Antecedents of intention to adopt artificial intelligence services by consumers in personal financial investing

Objective Autonomous cars (ACs) controlled by artificial intelligence are expected to play a significant role in transportation in the near future. This study investigated determinants of trust in ACs. Background Trust in ACs influences different variables, including the intention to adopt AC technology. Several studies on risk perception have verified that shared value determines trust in risk managers. Previous research has confirmed the effect of value similarity on trust in artificial intelligence. We focused on moral beliefs, specifically utilitarianism (belief in promoting a greater good) and deontology (belief in condemning deliberate harm), and tested the effects of shared moral beliefs on trust in ACs. Method We conducted three experiments ( N = 128, 71, and 196, for each), adopting a thought experiment similar to the well-known trolley problem. We manipulated shared moral beliefs (shared vs. unshared) and driver (AC vs. human), providing participants with different moral dilemma scenarios. Trust in ACs was measured through a questionnaire. Results The results of Experiment 1 showed that shared utilitarian belief strongly influenced trust in ACs. In Experiment 2 and Experiment 3, however, we did not find statistical evidence that shared deontological belief had an effect on trust in ACs. Conclusion The results of the three experiments suggest that the effect of shared moral beliefs on trust varies depending on the values that ACs share with humans. Application To promote AC implementation, policymakers and developers need to understand which values are shared between ACs and humans to enhance trust in ACs.

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Integration of Artificial Intelligence Technology Towards E-Services Among Online Clothing Websites

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.b7266.129219 ◽

2019 ◽

Vol 9 (2) ◽

pp. 1562-1567

Keyword(s):

Artificial Intelligence ◽

Customer Satisfaction ◽

Customer Service ◽

Tamil Nadu ◽

Customer Relationship ◽

Snowball Sampling ◽

Customer Experience Management ◽

Intelligence Services ◽

Artificial Intelligence Technology ◽

Tamil Nadu State

Online Apparel Industry is one of the growing industries among many other online markets. The industry is moving towards a major technological shift due to new and innovative tools such as Artificial Intelligence (AI), Virtual Reality (VR) and Augmented Reality (AR). Customer Experience Management is highly influenced by gaining customer satisfaction via integrated AI technology for providing efficient customer service. This study emphasizes the intervention of AI technology with online clothing websites such as Jabong and Myntra. The findings explore that Customer Relationship Management (CRM) Services, Personalization services, Visual Assistance and Fit Intelligence Services are enhanced from AI tools that lead to Customer Satisfaction and Customer Retention. The research utilized non-probability Judgmental Sampling and snowball sampling where the respondents belong to Tamil Nadu State and were genuine online customers who purchase clothes from online clothing websites

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Exploring Visual Representation of #ShoutYourAbortion Hashtag Movement and the Public’s Responses on Instagram: Descriptive and Infodemiology Study (Preprint)

10.2196/preprints.33684 ◽

2021 ◽

Author(s):

Sunmi ‍Lee ◽

Yunhwan Kim

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Social Media ◽

Neural Network Model ◽

Predictive Analytics ◽

Visual Representation ◽

Point Of View ◽

The Public ◽

Intelligence Services ◽

Human Faces

BACKGROUND Hashtag movement has become one of the major ways of online movement, but few studies have examined how social media photos were used for the movement. Also, it has not been actively investigated how photo features were related to the public’s responses in hashtag movements. OBJECTIVE The aim of the present research was to explore Instagram photos with #ShoutYourAbortion hashtag, as an example of hashtag movements via photos, in terms of their visual representation and the relationships between photo features and the public’s responses to the photos. METHODS Instagram photos with #ShoutYourAbortion hashtag, 11,176 in total, were downloaded, and their content and embedded texts were analyzed using online artificial intelligence services. The photos were clustered into subgroups based on the features extracted using a pretrained convolutional neural network model. The resulting clusters were compared in terms of their content tags, embedded texts, and photo features which were manually extracted at the content and pixel levels. The public’s responses were measured by engagement and comment sentiment. Correlational analysis and predictive analytics were conducted to examine the relationships between photo features and the public’s responses. RESULTS It was found that the photos in the text category took the largest share (57.19%), and the embedded texts were mainly about stories told in first person point of view as a woman. A possible evidence of hashtag hijacking was observed. The photos were grouped into two clusters; the first cluster comprised photos which exhibit text materials on them, while the second cluster consisted of photos which contain human faces with texts. The photos in the first cluster were brighter, while the photos in the second cluster were more colorful than the others. And public responses were found to be related to photo features such as size of faces, happy emotion, and share of warm colors. Engagement was predicted from the photo features with an acceptable level of accuracy, while comment sentiment was not. CONCLUSIONS This This study has shown the visual representation of #ShoutYourAbortion hashtag movement. It has also shown how photo features at content and pixel levels were related to the public’s responses to the photos. The results are expected to contribute to the understanding of hashtag movements via photos and making photos in hashtag movements more appealing to the public. CLINICALTRIAL Not Applicable

