Trust in Artificial Intelligence: Toward Measuring the Impact of Public Perception

Applications of Artificial Intelligence (AI) are currently seen in almost every sector. Some of the common examples of AI applications are visible in recommender systems such as movie recommendations, books recommendation, restaurant recommendations, etc. Earlier, the role of trust in technology adoption was recognized in the Information Systems (IS) discipline. Thus, with the growing use of AI, identifying the factors contributing toward building trust in this technology has become a critical issue. The public perception of AI was found to reveal trust toward AI (Zhang 2021). Therefore, we propose to measure the impact of two dimensions of AI public perception toward building trust in this technology. These two dimensions are control of AI and ethics in AI. We also propose to include a mediating factor called mood. These dimensions and the mediating factor were found as a component of public perception of AI in a previous study. This study used a dataset of trends in public perception of AI extracted from news articles published in the New York Times over 30 years (Fast and Horvitz 2017). The dimensions of trust that may impact trust in AI have been identified previously (Glikson and Woolley 2020). These dimensions were based on two aspects of trust – cognitive trust and emotional trust. Although separate dimensions for each of these aspects have been identified, some of them seem to overlap. The dimensions of cognitive trust include tangibility, transparency, reliability, task characteristics, and immediacy behavior. On the other hand, the dimensions of emotional trust also include tangibility, and immediacy behaviors, in addition to anthropomorphism. Our proposed dimensions will have an impact on both cognitive trust and emotional trust in AI. However, control and ethics will have a direct impact on cognitive trust, and an indirect impact on emotional trust through the mediating factor mood. In a previous study, mood was identified as an internal factor that can alter trust in AI (Hoff and Bashir 2015). In the dataset to be used for this study, the variable named “control” indicates whether a certain paragraph in an article implies public concern about the loss of control in AI. On the other hand, the variable named “ethics” indicates the presence of ethical concern in public perception. The mediating variable “mood” is measured ranging from pessimistic to optimistic. For the purpose of our study, we will measure the direct impact of “control” and “ethics” toward building trust in AI, as well as the indirect impact through the mediating variable “mood”. We plan to use structural equation modeling (SEM) for the analysis, as it will enable us to measure the impact of the mediating variable in this context

Better supporting workers in ML workplaces

This workshop is aimed at bringing together a multidisciplinary group to discuss Machine Learning and its application in the workplace as a practical, everyday work matter. It's our hope this is a step toward helping us design better technology and user experiences to support the accomplishment of that work, while paying attention to workplace context. Despite advancement and investment in Machine Learning (ML) business applications, understanding workers in these work contexts have received little attention. As this category experiences dramatic growth, it's important to better understand the role that workers play, both individually and collaboratively, in a workplace where the output of prediction and machine learning is becoming pervasive. There is a closing window of opportunity to investigate this topic as it proceeds toward ubiquity. CSCW and HCI offer concepts, tools and methodologies to better understand and build for this future

Are Textual Recommendations Enough? Guiding Physicians Toward the Design of Machine Learning Pipelines Through a Visual Platform

The prevalence of artificial intelligence (AI) in our daily lives is often exaggerated by the media, leading to a positive public perception while overlooking potential problems. In the field of medicine, it is crucial to educate future healthcare professionals on the advantages and disadvantages of AI and to emphasize the importance of creating fair, ethical, and reproducible models. The KoopaML platform was developed to provide an educational and user-friendly interface for inexperienced users to create AI pipelines. This study analyzes the quantitative and interaction data gathered from a usability test involving physicians from the University Hospital of Salamanca, with the aim of identifying new interaction paradigms to improve the platform’s usability. The results shown that the platform is difficult to learn for inexperienced users due to its contents related to AI. Following these results, a set of improvements are proposed for the next version of KoopaML, focusing on reducing the interactions needed to create the pipelines

Characteristics and Subjective Evaluation of an Intelligent Empowering Agent for Health Person Empowerment

