Are you saying that always when a sentence is translated, its author must have high status or gains high status at the moment of translation, because the default attitude is to ignore anything originally uttered in foreign language?
Yes. More generally, the default attitude is to ignore anything uttered by a member of an outgroup. By calling attention to the fact that a sentence has been translated, one is calling attention to the fact that the author speaks a foreign language and thus to the author's outgroup status. Omitting mention of a person's outgroup status is a courtesy extended to those we wish to privilege above typical outgroup members.
(A thought experiment: A Gujarati speaking beggar approaches a rich English gentleman, says something and goes away. The Englishman's wife, who is accompanying him at the moment, accidentally understands Gujarati. The man can recognise the language but doesn't understand a word. What is the probability that he asks his wife "what did he say"? As a control group, imagine the same with an English beggar, this time the gentleman didn't understand because when the beggar had spoken, a large truck had passed by. Is the probability of asking "what did he say" any different from the first group?)
Curiosity about what a low-status person says does not imply that one thinks the content of their words is a more important fact about them than their low status. With high probability, the most salient aspect of the beggar from the perspective of the Englishman is that he is a beggar (and, in the first case, a foreign beggar at that). Whatever the beggar said, if the Englishman finds out and deems it worthy of recounting later, I would be willing to bet that he will not omit mention of the fact that he heard it from a beggar.
The best laid schemes of mice and men
Go often askew,
And leave us nothing but grief and pain,
For promised joy!
- Robert Burns (translated)
Consider the following question:
Or, suppose Holden Karnofsky of charity-evaluator GiveWell has been presented with a complex analysis of why an intervention that reduces existential risks from artificial intelligence has astronomical expected value and is therefore the type of intervention that should receive marginal philanthropic dollars. Holden feels skeptical about this 'explicit estimated expected value' approach; is his skepticism justified?
Suppose you're a business executive considering n alternatives whose 'true' expected values are μ1, ..., μn. By 'true' expected value I mean the expected value you would calculate if you could devote unlimited time, money, and computational resources to making the expected value calculation.2 But you only have three months and $50,000 with which to produce the estimate, and this limited study produces estimated expected values for the alternatives V1, ..., Vn.
Of course, you choose the alternative i* that has the highest estimated expected value Vi*. You implement the chosen alternative, and get the realized value xi*.
Let's call the difference xi* - Vi* the 'postdecision surprise'.3 A positive surprise means your option brought about more value than your analysis predicted; a negative surprise means you were disappointed.
Assume, too kindly, that your estimates are unbiased. And suppose you use this decision procedure many times, for many different decisions, and your estimates are unbiased. It seems reasonable to expect that on average you will receive the estimated expected value of each decision you make in this way. Sometimes you'll be positively surprised, sometimes negatively surprised, but on average you should get the estimated expected value for each decision.
Alas, this is not so; your outcome will usually be worse than what you predicted, even if your estimate was unbiased!
Why?
This is "the optimizer's curse." See Smith & Winkler (2006) for the proof.
The Solution
The solution to the optimizer's curse is rather straightforward.
To return to our original question: Yes, some skepticism is justified when considering the option before you with the highest expected value. To minimize your prediction error, treat the results of your decision analysis as uncertain and use Bayes' Theorem to combine its results with an appropriate prior.
Notes
1 Smith & Winkler (2006).
2 Lindley et al. (1979) and Lindley (1986) talk about 'true' expected values in this way.
3 Following Harrison & March (1984).
4 Quote and (adapted) image from Russell & Norvig (2009), pp. 618-619.
5 Smith & Winkler (2006).
References
Harrison & March (1984). Decision making and postdecision surprises. Administrative Science Quarterly, 29: 26–42.
Lindley, Tversky, & Brown. 1979. On the reconciliation of probability assessments. Journal of the Royal Statistical Society, Series A, 142: 146–180.
Lindley (1986). The reconciliation of decision analyses. Operations Research, 34: 289–295.
Russell & Norvig (2009). Artificial Intelligence: A Modern Approach, Third Edition. Prentice Hall.
Smith & Winkler (2006). The optimizer's curse: Skepticism and postdecision surprise in decision analysis. Management Science, 52: 311-322.