Factors that Hinder Effective Adoption and Utilization of Artificial Intelligence Algorithms
For the final paper for this course, please submit an essay on the topic of your choosing. The only constraints on content for this assignment are as follows:
• The topic must be relevant to the issues addressed in modules 5 and/or 6.
• Your essay must substantively engage with at least three (3) of the required readings from modules 5 and/or 6.
• You may include outside sources, if appropriate, and you may refer to readings outside modules 5/6, as long as you have satisfied the above criterion. As before, keep in mind that you should not lean too heavily on external sources, insofar as this is a short essay and we are interested in your views on the subject.
• Since there is no specific prompt for this assignment, your thesis statement should be clearly stated near to the start of the paper. It should be obvious to your reader what the topic of your paper is, and what you are arguing.
As with essays 1 and 2, your final submission should be around 1250 words in total (±10%), excluding references.
Your essay submission is subject to the following restrictions:
• Do not include any identifying information in your submission. The essays will be graded anonymously.
o Note : Including identifying information (name, banner number, etc.) anywhere on your essay or the file name will be sufficient for a penalty of 1 mark on your final grade for essay 3.
• As per the syllabus, you must have completed and obtained 100% on the plagiarism quiz prior to submitting your assignment. If the plagiarism quiz is not submitted, or a score of 100% is not obtained, then your essay submission will not be graded. In this case, the 'submission date' of your essay will be counted as the submission date of your plagiarism quiz, and the essay will then be subject to the late penalty policy outlined in the syllabus.
o Note : You only have to complete the plagiarism quiz once. If you have already completed the quiz and obtained a score of 100%, you do not need to re-take the quiz.
• Your essay should be around 1125-1375 words in length.
• Your essay should be submitted in .pdf or .docx format.
• Your essay should be double-spaced with standard formatting (12 point, times new roman or similar font, 1 inch Margins, etc.)
• Any quotations or paraphrases of other people's ideas must be properly cited.
• A complete and properly-formatted list of 'works cited' should be included at the end of the document.
o Note 1 : the 'works cited' does not count toward the word limit.
o Note 2 : a properly-formatted citation for each of the articles in Modules 5 and 6 are given in the complete reading list on the course web page.
• Your submission should be generally free of grammatical and typographical errors—i.e., you should spend some time editing before you submit. A few minor mistakes will not warrant a deduction in points; however, significant typographical and grammatical errors, which lead to unclarity in exposition, will be penalised according to the rubric (below).
• A rubric for how this essay will be graded has been posted on the Additional Resources section of the course webpage. You should look at it before you begin writing so that you understand how your work is being graded.
• You should take account of the feedback given to you by the TAs from your first and second essays.
A summary of the breakdown of points is given in the table below, but more details are provided in the rubric.
Thesis Statement 5 Points
Arguments: Inferential Structure and Consistency 15 Points
Arguments: Strength 15 Points
Consideration of Alternatives / Counterarguments 10 Points
Insightfulness, Creativity, Novelty 5 Points
Explanation and Analysis of Topic/Question/Problem 15 Points
Exposition, Analysis, and Evaluation of Others' Arguments & Position 15 Points
Integration of Background Explanation and One's Own Position 5 Points
Roadmapping 2.5 Points
Guide-Posting 2.5 Points
Structure 5 Points
Readability 5 Points
OVERALL 100 Points
Factors that Hinder Effective Adoption and Utilization of Artificial Intelligence Algorithms
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Factors that Hinder Effective Adoption and Utilization of Artificial Intelligence Algorithms
In recent years, artificial intelligence (AI) algorithms have been used to automate decision-making in many areas and to help pass on information. They have been used in the health sector to predict the risk of disease or to diagnose patients (Creel, 2020), in criminal justice to improve justice outcomes, and in the news media to decide what stories to share with the public. One of the reasons why AI algorithms have been considered crucial in decision-making is because most people, as revealed by Johnson (2020), see the use of AI algorithms in decision-making as more objective and bias-free when compared to human decision-making. However, to make these decisions, AI algorithms rely on data and these data are not free from human influence. AI algorithms learn how to look at social patterns and human behaviors and these are characterized by human bias in one way or another. The information that AI algorithms rely on to make decisions can be tainted or incomplete, and this makes their decision-making biased or subjective. As such, questions about the dependability of AI algorithms in decision-making have been raised even as scientists and users of AI algorithms consider their application and value in creating solutions in society. AI algorithms present significant value to society but their adoption and utilization are characterized by many challenges such as data credibility and quality, limited transparency, and bias.
Data Credibility and Quality
The trustworthiness of AI algorithms is highly dependent on the quality and credibility of data used to train them. According to Miceli et al. (2022), the quality of data used in AI algorithms influences the quality of automated decisions and outcomes. When the quality of data is poor, the outcomes will also be poor. It is important to look at the quality and credibility of data in terms of data accuracy, consistency, and completeness. Tomasev et al. (2021) indicate that the accuracy of data depends on the accuracy of the data sources. Yet, there are many sources of errors that contribute to inaccurate training data in AI algorithms. If the data sources are generating inaccurate data, they can contribute to poor decision-making of not only the AI algorithms but also of humans presented with such data. Creel (2020) indicates that AI algorithms have coding errors that can result in faulty computations. This affects the outcome of automated decisions. In addition, a lack of data consistency hinders the ability of AI algorithms to make accurate computations, and this hinders the reliability of the outcomes resulting from automated decision-making. Often, AI algorithms rely on databases to learn (Creel, 2020). Yet, these databases may be plagued with inconsistencies that negatively influence AI algorithms, rendering them untrustworthy. In addition, AI algorithms sometimes face the danger of data errors that are difficult to detect (Creel, 2020). This is made worse by the fact that the data processes in AI ...
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