Artificial Intelligence Annotated Bibliography

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Artificial Intelligence Annotated Bibliography

Introduction

Artificial intelligence (AI) is increasingly becoming important in modern organizations. The technology promises to facilitate computer learning and aid in the decision-making processes in different organizations. AI will help process decisions more quickly than humans would make similar decisions. The emergence of the Internet of Things, connected network devices, and the introduction of fast data-processing computers means organizations can now collect a lot of data within a short period. Notably, such data may only be helpful if it is applied accurately. The demand for narrow AI that focuses on performing objective functions based on deep learning is required. Sectors such as customer service, healthcare, and manufacturing require the incorporation of narrow AI with human insights to make data-driven decisions objectively and accurately. AI might only produce the required results if a question is well-defined and based on a deep understanding of a topic. Therefore, incorporating human insights with artificial intelligence will help organizations apply AI to solve real-life healthcare and customer service situations, among other critical sectors.

Annotation 1

Asan, O., Bayrak, A. E., & Choudhury, A. (2020). Artificial Intelligence and Human Trust in Healthcare: Focus on Clinicians. Journal of Medical Internet Research, 22(6), e15154. https://doi.org/10.2196/15154

Asan et al. (2020) state that a lack of trust is a major hindrance to adopting AI in healthcare. The authors argue that AI makes imperfect healthcare suggestions, which may hinder clinicians’ adoption of AI. It means that even if AI were to make accurate decisions, humans might not believe its suggestions since healthcare decisions may be a matter of life and death. In driving their point, Asan et al. (2020) article includes an abstract, introduction, definition of AI, AI application in healthcare, human-AI collaboration, and a conclusion. The source mostly draws from the work of other intellectuals.

This source is based on human psychology. The goal is to improve AI to create an environment that allows human psychology, through clinicians, to trust the output of AI. To that end, Asan et al. (2020) favour the development of calibrated trust, which will involve the creation of rule-based software that makes repetitive healthcare decisions, just like clinicians do. The source emphasizes how difficult it is for AI to work on its own without human input.

Annotation 2

Beaulac, C., & Larribe, F. (2017). Narrow Artificial Intelligence with Machine Learning for Real-Time Estimation of a Mobile Agent’s Location Using Hidden Markov Models. International Journal of Computer Games Technology, 2017, 1–10. https://doi.org/10.1155/2017/4939261

According to Beaulac and Larribe (2017), narrow artificial intelligence can be applied to determining the location of a mobile agent in real-time. From the outset, the authors state that general AI, on its own, may not be able to locate a mobile agent. Using a video game example, the authors highlight how a human player may improve performance against an opponent by observing their playing strategies. However, this is not possible if AI is applied. The authors use a mathematical model to understand how AI misses studying prior behaviour.

Beaulac and Larribe (2017) conclude that AI lacks adaptive memory based on prior experience to self-improve. Therefore, there is a need for human intervention, such as creating a model that will facilitate AI’s machine-learning capabilities. Once a learning model is developed, AI can improve in subsequent games based on previous encounters. The Hidden Markov Model helped AI improve the gaming experience against human players by using the model’s estimation and learning experience. That shows the importance of including human input in AI to improve tech-based decision-making.

Annotation 3

Cheng, X., Guo, F., Chen, J., Li, K., Zhang, Y., & Gao, P. (2019). Exploring the Trust Influencing Mechanism of Robo-Advisor Service: A Mixed Method Approach. Sustainability, 11(18), 4917. https://doi.org/10.3390/su11184917

According to Cheng et al. (2019), a robo-advisor service that allows individual investors to use AI to solve investment problems is supervised by humans. The service relies on AI to help individuals and social organizations build their portfolios in fintech companies. Cheng and colleagues’ article includes an abstract that introduces a robo-advisor service; the introduction clarifies what a fintech company is and how a robo-service may be applied in making investment decisions. Subsequently, the literature review focuses on the functioning of the robo-advisor service, investors’ trust in the service, trust-influencing factors, and trust transfer theory. Other parts include qualitative and quantitative research models, hypotheses, discussion, and conclusion on applying a robo-advisor service.

The authors use statistics and numbers to draw their conclusions. For instance, 27 valid investors between the ages of 20-50 years were interviewed to build qualitative data. Semi-structured interviews were conducted in the process. On the other hand, a study on 240 investors was conducted through online surveys to generate quantitative data. A significant difference between this source and others is that Cheng et al. (2019) relied on primary data to come up with conclusions. To conclude, Cheng et al. (2019) argue that supervisory control is vital in enhancing trust in a robo-advisor service. There is a need for human intervention through supervisory authority to improve trust in AI tools due to their novelty.

