Talk:Black Box Algorithms

The article meets the word count requirement at 1118 words (including headers).
There is an introduction paragraph to summarize the issue. This introduction provides great insight into the ethical debate regarding Black Box Algorithms, but it could discuss their definition of these algorithms in more detail. Additionally, it may benefit from providing examples of some black box algorithms in use (Chat GPT, Tik Tok Recomender, etc). Additionally, some information in the introduction needs to have corresponding citations.
There are multiple body paragraphs in the article. The Examples section goes into great detail regarding the COMPAS algorithm. The text related to the Loomis v. Washington case is not necessarily directly related to Black Box Algorithms. It may be helpful to have two separate sections. One of these could relate to Black Box Algorithms in use (Google, COMPAS, etc.), and the other could relate to instances of Black Box Algorithms in the news where you discuss the court case and other incidents related to the use of these algorithms. Additionally, the Ethical Concerns section discusses some ethical considerations regarding Black Box Algorithms in-depth. However, there is significantly less information in the article regarding why black box algorithms are helpful. I am not sure if that content is in your future plans, but without it, the article could be seen as biased by only depicting the views of one perspective of the topic.
Not all statements have corresponding references. I am not familiar with Arimetrics, but it is neither a mainstream publication nor an academic paper, so you may benefit from finding a different source there. You include sources from the MIT Press, Harvard Law Review, Duke Research Blog, Harvard Business Review, the Journal of Medical Ethics, and the Michigan Telecommunications and Technology Law Review which are all reliable sources.
After reading through the article, I am familiar with examples of black box algorithms and associated ethical concerns, but the article could benefit from more information about what black box algorithms are, how they are created, and why many companies choose to use them.
As mentioned earlier, the article currently does not go into detail about why companies would choose to implement their machine learning algorithms as black box algorithms, and without this information, the article could be construed as biased.