately one site. Scouts, who only formed 4.4% of the swarm bees, consumed 22.8% of the swarm's energy budget, showing that their best‐of‐N computations were energetically very expensive. A typical swarm of 12,000 bees only contained enough energy reserves to last for 13.5 days after which foraging had to restart. [ABSTRACT FROM AUTHOR]