scholarly journals Human-Centered Explainable Artificial Intelligence for Marine Autonomous Surface Vehicles

2021 ◽  
Vol 9 (11) ◽  
pp. 1227
Author(s):  
Erik Veitch ◽  
Ole Andreas Alsos
Keyword(s):  
Artificial Intelligence ◽  
User Interaction ◽  
Mental Simulation ◽  
End User ◽  
User Interactions ◽  
Model Interpretation ◽  
Autonomous Surface Vehicles ◽  
Research Activities ◽  
Explainable Artificial Intelligence ◽  
Carry Over

Explainable Artificial Intelligence (XAI) for Autonomous Surface Vehicles (ASVs) addresses developers’ needs for model interpretation, understandability, and trust. As ASVs approach wide-scale deployment, these needs are expanded to include end user interactions in real-world contexts. Despite recent successes of technology-centered XAI for enhancing the explainability of AI techniques to expert users, these approaches do not necessarily carry over to non-expert end users. Passengers, other vessels, and remote operators will have XAI needs distinct from those of expert users targeted in a traditional technology-centered approach. We formulate a concept called ‘human-centered XAI’ to address emerging end user interaction needs for ASVs. To structure the concept, we adopt a model-based reasoning method for concept formation consisting of three processes: analogy, visualization, and mental simulation, drawing from examples of recent ASV research at the Norwegian University of Science and Technology (NTNU). The examples show how current research activities point to novel ways of addressing XAI needs for distinct end user interactions and underpin the human-centered XAI approach. Findings show how representations of (1) usability, (2) trust, and (3) safety make up the main processes in human-centered XAI. The contribution is the formation of human-centered XAI to help advance the research community’s efforts to expand the agenda of interpretability, understandability, and trust to include end user ASV interactions.

scholarly journals Towards an Explainer-agnostic Conversational XAI

2021 ◽  
Author(s):  
Navid Nobani ◽  
Fabio Mercorio ◽  
Mario Mezzanzanica
Keyword(s):  
Artificial Intelligence ◽  
Black Box ◽  
The Other ◽  
Human Being ◽  
End User ◽  
Other Hand ◽  
Explainable Artificial Intelligence ◽  
Overall Performance

Explainable Artificial Intelligence (XAI) is gaining interests in both academia and industry, mainly thanks to the proliferation of darker more complex black-box solutions which are replacing their more transparent ancestors. Believing that the overall performance of an XAI system can be augmented by considering the end-user as a human being, we are studying the ways we can improve the explanations by making them more informative and easier to use from one hand, and interactive and customisable from the other hand.

scholarly journals Artificial Intelligence Affordances for Business Innovation: A Systematic Review of Literature

10.29007/jj72 ◽  
2019 ◽  
Author(s):  
Luqman Achmat ◽  
Irwin Brown
Keyword(s):  
Artificial Intelligence ◽  
Language Processing ◽  
Business Processes ◽  
User Interaction ◽  
Clear Understanding ◽  
Strategic Decisions ◽  
End User ◽  
Business Innovation ◽  
Definition Of ◽  
Review Current

Emerging technologies like artificial intelligence (AI) have begun to play an ever- more important role in business innovation. The purpose of this paper is to review current literature to identify definitions and concepts related to artificial intelligence affordances and how artificial intelligence affords business innovation. Using a systematic six-step literature review methodology conducted with an iterative disposition, seven major affordances of AI for business innovation were identified, i.e. (i) Automate business processes, (ii) Customise end user interaction, (iii) Proactively anticipate and react to changes, (iv) Augment and upskill the workforce, (v) Assist decision making, (vi) Improve risk management, and (vii) Develop and enhance intellectual property. The literature surveyed furthermore shows that there are several gaps which allow for further research. Firstly, the definition of artificial intelligence is inconsistent and there is no widely accepted definition. Several AI-based technologies and applications being developed (e.g. Machine Learning, Deep Learning, Natural Language Processing and Neural Networks) require a clear understanding of the affordances of such technologies to be able to make informed strategic decisions. Therefore, understanding the affordances of artificial intelligence in general plays an important role in making such decisions.

Use of activated carbon to remove undesirable residual amylase from refinery streams

2017 ◽  
pp. 96-103 ◽  
Author(s):  
Gillian Eggleston ◽  
Isabel Lima ◽  
Emmanuel Sarir ◽  
Jack Thompson ◽  
John Zatlokovicz ◽  
...  
Keyword(s):  
Activated Carbon ◽  
High Temperature ◽  
High Performance ◽  
Activated Carbons ◽  
Amylase Activity ◽  
Sugar Industry ◽  
End User ◽  
Customer Complaints ◽  
Carry Over ◽  
Very High

