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Week4 ethics discussion | Social Science homework help

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Week 4 Discussion: Utilitarianism

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Required Resources
Read/review the following resources for this activity:

  • Textbook: Chapters 7, 8
  • Lesson
  • Minimum of 1 scholarly source (in addition to the textbook)

Initial Post Instructions
The principle of utility involves maximizing happiness as a desirable outcome of decisions. Although it does not get directly said, there is an inverse intention to minimize the undesirable outcome of disaster. Utilitarian decisions are directed toward outcomes—that is, the consequences of decisions.

We need to look at results. We first look at the actual results of an action. We judge if it was the best possible result. We can judge the actual results in comparison to other results that reasonably could be said to have been possible.

If we do not yet have the actual results of an action, we do not know if it is moral or not. We can talk hypothetically about what might happen, and then what that would show about the morality of an action. However, if we do not know what the action had as its consequences, we cannot yet say if it is moral or not.

Initial Post Instructions
For the initial post of this week’s discussion respond to one of the following options, and label the beginning of your post indicating either Option 1, Option 2, or Option 3:

Option 1: You are a nurse on a floor with only elderly patients. Every day, each patient tells you about how much pain they are in and asks you to help them. They want you to inject them with something to end their lives. If the patients die, the beds on that floor would be freed up for other patients. The hospital is at 100 percent capacity. There is no other hospital for 30 miles. Other patients may be not receiving care due to a lack of free beds. What is the moral thing to do here? Why is that the moral thing to do? What would an utilitarian say is the moral thing to do? Why would they say that? Compare and contrast the utilitarian approach with that of an ethical egoist or social contact theorist

Option 2: A new social media app is offering itself to you for free. If you upload a picture to it, the app will show how you will look at 10 years. John Doe, a friend of yours, says not to use the app as it will then possess your biometric facial data. Jane Doe, another friend of yours, says that she heard the app shares the facial data with a security firm that helps the government detect terrorists at airports. Should you use this app? Why or why not? If John Doe is right, would an utilitarian say it is right to use the app? Why or why not?  If Jane Doe is right, would a social contract theorists say it is right to use the app? Consider the role the Fourth Amendment at play here.

Option 3: You are a nursing student at the XYZ College. It has a 50 percent acceptance rate (half the applicants do not get in). XYZ is a public college. XYZ has decided to implement an affirmative action policy. The college has few students over the age of 50. To encourage more students of that age, every student 50 or older will receive a bonus point. A student’s admission is dependent on having 11 points. One earns points for a GPA above a certain score, ACT/SAT score above a certain number, having a letter of recommendation, etc. XYZ also lacks LGBT students, Muslim, and African-American students and is considering offering a bonus point for any student fitting those categories. What is the key moral conflict for XYZ? What social values should XYZ promote here? What diverse populations are involved here, and what are their interests? Do you think XYZ’s social action is the correct solution to lack of diversity? Why or why not? Factor the ethics of egoism and utilitarianism into your answer.

Follow-Up Post Instructions
Respond to at least one peer. If possible, respond to one peer who chose an option different than the one you chose. Further the dialogue by providing more information and clarification. Make sure that you add additional information and not repeat the same information already posted on the discussion board as you further the dialogue.

Writing Requirements

  • Minimum of 2 posts (1 initial & 1 follow-up)
  • Minimum of 2 sources cited (assigned readings/online lessons and an outside scholarly source)
  • APA format for in-text citations and list of references

Answer 1:

 

I chose option number two: An app that is potentially using your biometric facial data for unknown purposes and/or acquires the data for a security firm geared at detecting terrorists. This is an interesting question of ethics because it is a violation of human privacy but could potentially be used for a reasonable purpose.

The fourth amendment essentially is the right to privacy and security against unreasonable search and seizures (U.S. Const. amend. IV). Under this amendment the collection of peoples private information without explicit cause, including physical information such as biometric data, could be interpreted as a violation of rights. And I agree with that viewpoint. People have a right to retain privacy over all aspects of themselves including intellectually and bodily, and as such any collection of this data without explicit permission from the individual and proper cause for doing so is immoral. Even if the data were to be used for “good reason” who is to say such technology and information would always be used for “the greater good” and would not fall into the hands of people or corporations that would use it for nefarious purposes. People have a right to know their information is private.

