Even including super-intelligent machines into the concept of dedomenocracy, there will remain the present-day complaint that the government needs to get lucky every day but the criminal human needs to get lucky only once. This problem will remain long after we replace democracy with dedomenocracy. The most dangerous criminal is the non-criminal who immediately acts on his newly discovered hypothesis. Even superhuman intelligent dedomenocracy may not be able to discover this hypothesis first.
When a dedomenocracy makes a decision to impose some new policy, it is just like our making a decision to employ airlines for our travel needs. We suspend our knowledge of what might go wrong by accepting the data that shows that those things rarely if ever happen.
My point is that the unexpected category is never a topic of analysis itself. There is no value in making policy based on volume of unexpected results. Instead, the unexpected category justified and directs more in depth investigation into explaining the members within that category. This should be the same same response for all of the negative categories. If the negative category draws attention, the appropriate reaction is to dive in and find out how to divide it into new positive categories so that they may support analysis of specific policies or decisions based on these categories. We have negative categories for the uninteresting elements.
When we identify a population with a label of low incomes we imply that their lives would be better if they had higher incomes. This meaning is similar to the above syllogistic fallacy of the illicit major. While there is no doubt that many poor people would desire higher incomes, there are many who choose lower incomes because of some other benefit they get from the jobs. The jobs may be less demanding, or may involve the kind of work they find more enjoyable.
Data should meet tests against fallacies that apply to data like errors in grammar, logic, or reasoning are fallacies in arguments. The above example of a medical health record of a birth with same-sex parents and the mother identifying as a male is analogous to a grammatical error even though the data itself meets the business rules for the form. We should be able to object to this data as valid to use for some purposes such as determining eligibility medical necessity for health services just like we would reject a grammatically incorrect sentence in an formal argument.
Democracy also can not afford to be distracted by spark data (stray voltage) for the same reason. The urgent issues need solutions that require hard and painful choices. Unfortunately, the modern practice of democracy demands obedience to daily public opinion polls that are easily manipulated by stray voltage or spark data. Instead of governing by the people, modern democracy wastes time on arguments over sparks.
There may be many other ways to identify fallacies in handling data that may have an analogous effect on dedomenocracy automated rule-making as classical rhetorical fallacies have on persuasive arguments. In order to defend against malicious or unfair manipulation of a dedomenocracy, we need to develop ways to identify data fallacies that we can use to govern the quality of data for automated rule making.
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