In recent years, there has been increased world-wide concern over residual (carry-over) activity of mostly high temperature (HT) and very high temperature (VHT) stable amylases in white, refined sugars from refineries to various food and end-user industries. HT and VHT stable amylases were developed for much larger markets than the sugar industry with harsher processing conditions. There is an urgent need in the sugar industry to be able to remove or inactivate residual, active amylases either in factory or refinery streams or both. A survey of refineries that used amylase and had activated carbon systems for decolorizing, revealed they did not have any customer complaints for residual amylase. The use of high performance activated carbons to remove residual amylase activity was investigated using a Phadebas® method created for the sugar industry to measure residual amylase in syrups. Ability to remove residual amylase protein was dependent on the surface area of the powdered activated carbons as well as mixing (retention) time. The activated carbon also had the additional benefit of removing color and insoluble starch.

scholarly journals Artificial Intelligence and Behavioral Science Through the Looking Glass: Challenges for Real-World Application

2020 ◽  
Vol 54 (12) ◽  
pp. 942-947
Author(s):  
Pol Mac Aonghusa ◽  
Susan Michie
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Behavior Change ◽  
Behavioral Science ◽  
Lessons Learned ◽  
Learning Approaches ◽  
Intervention Evaluation ◽  
Research Activities ◽  
Behavior Change Interventions

Abstract Background Artificial Intelligence (AI) is transforming the process of scientific research. AI, coupled with availability of large datasets and increasing computational power, is accelerating progress in areas such as genetics, climate change and astronomy [NeurIPS 2019 Workshop Tackling Climate Change with Machine Learning, Vancouver, Canada; Hausen R, Robertson BE. Morpheus: A deep learning framework for the pixel-level analysis of astronomical image data. Astrophys J Suppl Ser. 2020;248:20; Dias R, Torkamani A. AI in clinical and genomic diagnostics. Genome Med. 2019;11:70.]. The application of AI in behavioral science is still in its infancy and realizing the promise of AI requires adapting current practices. Purposes By using AI to synthesize and interpret behavior change intervention evaluation report findings at a scale beyond human capability, the HBCP seeks to improve the efficiency and effectiveness of research activities. We explore challenges facing AI adoption in behavioral science through the lens of lessons learned during the Human Behaviour-Change Project (HBCP). Methods The project used an iterative cycle of development and testing of AI algorithms. Using a corpus of published research reports of randomized controlled trials of behavioral interventions, behavioral science experts annotated occurrences of interventions and outcomes. AI algorithms were trained to recognize natural language patterns associated with interventions and outcomes from the expert human annotations. Once trained, the AI algorithms were used to predict outcomes for interventions that were checked by behavioral scientists. Results Intervention reports contain many items of information needing to be extracted and these are expressed in hugely variable and idiosyncratic language used in research reports to convey information makes developing algorithms to extract all the information with near perfect accuracy impractical. However, statistical matching algorithms combined with advanced machine learning approaches created reasonably accurate outcome predictions from incomplete data. Conclusions AI holds promise for achieving the goal of predicting outcomes of behavior change interventions, based on information that is automatically extracted from intervention evaluation reports. This information can be used to train knowledge systems using machine learning and reasoning algorithms.

scholarly journals The most used questionnaires for evaluating telemedicine services

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sadrieh Hajesmaeel-Gohari ◽  
Kambiz Bahaadinbeigy
Keyword(s):  
Technology Acceptance ◽  
User Interaction ◽  
End User ◽  
Publication Type ◽  
Patient Satisfaction Questionnaire ◽  
Satisfaction Questionnaire ◽  
Implementation Processes ◽  
Pubmed Database ◽  
System Usability Scale ◽  
Acceptance Model

Abstract Background Questionnaires are commonly used tools in telemedicine services that can help to evaluate different aspects. Selecting the ideal questionnaire for this purpose may be challenging for researchers. This study aims to review which questionnaires are used to evaluate telemedicine services in the studies, which are most common, and what aspects of telemedicine evaluation do they capture. Methods The PubMed database was searched in August 2020 to retrieve articles. Data extracted from the final list of articles included author/year of publication, journal of publication, type of evaluation, and evaluation questionnaire. Data were analyzed using descriptive statistics. Results Fifty-three articles were included in this study. The questionnaire was used for evaluating the satisfaction (49%), usability (34%), acceptance (11.5%), and implementation (2%) of telemedicine services. Among telemedicine specific questionnaires, Telehealth Usability Questionnaire (TUQ) (19%), Telemedicine Satisfaction Questionnaire (TSQ) (13%), and Service User Technology Acceptability Questionnaire (SUTAQ) (5.5%), were respectively most frequently used in the collected articles. Other most used questionnaires generally used for evaluating the users’ satisfaction, usability, and acceptance of technology were Client Satisfaction Questionnaire (CSQ) (5.5%), Questionnaire for User Interaction Satisfaction (QUIS) (5.5%), System Usability Scale (SUS) (5.5%), Patient Satisfaction Questionnaire (PSQ) (5.5%), and Technology Acceptance Model (TAM) (3.5%) respectively. Conclusion Employing specifically designed questionnaires or designing a new questionnaire with fewer questions and more comprehensiveness in terms of the issues studied provides a better evaluation. Attention to user needs, end-user acceptance, and implementation processes, along with users' satisfaction and usability evaluation, may optimize telemedicine efforts in the future.

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