A Utilitarian believes that “That principle requires us, in all circumstances, to “maximize happiness”—in other words, to produce the greatest total balance of happiness over unhappiness, or of pleasure over suffering” (Rachels & Rachels, 2019). Fundamentally a utilitarian sees anything that is unpleasant as unjust, however just taking a photo for fun to see a potential age progression of yourself is in no way unpleasant, regardless of the potential for biometric data to be taken from the photo. And as such I feel as though a utilitarian would not have an issue with the app.

The social contract theorist believes “To escape the state of nature, we must find a way to work together. In a stable and cooperative society, we can produce more essential goods and distribute them in a rational way” (Rachels & Rachels, 2019). Because of this I believe that a social contract theorist would believe that the reward of potentially catching terrorists outweighs the violation of privacy and a cooperative approach would be the most beneficial.

Answer 2:

I choose option 1

If the patient would like to end their life, it would be best to consult with the doctor and the doctor will be the best solution for the patient. Furthermore, what I can do in this case is to ask the doctor to increase the dose of pain medication to help the patient feels more comfortable. In this case scenario, I would not grant the patient’s wish to end their lives because it will be a violation of the code of nursing ethics and against the law.

Utilitarianism is a theory of morality, “which advocates actions that foster happiness or pleasure and opposed actions that cause unhappiness or harm” (Tardi, 2020). Utilitarians would say in order to make the patient suffering from an illness and be happy it would be best to give them an injection to end their life. In addition, a utilitarian would also mention ending this patient’s life to make the other patient receive the best care. I would not agree with the utilitarian.

Secondly, the concept of ethical egoism is “each person ought to pursue his or her own self-interest exclusively” (Rachel & Rachel, 2019). In this case, the patient acting on their own interests by asking the nurse to end their lives but not considering the consequences the nurse will encounter.

Thirdly, the idea of social contract theory is this “morality consists of the set of rules, governing behavior, that rational people will accept, on the condition that others accept them as well” (Rachel & Rachel, 2019). The social contract theorist would allow the nurse to inject the medical to the patient to end their life in order to save other patients who are waiting to get treatment.

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July 1, 2023
    

Project 3 - Ensemble Methods and Unsupervised Learning

In this project you will explore some techniques in unsupervised learning
as well as ensemble methods. It is important to realize that understanding
an algorithm or technique requires understanding how it behaves under a
variety of circumstances. You will go through the process of choosing and
exploring two classification datasets, tuning the algorithms you have
learned about, writing a thorough analysis of your findings, and presenting
your findings. The most crucial part of this assignment is the analysis and
your ability to explain and justify your results.

I. Choosing Datasets

The first task in this assignment is choosing two interesting classification
datasets, these can be binary or multiclass. The features can be of any
type, and it is recommended that you choose datasets with diverse feature
sets. I don’t care where you get the data from. You can download some,
take some from your own research, or make some up on your own. What I
do care about is that the datasets must be interesting. They should
contain a decent amount of features and a sufficiently large amount of
examples. Do not choose an “easy” dataset, however don’t go crazy either
trying to find the perfect one. Your two datasets should also differ in some
way such that you can compare and contrast your results between the
two. You should also be following standard machine learning practice by
splitting your dataset into training and testing, and only touching the
testing dataset at the very end when you are ready to report results. (Cross
validation is highly recommended).

II. Coding (10%)

After choosing your datasets you will now be tasked with writing code to apply
the machine learning algorithms you have learned about. Your code must be
written in python, but you may use any libraries that have already implemented
the machine learning algorithms (e.g scikit-learn). You are not expected to code
the algorithms from scratch, and in fact I would highly discourage it. What you
may not do is copy code from the internet. Below are the analyses you are
required to run.

1) Run K-means and Hierarchical Clustering on your datasets and analyze
what you observe.

2) Run two dimensionality reduction algorithms (PCA and UMAP) on your
datasets. Observe and analyze the results.

3) Re-run the K-means and Hierarchical Clustering on your dimensionality
reduced datasets and compare the results to part (1).

4) Tune and train two ensemble models (AdaBoost and Random Forests) on
both your original and dimensionality reduced datasets. Compare and
analyze the results.

Your code does not have to be pretty or well written. However, it must be written
in python and I must be able to run one script (main.py) that will produce all the
results and figures in your report.

III. Report (80%)

You will then produce a report describing and analyzing your methods and
results. Here you will describe the datasets you have chosen and why they are
interesting. You will then provide an analysis on how the different machine
learning algorithms performed on each dataset. The report must be limited to 10
pages maximum. Plots and figures are highly recommended. It is up to you
how you wish to demonstrate your understanding of the machine learning
algorithms you have explored, but below I have listed some potential ideas for
analysis and items you may wish to include in the report.

