Possible correction: this does not appear to be an example of machine bias. It's also important to keep in mind that there can be other sources (such as brittleness) of bad ML outcomes than bias.
When I do an exact search for the Justification Sentence with Google, what best matches is a quote by Rajiv Gandhi. The relevant context is: "History is full of such prejudices paraded as iron laws"
His stance is clearly opposite to what the extracted text implies. This is a common problem with knowledge extraction and one I've run into often myself.
Extracting just a phrase, or utterances of a generative model cannot be trusted because the original meaning can be opposite to what is presented. Existing models fail to preserve nuance imparted by context, struggle with negation, lack deep understanding and an ability to truly reason.
I remember a teacher avoided spelling mistakes on the black board and simply wrote the correct form on the black board, lest pupils misremember the wrong form. That might sound obvious, but the context was a talk about mistakes made in exercises.
It's really hard not to mention negatives to illustrate contrast.
In other words: Some people need to learn to speak constructively. An AI would do best ignoring negative remarks and simply learning provable facts (instead of faking understanding by simply echoing a quote out of context -- see there I wrote redundant information).
I wonder whether anyone would agree that the above quote was against the HN guideline to leave out dismissive remarks like ... (ha, I'm not going to repeat the specific example). Theorizing about potential referents for "such", "that", etc. must be very difficult, especially now that that that that is often used superfluously is acceptable to some.
When I do an exact search for the Justification Sentence with Google, what best matches is a quote by Rajiv Gandhi. The relevant context is: "History is full of such prejudices paraded as iron laws"
His stance is clearly opposite to what the extracted text implies. This is a common problem with knowledge extraction and one I've run into often myself.
Extracting just a phrase, or utterances of a generative model cannot be trusted because the original meaning can be opposite to what is presented. Existing models fail to preserve nuance imparted by context, struggle with negation, lack deep understanding and an ability to truly reason.