Friday, July 27, 2012

Fraud in science

Ars Technica has a slightly tongue-in-cheek article on how to commit scientific fraud and get away with it. The article discusses eight points:

  1. Fake data nobody ever expects to see
  2. Work with many collaborators
  3. Tell people what they already know
  4. Don't do research anyone cares about
  5. Don't publish in journals focused on your field
  6. Distribute responsibility
  7. Don't plagiarize
  8. Don't duplicate images
It seems to me that, unfortunately, computational intelligence (CI) is more susceptible to many of these methods than many other fields. I sometimes joke that I am fortunate to work in a field where I can perform solid research by making stuff up as I go along, by which I mean that developing algorithms or techniques is often a more creative process than, for example, research in biology or physics. But think about how many CI papers you've seen that don't make the data available (point 1), or even describe its statistical parameters?

A long list of co-authors is not as common in CI (point 2) as it is in other fields, but I have seen many, many papers that are going over the same topic as has been covered many times before (point 3). Also, many, many papers cover minimal, slightly incremental "improvements" to existing algorithms that are of little true interest to most other researchers (point 4).

While one of the great joys of working in computational intelligence lies in the broad range of applications the field can be applied to, it does provide more opportunity to publish in journals that specialise is other fields (point 5).

The remaining three points (6-8) are more concerned with how not to get caught, or rather, how not to draw attention to yourself while committing fraud.

Fraud is always a problem, and I don't think that it is any less common in CI than in any other field. A greater emphasis on the use of statistics in CI papers would help guard against fraud (see my posts here and here about increasing the statistical basis of CI papers). But apart from that, we still depend on the honesty and integrity of the authors.

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