ChatGPT Summaries
Human Intelligence
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~ 100 word summary of articles in YOUR words |
Although AI algorithms
are constructed to be impartial, biased data produces skewed responses. Much
of the time, this bias is introduced accidently by programmers who do not recognize
discriminatory data. Computer algorithms are often based on categorization, or
identifying and sorting inputs into specific groups. Groups that are underrepresented
in the inputted data lead to algorithms that are biased against these groups,
perpetuating discrimination. For instance, facial recognition software performs
most efficiently with white male faces, indicating that the inputted data was
biased. As AI increasingly projects its influence over the world, this
understanding of its biases is crucial to ending discrimination. |
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Artificial Intelligence (Round 1)
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Instructions you gave ChatGPT |
Write
a 100-word summary of these two articles:
https://time.com/5520558/artificial-intelligence-racial-gender-bias/Links to
an external site.
https://www.vox.com/recode/2020/2/18/21121286/algorithms-bias-discrimination-facial-recognition-transparencyLinks
to an external site. |
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ChatGPT response |
Article 1:
"Artificial Intelligence Has a Bias Problem, and It's Our Fault"
(Time) The article
highlights the pervasive issue of racial and gender bias in artificial
intelligence (AI) systems. It emphasizes that these biases are not inherent
in AI but are a reflection of the biases present in the data used to train
these systems. The article discusses real-life examples where AI has
exhibited discriminatory behavior, such as facial recognition software
misidentifying individuals from minority groups. It emphasizes the need for
diversity and inclusivity in AI development teams and data collection processes
to mitigate biases. The article calls for collective responsibility and
proactive measures to address the bias problem. Article 2:
"Algorithms Have a Bias Problem, and Transparency Is the Solution"
(Vox) This article
focuses on the issue of bias in algorithms, particularly in the context of
facial recognition technology. It discusses the inherent limitations and
biases present in these systems, leading to misidentification and
discrimination, especially against marginalized communities. The article
advocates for increased transparency and scrutiny of algorithms, emphasizing
the need to understand their decision-making processes. It calls for
regulations and independent audits to ensure algorithmic accountability and
mitigate biases. The article highlights the importance of public awareness
and engagement in holding algorithm creators and users accountable for
addressing bias-related concerns. |
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Artificial Intelligence (Round 2)
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Instructions you gave ChatGPT |
Write
a 100-word combined summary of these two articles (1 response total):
https://time.com/5520558/artificial-intelligence-racial-gender-bias/Links to
an external site.
https://www.vox.com/recode/2020/2/18/21121286/algorithms-bias-discrimination-facial-recognition-transparencyLinks
to an external site. |
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ChatGPT response |
The combined
summary of the two articles reveals the common theme of bias in artificial
intelligence (AI) and algorithms. The articles highlight the presence of
racial and gender biases in AI systems, which stem from the biases present in
the data used to train them. Instances of misidentification and
discrimination, particularly in facial recognition technology, are discussed.
Both articles stress the need for diversity in AI development teams and data
collection processes to address biases effectively. Transparency and
accountability are key solutions proposed, emphasizing the importance of
understanding algorithmic decision-making and implementing regulations and
independent audits to mitigate biases and ensure fairness. |
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The Turing Test
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~ 200 word discussion |
The first AI
generated responses were more sophisticated than my writing. Chat GPT, learning
from objective summaries on the Internet, used professional writing practices
taught in English class, such as the use of the active voice instead of the
passive voice, as well as elevated vocabulary that accurately reflected the
ideas of the article authors. However, it responded more rigidly to my
request for a combined summary, and the syntax was unoriginal and showed
slight repetition. For instance, each sentence had a tendency to start with “the
article”, and sentences were more likely to start with an independent clause.
The style and tone of the two responses was remarkably similar, as I had only
changed 1 word in the directions given for 2 responses (specified 1 summary
instead of 2). Wanting to test its limits, I instructed ChatGPT to write in a
“inspirational, slightly offended, hopeful, but otherwise polite and
objective tone”, seeking for it emulate the style of the Times
article. I noted a few changes in its response: more figurative language (“rooted
in biases”, “glimmer of hope”) was used, and the last ended in the first
person, a common writing technique. These changes, though subtle, illustrate
the frightening potential of AI to produce work that is indistinguishable from
that of humans. With more complicated algorithms and more data, the future for
writers looks bleak. |
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