Difference between revisions of "Talk:Machine Learning Underlying Technology and Ethical Issues"

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Length of article is at roughly 2800 words, without counting the content block. I see that you have a couple subheadings under "Real World Impacts of Bias and Unfairness in Machine Learning" which hasn't been written about yet. The subheadings look like they could be expanded into decent size paragraphs so you should be good on word count then.  
 
Length of article is at roughly 2800 words, without counting the content block. I see that you have a couple subheadings under "Real World Impacts of Bias and Unfairness in Machine Learning" which hasn't been written about yet. The subheadings look like they could be expanded into decent size paragraphs so you should be good on word count then.  
  
The article does contain the 3 major components of a good article. // I will continue later, hopefully I'll be done by 2.30pm today :)
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The article does contain the 3 major components of a good article. The topic is summarized effectively in the opening paragraph, however I felt that it contained too much information and lacked (nearby) citations. Specifically, the details of gender bias for the Apple Card felt out of place, while the assertion of facts such as "The field of machine learning has grown significantly since the 1970s due to the rapid increase in computational power as well as the decreased costs", and "Conversations about the ethics of machine learning are becoming more widespread and there has been an increase in research into mitigation strategies", among others, seem to lack supporting citations. In particular, the assertion that computational power (and lower costs) was the only reason for growth since the 70s is contradictory to the later section explaining the AI Winter- when researchers/funders lost interest in the subject, and later regained interest (fueling growth) when new research did emerge.
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The article is also split into multiple logical sections, but I feel that the subheadings on "Bias in Neural Networks" and "Methods to Limit Bias in Neural Networks" should be under a "Bias in ML" heading rather than under ML algorithms, as these are, based on the article (and from my EECS445 experience), parameter tuning techniques based on the AUROC graph/Grad-CAM and usually applied post-training.
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Citations and references are present throughout the article, but feel lacking in some paragraphs, and most of the references seem to be from reliable journals/websites. Many of the references are repeated, you can name a tag by using \<ref name=xyz></ref> and refer to the same reference multiple times without repeats showing up at the bottom.
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// I will continue later, hopefully I'll be done by 2.30pm today :)

Revision as of 17:30, 4 February 2022

Length of article is at roughly 2800 words, without counting the content block. I see that you have a couple subheadings under "Real World Impacts of Bias and Unfairness in Machine Learning" which hasn't been written about yet. The subheadings look like they could be expanded into decent size paragraphs so you should be good on word count then.

The article does contain the 3 major components of a good article. The topic is summarized effectively in the opening paragraph, however I felt that it contained too much information and lacked (nearby) citations. Specifically, the details of gender bias for the Apple Card felt out of place, while the assertion of facts such as "The field of machine learning has grown significantly since the 1970s due to the rapid increase in computational power as well as the decreased costs", and "Conversations about the ethics of machine learning are becoming more widespread and there has been an increase in research into mitigation strategies", among others, seem to lack supporting citations. In particular, the assertion that computational power (and lower costs) was the only reason for growth since the 70s is contradictory to the later section explaining the AI Winter- when researchers/funders lost interest in the subject, and later regained interest (fueling growth) when new research did emerge.

The article is also split into multiple logical sections, but I feel that the subheadings on "Bias in Neural Networks" and "Methods to Limit Bias in Neural Networks" should be under a "Bias in ML" heading rather than under ML algorithms, as these are, based on the article (and from my EECS445 experience), parameter tuning techniques based on the AUROC graph/Grad-CAM and usually applied post-training.

Citations and references are present throughout the article, but feel lacking in some paragraphs, and most of the references seem to be from reliable journals/websites. Many of the references are repeated, you can name a tag by using \[1] and refer to the same reference multiple times without repeats showing up at the bottom.




// I will continue later, hopefully I'll be done by 2.30pm today :)
  1. Cite error: Invalid <ref> tag; no text was provided for refs named xyz