Page 20 - ChatGPT Prompts Book: Precision Prompts, Role Prompting, Training & AI Writing Techniques for Mortals

As a new technology, the extent and nature of bias
associated with AI model design have yet to be fully

documented. Although the capabilities of ChatGPT are
impressive, they may also reflect and exaggerate societal
biases. As ChatGPT is mostly trained on data crawled from
the Internet, it’s possible that the model will generate
content that contains or purports harmful stereotypes. If

the training data is skewed towards a particular
demographic, political, or geographical location, ChatGPT's
responses may reflect that bias. For example, if the training

data is biased towards male viewpoints on a certain topic,
ChatGPT will struggle to generate accurate responses for
female viewpoints on that topic.

Transparency

It's important to note that the source data used to train
ChatGPT and generate outputs is not fully transparent. As
some data may originate from online sources that aren’t

properly cited or verified, this may lead to inaccuracies,
biases, or other problems with ChatGPT’s responses. For
instance, in the case of programming and technology, you
might want to know where the model is sourcing its

information, i.e. official documentation, blogs, forum
comments, etc. If you are writing code, you also want to
know if the code ChatGPT is recommending is secure and
efficient, for example.

Commoditization of Content
While ChatGPT can generate high-quality content, it’s
important to remember that each output is based on

patterns learned from the training data. With millions of
users generating content using the same training data,
ChatGPT-generated content may skew toward reoccurring
perspectives, case studies, arguments, and phrasing. This

should lead to some level of content commoditization, with