The Narrative fallacy or the assumption of linearity (the failure to recognize the possibility of a Black
Swan—to assume that the past and or the present is the best predictor of the future and to rewrite history to support ones assumptions
The error of unexamined value assumptions—the lack of willingness to state the values that underlie ones observations
Pilot error—the unwillingness to question one’s assumption when failure occurs and blame results on how the experiment was carried out.
Normal error or the error of complexity—error resulting from the complexity produced by AI and not solvable through human intervention
The error of internal validity—to not measure what you think your are measuring
The error of external validity or lack of reliability—the inability to reproduce the results under different circumstances.
The self-fulfilling prophesy—to produce the results that you anticipate by your own actions
The error overfitting or the problem of big data—you find the data that fits your assumption
The error of underfitting or the problem of data scarcity—you generalize from too small a number of cases