“The Story of an Hour,”

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If there’s one thing every story needs; it’s conflict! And while conflict takes many
forms, some of literature’s most beloved stories involve conflict in the classic form of
the protagonist and antagonist.
• A protagonist is the central character or leading figure in a narrative/story. A
protagonist is sometimes a “hero” to the audience or readers.
• An antagonist is a character, or a group of characters, which stands in
opposition to the main character.
• The protagonist works toward the central story goals, while the antagonist
works against the goals.
For this week’s Discussion Board I would like you to:
1. Identify the protagonist and the antagonist in each of the 3 stories we read and
give brief examples to back up your choices.
2. Post which of the three stories you liked the best and briefly explain why.
tamplet:
1. “The Story of an Hour,” p. 48, (4th ed.)
Protagonist:
Why?
Antagonist:
Why?
2. “The Lottery,” p. 216, (4th ed.)
Protagonist:
Why?
Antagonist:
Why?
3. “Hills Like White Elephants,” p. 176 (4th ed.)
Protagonist:
Why?
Antagonist:
Why?
4. Favorite Story:
Why?

 

Sample Solution

t is shown in a study by P. J. Phillips [22] that algorithms developed in East Asia recognized Asian faces far more accurately than Caucasian faces. The exact opposite was true for algorithms developed in Europe and the United states. This implies that the conditions in which an algorithm is created can influence the accuracy of its results. A possible explanation for this is that the developer of an algorithm may program it to focus on facial appearances that are more easily distinguishable in some races than in others [10][22].

It is not only in the way the algorithm is programmed. It is also in the way the algorithm is trained. It is possible that a certain algorithm has more experience with Asian faces than with Caucasian faces. This unfair representation of the population which the algorithm might me used on, will lead to problems. If you do not include many images from one ethnic subgroup, it won’t perform too well on those groups because Artificial Intelligence learns from the examples it was trained on [19][22].

In conclusion, the performance of face recognition algorithms suffers from a racial or ethnic bias. The demographic origin of the algorithm, and the demographic structure of the test population has a big influence on the accuracy of the results of the algorithm. This bias is particularly unsettling in the context of the vast racial disparities that already exist in the arrest rates [22][10].

iii. System still needs a human judge
The last problem that will be discussed in this paper is that the technologies that are existing today are far from perfect. Right now, companies are advertising their technologies as “a highly efficient and accurate tool with an identification rate above 95 percent.” (said by Facefirst.) In reality, these claims are almost impossible to verify. The facial-recognition algorithms used by police are not enforced to go th

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