• A description of your two datasets and why you feel that they are interesting.

• Hypotheses on how you believe the learning algorithms will perform on each

dataset and why.

• How you dealt with different features in your datasets? missing data? different

scalings?

• Training and testing error rates you obtained for your various learning

algorithms (some sort of cross validation is highly recommended)

• The effect of hyperparameters on performance

• Comparing and contrasting results between datasets

• Comparing and contrasting results between learning algorithms

• Training and testing error rates as a function of training dataset size

• Timing analysis of how long it takes to train/test each algorithm

• Conclusions

• Ideas for future analyses

• What you may have done differently

• References

You are NOT being graded on how well the algorithms perform on your datasets.
What is most important is WHY? You should be explaining and justifying all of
your figures and results, and demonstrating that you understand the intricate
details of the machine learning process, and the machine leaning algorithms you
are using.

IV. Presentation (10%)

Finally you will give a maximum 7 minute presentation of your results (You will be
cut off exactly at the 7 minute mark). In this presentation you will describe your
datasets, your methods, and any interesting results you found!

What to turn in?

Below is a list of items you will be required to turn in via canvas. Please make
sure all documents are named as described bellow.

• report.pdf - Your maximum 10 page report in pdf format. Do not use super
tiny or large font. No specific formatting is required but use common sense.

• presentation.pptx or presentation.key - Your presentation slides either in a
powerpoint or keynote document.

• code.zip - A zip file with all of the code you have written. Within the folder
there should a file called README.txt that contains instructions on how to run
your code, and a python file called main.py that will produce all figures and
plots in your report/presentation. I should be able to reproduce your results
easily.

• data.zip - A zip file that contains the two datasets you have chosen.

Grading

You are being scored on your analysis more than anything else. Roughly
speaking, implementing everything and getting it to run is worth very little for
this assignment. Of course though, analysis without proof of working code
makes the analysis suspect. The key thing is that your explanations should be

both thorough and concise, and your analysis should prove to me you have a
deep understanding of the machine learning process and the machine learning
algorithms you are using.

default image
July 1, 2023
    

Project 3 - Ensemble Methods and Unsupervised Learning

In this project you will explore some techniques in unsupervised learning
as well as ensemble methods. It is important to realize that understanding
an algorithm or technique requires understanding how it behaves under a
variety of circumstances. You will go through the process of choosing and
exploring two classification datasets, tuning the algorithms you have
learned about, writing a thorough analysis of your findings, and presenting
your findings. The most crucial part of this assignment is the analysis and
your ability to explain and justify your results.

I. Choosing Datasets

The first task in this assignment is choosing two interesting classification
datasets, these can be binary or multiclass. The features can be of any
type, and it is recommended that you choose datasets with diverse feature
sets. I don’t care where you get the data from. You can download some,
take some from your own research, or make some up on your own. What I
do care about is that the datasets must be interesting. They should
contain a decent amount of features and a sufficiently large amount of
examples. Do not choose an “easy” dataset, however don’t go crazy either
trying to find the perfect one. Your two datasets should also differ in some
way such that you can compare and contrast your results between the
two. You should also be following standard machine learning practice by
splitting your dataset into training and testing, and only touching the
testing dataset at the very end when you are ready to report results. (Cross
validation is highly recommended).

II. Coding (10%)

After choosing your datasets you will now be tasked with writing code to apply
the machine learning algorithms you have learned about. Your code must be
written in python, but you may use any libraries that have already implemented
the machine learning algorithms (e.g scikit-learn). You are not expected to code
the algorithms from scratch, and in fact I would highly discourage it. What you
may not do is copy code from the internet. Below are the analyses you are
required to run.

1) Run K-means and Hierarchical Clustering on your datasets and analyze
what you observe.

2) Run two dimensionality reduction algorithms (PCA and UMAP) on your
datasets. Observe and analyze the results.

3) Re-run the K-means and Hierarchical Clustering on your dimensionality
reduced datasets and compare the results to part (1).

4) Tune and train two ensemble models (AdaBoost and Random Forests) on
both your original and dimensionality reduced datasets. Compare and
analyze the results.

Your code does not have to be pretty or well written. However, it must be written
in python and I must be able to run one script (main.py) that will produce all the
results and figures in your report.

