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I've been using AI as a utility to analyze discussions with other users and to determine who has the objectively better argument. I have it compare fallacies used in both quantity and severity.
Particularly with Trump supporters it's no surprise the ratio is usually 3 or 4:1.
It could be used in this context against AI, too, because the directive isn't to persuade but to analyze logic.
All that means is the left is primarily the populous option, or at least whoever sifted through the training data is predominantly left wing. AI is a statics machine and it will give you the answers it mostly received in training. If the most popular answer fed into it's training was right wing like in your other answers it would show up more right biased, in other words AI is the ultimate average being and as we all know the average man can be pretty dumb.
Feel free to run this through your AI to evaluate the truthfulness.
Does it? This is in fact just as speculative; after all, it could also mean that the left is primarily the more logically-accurate sample. Alternatively it could mean the data sets from linguistics and critical-thinking books are inherently antithetical to the "poorly educated" conservatives (Trump's words, not mine). Besides, I venture a guess that if this data is pulled from the vastness of the internet that includes the likes of 4chan, facebook, breitbart, fox news, and so on... Then I'm going to take a wild guess and say that the conservative sets are pretty well represented.
Not that they're perfect, but yes, it all depends on the data sets through which they're evaluated. Nevertheless, logic is logic and one of the easier things to parse for these machine-learning tools. Imperfect though they may, if they can code a Python application, then they can recognize that 2 + 2 != 5 just the same within linguistics.
Not the end-all solution, of course, but merely a diagnostic utility in the toolbox to help thwart fallacious thinking and gaslighting. This Software Engineer shall continue to use it absent of a better argument, thank you very much.