Annotation 4

Johnson, M., Albizri, A., Harfouche, A., & Fosso-Wamba, S. (2022). Integrating human knowledge into artificial intelligence for complex and ill-structured problems: Informed artificial intelligence. International Journal of Information Management, 64, 102479. https://doi.org/10.1016/j.ijinfomgt.2022.102479

Johnson et al. (2022) delve into how artificial intelligence may be applied to solve complex and ill-structured problems with the help of human knowledge. In drawing their conclusion, Johnson and colleagues include an abstract, an introduction to ill-structured problems, a literature review on the application of human domain knowledge, a discussion, and a conclusion on the subject. Ill-structured problems occur in a dynamic environment and do not have a single best solution. In the same breath, no correct algorithm may be applied in AI to bring a single-best solution. The authors draw most of the insights from peer literature on the subject.

A similarity between this source and the others used in this article is incorporating human knowledge into AI to create a narrow and objective AI. However, unlike other sources, this source focuses on dynamic environments with no specific AI algorithm. Expert human knowledge is incorporated with symbolic AI to drive decisions where there is no definitive answer. For instance, businesses seeking customer satisfaction may apply human customer service experts’ inputs and symbolic AI to create custom satisfaction metrics for different customers based on their needs.

Annotation 5

Reddy, S. (2018). Use of Artificial Intelligence in Healthcare Delivery. EHealth – Making Health Care Smarter. https://doi.org/10.5772/intechopen.74714

According to Reddy (2018), the application of AI in healthcare has opened more opportunities for AI to improve healthcare delivery beyond the traditional replication of human insights. The author’s main point is that AI does not necessarily have to be limited to human/clinician actions but can be used to improve delivery. Reddy’s (2018) article includes an abstract, introduction, development of AI, AI tools, history of AI in healthcare, likely future trends, challenges, and a conclusion. In driving his point, the author primarily refers to historical documents and peer research to predict future AI applications.

While annotations one and two emphasize how AI may be improved through human capabilities, this source explains that human capabilities should form the basis of AI. The author uses the analogy of a bird and a plane to explain why AI capabilities should not be limited (Reddy, 2018). The bird may have been the basis of developing the aeroplane, but today’s plane technology exceeds the bird’s capabilities. To that end, clinicians’ notes, patient photos, videos, and diagnosis reports may be used to make effective healthcare decisions beyond what human capabilities would achieve.

References

Beaulac, C., & Larribe, F. (2017). Narrow Artificial Intelligence with Machine Learning for Real-Time Estimating a Mobile Agent’s Location Using Hidden Markov Models. International Journal of Computer Games Technology, 2017, 1–10. https://doi.org/10.1155/2017/4939261.

Reddy, S. (2018). Use of Artificial Intelligence in Healthcare Delivery. EHealth – Making Health Care Smarter. https://doi.org/10.5772/intechopen.74714.

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Question

Prepare: Prior to beginning work on this assignment, review the Introductions & Conclusions. Links to an external site. And Annotated BibliographyLinks to an external site. Web pages and Evaluating Sources Links to an external site. And Annotated Bibliography Links to an external site. Tutorials.

Artificial Intelligence Annotated Bibliography

Reflect: Reflect on the Week 1 discussion in which you shared the global societal issue you would like to address further with the class. Explore critical insights shared by your peers and/or your instructor on the chosen topic, and begin your search for scholarly sources with those insights in mind.

Write: For this assignment, review the Annotated Bibliography Formatting Guidelines Download Annotated Bibliography Formatting Guidelines and address the following prompts:

Introductory paragraph to topic (refer to the Final Paper guidelines for your topic selection).
Write an introductory paragraph with at least 150 words explaining the topic, the importance of further research, and ethical implications.
Thesis statement.
Write a direct and concise thesis statement to solve the problem you will argue or prove in the Week 5 Final Paper. (A thesis statement should be a concise, declarative statement. The thesis statement must appear at the end of the introductory paragraph.)
Annotated bibliography.
Develop an annotated bibliography to indicate the quality of the sources you have read.
Summarize in your own words how the source contributes to the solution of the global societal issue for each annotation.
Address fully the purpose, content, evidence, and relation to other sources you found on this topic (your annotation should be one to two paragraphs long—150 words or more.
Include no less than five scholarly sources in the annotated bibliography that will be used to support the major points of the Final Paper.
Demonstrate critical thinking skills by accurately interpreting evidence used to support various positions on the topic.