III. Report (80%)

You will then produce a report describing and analyzing your methods and
results. Here you will describe the datasets you have chosen and why they are
interesting. You will then provide an analysis on how the different machine
learning algorithms performed on each dataset. The report must be limited to 10
pages maximum. Plots and figures are highly recommended. It is up to you
how you wish to demonstrate your understanding of the machine learning
algorithms you have explored, but below I have listed some potential ideas for
analysis and items you may wish to include in the report.

• A description of your two datasets and why you feel that they are interesting.

• Hypotheses on how you believe the learning algorithms will perform on each

dataset and why.

• How you dealt with different features in your datasets? missing data? different

scalings?

• Training and testing error rates you obtained for your various learning

algorithms (some sort of cross validation is highly recommended)

• The effect of hyperparameters on performance

• Comparing and contrasting results between datasets

• Comparing and contrasting results between learning algorithms

• Training and testing error rates as a function of training dataset size

• Timing analysis of how long it takes to train/test each algorithm

• Conclusions

• Ideas for future analyses

• What you may have done differently

• References

You are NOT being graded on how well the algorithms perform on your datasets.
What is most important is WHY? You should be explaining and justifying all of
your figures and results, and demonstrating that you understand the intricate
details of the machine learning process, and the machine leaning algorithms you
are using.

IV. Presentation (10%)

Finally you will give a maximum 7 minute presentation of your results (You will be
cut off exactly at the 7 minute mark). In this presentation you will describe your
datasets, your methods, and any interesting results you found!

What to turn in?

Below is a list of items you will be required to turn in via canvas. Please make
sure all documents are named as described bellow.

• report.pdf - Your maximum 10 page report in pdf format. Do not use super
tiny or large font. No specific formatting is required but use common sense.

• presentation.pptx or presentation.key - Your presentation slides either in a
powerpoint or keynote document.

• code.zip - A zip file with all of the code you have written. Within the folder
there should a file called README.txt that contains instructions on how to run
your code, and a python file called main.py that will produce all figures and
plots in your report/presentation. I should be able to reproduce your results
easily.

• data.zip - A zip file that contains the two datasets you have chosen.

Grading

You are being scored on your analysis more than anything else. Roughly
speaking, implementing everything and getting it to run is worth very little for
this assignment. Of course though, analysis without proof of working code
makes the analysis suspect. The key thing is that your explanations should be

both thorough and concise, and your analysis should prove to me you have a
deep understanding of the machine learning process and the machine learning
algorithms you are using.

default image
July 1, 2023
    

Project 3 - Ensemble Methods and Unsupervised Learning

In this project you will explore some techniques in unsupervised learning
as well as ensemble methods. It is important to realize that understanding
an algorithm or technique requires understanding how it behaves under a
variety of circumstances. You will go through the process of choosing and
exploring two classification datasets, tuning the algorithms you have
learned about, writing a thorough analysis of your findings, and presenting
your findings. The most crucial part of this assignment is the analysis and
your ability to explain and justify your results.

I. Choosing Datasets

The first task in this assignment is choosing two interesting classification
datasets, these can be binary or multiclass. The features can be of any
type, and it is recommended that you choose datasets with diverse feature
sets. I don’t care where you get the data from. You can download some,
take some from your own research, or make some up on your own. What I
do care about is that the datasets must be interesting. They should
contain a decent amount of features and a sufficiently large amount of
examples. Do not choose an “easy” dataset, however don’t go crazy either
trying to find the perfect one. Your two datasets should also differ in some
way such that you can compare and contrast your results between the
two. You should also be following standard machine learning practice by
splitting your dataset into training and testing, and only touching the
testing dataset at the very end when you are ready to report results. (Cross
validation is highly recommended).

II. Coding (10%)

After choosing your datasets you will now be tasked with writing code to apply
the machine learning algorithms you have learned about. Your code must be
written in python, but you may use any libraries that have already implemented
the machine learning algorithms (e.g scikit-learn). You are not expected to code
the algorithms from scratch, and in fact I would highly discourage it. What you
may not do is copy code from the internet. Below are the analyses you are
required to run.

1) Run K-means and Hierarchical Clustering on your datasets and analyze
what you observe.

2) Run two dimensionality reduction algorithms (PCA and UMAP) on your
datasets. Observe and analyze the results.

3) Re-run the K-means and Hierarchical Clustering on your dimensionality
reduced datasets and compare the results to part (1).

4) Tune and train two ensemble models (AdaBoost and Random Forests) on
both your original and dimensionality reduced datasets. Compare and
analyze the results.

Your code does not have to be pretty or well written. However, it must be written
in python and I must be able to run one script (main.py) that will produce all the
results and figures in your report.

