I am currently looking for a scientific reference (journal article, book etc) that describes the challenge of evaluating the performance of a method on the data it produces itself.

For example:

A trained ranking algorithm A in a production system is producing ranked result lists of documents for a set of users. These users are asked to use documents of these results lists in their work.

Come in algorithm B that is supposed to be evaluated against algorithm A. As an evaluation, it is decided to compare both algorithms by their ranking of the user-chosen documents in a defined time frame.

Although these data points were not part of the training, the results will highly likely be in favor for algorithm A, as it was used to produce the result list the user then selected the documents from.

The closes description I could come up with is "self-fulfilling prophecy bias", but I was unsuccessful in finding anything around this yet.

Many thanks!

  • $\begingroup$ There are more than a few books on algorithmic bias. Good places to start are Virginia Eubanks recent book Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor or Cathy O'Neil's Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy $\endgroup$ – Mike Hunter Mar 15 '18 at 12:25
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    $\begingroup$ Thank you very much! While these sound very interesting and are definitely worth looking up, I would need sth more scientific describing the issue in my mentioned example. $\endgroup$ – Matthias Mar 15 '18 at 12:43
  • $\begingroup$ Both books are written by highly trained, mathematical data scientists and are, therefore, scientific. $\endgroup$ – Mike Hunter Apr 22 '18 at 15:18

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