Empowerment is a process through which people acquire the necessary knowledge and self-awareness to understand their health conditions and treatment options, self-manage them, and make informed choices. Currently, few stand-alone applications for patient empowerment exist and people/patients often go on the Web to search for health information. Such information is mainly obtained through generic search engines and it is often overwhelming, too generic, and of poor quality. Intelligent Empowering Agents (IEA) can filter such information and assist the user in the understanding of health information about specific complaints or health in general. We have designed and developed a first prototype of an IEA that dialogues with the user in simple language, collects health information from the Web, and provides tailored, easily understood, and trusted information. It empowers users to create their own comprehensive and objective opinion on health matters that concern them. The paper describes the IEA main characteristics and presents the results of subjective tests carried out to assess the effectiveness of the IEA. Twenty-eight Master students in Digital Health filled an online survey presenting questions on usability, user experience and perceived value. Most respondents found the IEA easy to use and helpful. They also felt that it would improve communication with their doctors

Pursuing an AI Ontology for Landscape Architecture

Technological advancements have become ubiquitous within landscape architecture. One of the latest advancements is in Artificial Intelligence, including techniques such as Machine Learning, Artificial Neural Networks and problem optimization. These advancements have already worked their way into landscape architecture. In this theoretical paper we briefly identify what the state of the art in AI is, as well as its potential and limitations in the discipline. Specifically, we argue for the need to create a disciplinary ontology to make knowledge explicit and shared amongst humans and machines

Artificial Intelligence (AI) Solutions In English Language Teaching: Teachers-Students Perceptions And Experiences

This literature research article explores the perceptions and experiences of teachers and students regarding Artificial Intelligence (AI) solutions in English language teaching. With the rapid advancements in AI technologies, there is a growing interest in leveraging these tools to enhance language learning experiences. Understanding the perspectives of teachers and students is crucial for successful implementation and to harness the potential benefits of AI in the field of English language education. The study adopts a mixed-methods research approach, incorporating both quantitative and qualitative methods. A survey was conducted to gather quantitative data on participants' attitudes, beliefs, and experiences related to AI integration. Additionally, in-depth interviews and focus group discussions were conducted to obtain qualitative insights and delve into participants' perceptions and challenges. The findings of the study reveal positive attitudes towards AI solutions in English language teaching, with participants highlighting the effectiveness of AI technologies in improving language skills and providing personalized instruction. The adaptive nature of AI tools was valued for its ability to cater to individual needs and offer immediate feedback. However, concerns were raised regarding technological readiness and the need for training and support in effectively utilizing AI solutions. These findings have implications for educators, policymakers, and curriculum developers, highlighting the need for technological readiness, teacher training, and support in implementing AI solutions effectively. By embracing the potential of AI while preserving the human element, English language teaching can benefit from personalized and adaptive learning experiences

Painting Music : Using artificial intelligence to create music from live painted drawings

Peer reviewedPostprin

Attitudes of Patients and Their Relatives Towards Artificial Intelligence in Neurosurgery

BACKGROUND: Artificial Intelligence (AI) may favorably support surgeons but may result in concern among patients and their relatives. OBJECTIVE: To evaluate attitudes of patients and their relatives towards the use of AI in neurosurgery. METHODS: In this two-stage cross-sectional survey, a qualitative survey was administered to a focus group of former patients to investigate their perception of AI and its role in neurosurgery. Five themes were identified and used to generate a case-based quantitative survey administered to inpatients and their relatives over a two-week period. Presented AI platforms were rated appropriate and acceptable using 5-point Likert scales. Demographic data was collected. A Chi Square test was performed to determine whether demographics influenced participants' attitudes. RESULTS: In the first stage, 20 participants responded. Five themes were identified: interpretation of imaging (4/20; 20%), operative planning (5/20; 25%), real-time alert of potential complications (10/20; 50%), partially autonomous surgery (6/20; 30%), fully autonomous surgery (3/20; 15%). In the second stage, 107 participants responded. The majority felt appropriate and acceptable to use AI for imaging interpretation (76.7%; 66.3%), operative planning (76.7%; 75.8%), real-time alert of potential complications (82.2%; 72.9%), and partially autonomous surgery (58%; 47.7%). Conversely, most did not feel that fully autonomous surgery was appropriate (27.1%) or acceptable (17.7%). Demographics did not have a significant influence on perception. CONCLUSIONS: The majority of patients and their relatives believed that AI has a role in neurosurgery and found it acceptable. Notable exceptions remain fully autonomous systems, with most wanting the neurosurgeon ultimately to remain in control