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System Design and Development

10.4018/978-1-7998-7955-8.ch004 ◽

2022 ◽

pp. 72-86

Keyword(s):

Artificial Intelligence ◽

Natural Language ◽

System Design ◽

Development Process ◽

Service Providers ◽

Design And Development ◽

Language Processor ◽

Intelligence Services ◽

Intelligence Service

This chapter presents the Socrates DigitalTM system's design and development process. It describes the four phases of design and development: understand, explore, materialize, and realize. The completion of these four phases results in a Socrates DigitalTM system that leverages artificial intelligence services. The artificial intelligence services include a natural language processor provided by several artificial intelligence service providers, including Apple, Microsoft, Google, IBM, and Amazon.

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Drivers and social implications of Artificial Intelligence adoption in healthcare during the COVID-19 pandemic

PLoS ONE ◽

10.1371/journal.pone.0259928 ◽

2021 ◽

Vol 16 (11) ◽

pp. e0259928

Author(s):

Darius-Aurel Frank ◽

Christian T. Elbæk ◽

Caroline Kjær Børsting ◽

Panagiotis Mitkidis ◽

Tobias Otterbring ◽

...

Keyword(s):

Artificial Intelligence ◽

Initial Phase ◽

European Countries ◽

Social Implications ◽

Trust Building ◽

Scarce Resources ◽

Intention To Adopt ◽

Representative Study ◽

The Social ◽

Social Belonging

The COVID-19 pandemic continues to impact people worldwide–steadily depleting scarce resources in healthcare. Medical Artificial Intelligence (AI) promises a much-needed relief but only if the technology gets adopted at scale. The present research investigates people’s intention to adopt medical AI as well as the drivers of this adoption in a representative study of two European countries (Denmark and France, N = 1068) during the initial phase of the COVID-19 pandemic. Results reveal AI aversion; only 1 of 10 individuals choose medical AI over human physicians in a hypothetical triage-phase of COVID-19 pre-hospital entrance. Key predictors of medical AI adoption are people’s trust in medical AI and, to a lesser extent, the trait of open-mindedness. More importantly, our results reveal that mistrust and perceived uniqueness neglect from human physicians, as well as a lack of social belonging significantly increase people’s medical AI adoption. These results suggest that for medical AI to be widely adopted, people may need to express less confidence in human physicians and to even feel disconnected from humanity. We discuss the social implications of these findings and propose that successful medical AI adoption policy should focus on trust building measures–without eroding trust in human physicians.

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Artificial Intelligence Models for Crime Prediction in Urban Spaces

Machine Learning and Applications An International Journal ◽

10.5121/mlaij.2021.8101 ◽

2020 ◽

Vol 8 (1) ◽

pp. 1-13

Author(s):

Ana Laura Lira Cortes ◽

Carlos Fuentes Silva

Keyword(s):

Artificial Intelligence ◽

Neural Networks ◽

Urban Areas ◽

Urban Context ◽

Criminal Records ◽

Legal Information ◽

Legal Advice ◽

Intelligence Services ◽

Artificial Intelligence Models ◽

The Government

This work presents research based on evidence with neural networks for the development of predictive crime models, finding the data sets used are focused on historical crime data, crime classification, types of theft at different scales of space and time, counting crime and conflict points in urban areas. Among some results, 81% precision is observed in the prediction of the Neural Network algorithm and ranges in the prediction of crime occurrence at a space-time point between 75% and 90% using LSTM (Long-ShortSpace-Time). It is also observed in this review, that in the field of justice, systems based on intelligent technologies have been incorporated, to carry out activities such as legal advice, prediction and decisionmaking, national and international cooperation in the fight against crime, police and intelligence services, control systems with facial recognition, search and processing of legal information, predictive surveillance, the definition of criminal models under the criteria of criminal records, history of incidents in different regions of the city, location of the police force, established businesses, etc., that is, they make predictions in the urban context of public security and justice. Finally, the ethical considerations and principles related to predictive developments based on artificial intelligence are presented, which seek to guarantee aspects such as privacy, privacy and the impartiality of the algorithms, as well as avoid the processing of data under biases or distinctions. Therefore, it is concluded that the scenario for the development, research, and operation of predictive crime solutions with neural networks and artificial intelligence in urban contexts, is viable and necessary in Mexico, representing an innovative and effective alternative that contributes to the attention of insecurity, since according to the indices of intentional homicides, the crime rates of organized crime and violence with firearms, according to statistics from INEGI, the Global Peace Index and the Government of Mexico, remain in increase.

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