Module 5: Discussion: Text Mining Projects Using Artificial Intelligence (AI) Systems

Module 5: Discussion

Text Mining Projects Using Artificial Intelligence (AI) Systems

Text mining is the application of data mining unstructured, or less structured, text files. As the names indicate, text mining analyzes words, and data mining analyzes numeric data. With that said, is it possible that artificial intelligence systems are better suited for text mining than humans?

For your initial post in the discussion forum, review the module lesson, and address the following:

Explain Watson (see

What is the Internet of ThingsLinks to an external site.

, 3:09) or a similar AI system, and make a case for the advantages or disadvantages of applying this technology to a text mining project

Using the AI technology identified above, identify a couple of ethical or social issues posed by the technology

Respond to at least two of your peers who took the opposite position from you about the pros and cons of AI systems by addressing the following:

• Make a counter argument to either the advantages or disadvantages they identified for applying AI technology to a text mining project

Assess the ethical or social issues posed by AI technology, and offer possible solution(s) to help balance the ethical concerns identified

To successfully complete this assignment, view the

Discussion Rubric document.

To comment on:

Response 1: Google Cloud Natural Language Processing (NLP) is a cloud-based tool that utilizes advanced machine learning models to analyze unstructured text. It offers features like sentiment analysis, entity recognition, and text classification, making it valuable for tasks like customer feedback analysis, brand monitoring, and document categorization ?(Cloud et al.). For instance, a retail company could utilize Google Cloud NLP to assess customer reviews and enhance customer satisfaction (Data Science Salon, 2023). One significant advantage is scalability and seamless integration with other Google Cloud services for large-scale data analytics and streaming, such as BigQuery and Dataflow (Google Cloud, 2024) (Dataflow: Streaming Analytics, 2024). However, there are also drawbacks to consider. While Google Cloud NLP provides pre-trained models that work well for general applications, these models may not be customized to domain-specific needs. For example, the generic models might misinterpret industry-specific jargon in specialized fields such as legal, finance, or medical, leading to inaccurate insights (Bernardi et al., 2023). Custom training to improve accuracy in such contexts can be costly and time-consuming. Another significant disadvantage involves data privacy and security. When processing sensitive or proprietary information, businesses may face compliance challenges with regulations like GDPR or HIPAA, as sending data to cloud services poses potential risks (European Commission, n.d.). Additionally, the cost of using cloud-based NLP can increase, especially for continuous, large-scale text mining projects. Google’s pay-per-use pricing model may lead to higher expenses if usage is not carefully managed ?(Pricing |  loud Natural Language API | Google Cloud, 2019). In conclusion, while Google Cloud NLP offers robust and scalable solutions for text mining, organizations must weigh the benefits of ease and efficiency against the potential drawbacks of customization needs, data security concerns, and long-term costs.

AI technology poses various ethical and social challenges, including privacy, bias, job displacement, and accountability. One major concern is data privacy, as AI systems heavily rely on large datasets containing personal or sensitive information. AI-powered facial recognition technology has raised concerns about widespread surveillance and potential data misuse (Facial Recognition Technology: Fundamental Rights Considerations in the Context of Law Enforcement, 2019). Another significant ethical concern is algorithmic bias. AI systems trained on biased data can produce unfair or discriminatory outcomes. For example, AI hiring tools have been found to discriminate against women or minority groups based on historical data, perpetuating existing inequalities (European Union Agency for Fundamental Rights, 2022). A study by the World Economic Forum predicts that millions of jobs may be lost due to AI by 2030 ?(World Economic Forum, 2020). Finally, As AI systems play a more prominent role in decision-making, the issue of accountability becomes more complex. When AI makes harmful decisions, it is difficult to determine who should be held responsible. The complexity and opacity of AI algorithms add to this challenge. With AI becoming more autonomous, traditional notions of accountability may no longer apply. New legal and ethical frameworks are needed to ensure proper oversight of AI technologies.

Discussion Rubric
Your active participation in the discussion forums is essential to your overall success. Discussion questions are designed to help you make
meaningful connections between the course content and the larger concepts and goals of the course. These discussions offer you the opportunity to
express your own thoughts, ask questions for clarification, and gain insight from your classmates’ responses and instructor’s guidance.
Requirements for Discussion Board Assignments
Students are required to post one (1) initial post due by ​day three (3)​ (unless otherwise noted by the instructor) and to follow up with at
least two (2) response posts by ​day seven (7)​ for each discussion board assignment. Please be sure to post on at least two separate
days.
For your initial post (1), you must do the following:
● Compose a post of one to two paragraphs.
● Take into consideration material such as course content and other discussion boards from the current module and previous modules, when
appropriate. ​(Make sure you are using proper citation methods when referencing scholarly or popular resources.)
For your response posts (2), you must do the following:
● Reply to at least two different classmates outside of your own initial post thread.
● Demonstrate more depth and thought in your responses.
Instructor Feedback:​ This activity uses an integrated rubric in Blackboard.
Critical Elements
Comprehension
Timeliness
Engagement
Writing
(Mechanics)
Satisfactory​(100%)
Develops an initial post with
an organized, clear point of
view or idea using rich and
significant detail.
Submits initial post by day
three (3).
Proficient (85%)
Develops an initial post with
a point of view or idea using
adequate organization and
detail.
Submits initial post one day
late (day four).
Not Evident (0%)
Does not develop an initial
post with an organized point
of view or idea.
Provides relevant response
posts with some explanation
and detail.
Needs Improvement (55%)
Develops an initial post with
a point of view or idea but
with some gaps in
organization and detail.
Submits initial post two or
more days late (day five or
later).
Provides somewhat relevant
response posts with some
explanation and detail.
Provides relevant and
meaningful response posts
with clarifying explanation
and detail.
Writes posts that are easily
understood, clear, and
concise using proper citation
methods where applicable
with no errors in citations.
Writes posts that are easily
understood using proper
citation methods where
applicable with few errors in
citations.
Writes posts that are
understandable using proper
citation methods where
applicable with a number of
errors in citations.
Value
40
Does not submit a post.
10
Provides response posts that
are generic with little
explanation or detail.
30
Writes posts that others are
not able to understand and
does not use proper citation
methods where applicable.
20
Total
100%