III. Report (80%)

You will then produce a report describing and analyzing your methods and
results. Here you will describe the datasets you have chosen and why they are
interesting. You will then provide an analysis on how the different machine
learning algorithms performed on each dataset. The report must be limited to 10
pages maximum. Plots and figures are highly recommended. It is up to you
how you wish to demonstrate your understanding of the machine learning
algorithms you have explored, but below I have listed some potential ideas for
analysis and items you may wish to include in the report.

• A description of your two datasets and why you feel that they are interesting.

• Hypotheses on how you believe the learning algorithms will perform on each

dataset and why.

• How you dealt with different features in your datasets? missing data? different

scalings?

• Training and testing error rates you obtained for your various learning

algorithms (some sort of cross validation is highly recommended)

• The effect of hyperparameters on performance

• Comparing and contrasting results between datasets

• Comparing and contrasting results between learning algorithms

• Training and testing error rates as a function of training dataset size

• Timing analysis of how long it takes to train/test each algorithm

• Conclusions

• Ideas for future analyses

• What you may have done differently

• References

You are NOT being graded on how well the algorithms perform on your datasets.
What is most important is WHY? You should be explaining and justifying all of
your figures and results, and demonstrating that you understand the intricate
details of the machine learning process, and the machine leaning algorithms you
are using.

IV. Presentation (10%)

Finally you will give a maximum 7 minute presentation of your results (You will be
cut off exactly at the 7 minute mark). In this presentation you will describe your
datasets, your methods, and any interesting results you found!

What to turn in?

Below is a list of items you will be required to turn in via canvas. Please make
sure all documents are named as described bellow.

• report.pdf - Your maximum 10 page report in pdf format. Do not use super
tiny or large font. No specific formatting is required but use common sense.

• presentation.pptx or presentation.key - Your presentation slides either in a
powerpoint or keynote document.

• code.zip - A zip file with all of the code you have written. Within the folder
there should a file called README.txt that contains instructions on how to run
your code, and a python file called main.py that will produce all figures and
plots in your report/presentation. I should be able to reproduce your results
easily.

• data.zip - A zip file that contains the two datasets you have chosen.

Grading

You are being scored on your analysis more than anything else. Roughly
speaking, implementing everything and getting it to run is worth very little for
this assignment. Of course though, analysis without proof of working code
makes the analysis suspect. The key thing is that your explanations should be

both thorough and concise, and your analysis should prove to me you have a
deep understanding of the machine learning process and the machine learning
algorithms you are using.

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Journal article, 20 pages
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August 5, 2021
    

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Political Science, 6 pages
Admission Essay
August 5, 2021
    

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Tourism, 1 page
Discussion Essay
August 5, 2021
    

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Philosophy, 5 pages
Report
August 5, 2021
    

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Psychology, 4 pages
Essay (any type)
August 5, 2021
    

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customer #17823
Business, 8 pages

Calculate the price of your order

550 words
We'll send you the first draft for approval by September 11, 2018 at 10:52 AM
Total price:
$26
The price is based on these factors:
Academic level
Number of pages
Urgency
Basic features
  • Free title page and bibliography
  • Unlimited revisions
  • Plagiarism-free guarantee
  • Money-back guarantee
  • 24/7 support
On-demand options
  • Writer’s samples
  • Part-by-part delivery
  • Overnight delivery
  • Copies of used sources
  • Expert Proofreading
Paper format
  • 275 words per page
  • 12 pt Arial/Times New Roman
  • Double line spacing
  • Any citation style (APA, MLA, Chicago/Turabian, Harvard)

Our guarantees

Delivering a high-quality product at a reasonable price is not enough anymore.
That’s why we have developed 5 beneficial guarantees that will make your experience with our service enjoyable, easy, and safe.

Money-back guarantee

You have to be 100% sure of the quality of your product to give a money-back guarantee. This describes us perfectly. Make sure that this guarantee is totally transparent.

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Zero-plagiarism guarantee

Each paper is composed from scratch, according to your instructions. It is then checked by our plagiarism-detection software. There is no gap where plagiarism could squeeze in.

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Free-revision policy

Thanks to our free revisions, there is no way for you to be unsatisfied. We will work on your paper until you are completely happy with the result.

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Privacy policy

Your email is safe, as we store it according to international data protection rules. Your bank details are secure, as we use only reliable payment systems.

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Fair-cooperation guarantee

By sending us your money, you buy the service we provide. Check out our terms and conditions if you prefer business talks to be laid out in official language.

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