I recently read an article about how artificial intelligence replicates human stereotypes when applied to biased datasets.

What are examples of techniques to prevent bias (and stereotypes) in artificial intelligence (in particular, machine learning) systems?

  • 4
    $\begingroup$ There was also the disaster in which Microsoft made a Twitter bot to emulate millennials and it learned/tweeted things that were, uh, less than desirable. $\endgroup$
    – Ben N
    Apr 17, 2017 at 19:23

2 Answers 2


It's important to note that, ultimately, the statistical methods we currently use in ML research are just that: statistical methods. So, when they show some "bad behaviour", it's not because of problems with the statistical methods, but with the data we give them. But if the data we give them are as "genuine and unfiltered" as it gets, then it probably shows something about us.

From a cognitive science perspective, it's probably the case that the same heuristics and biases that create stereotypes are also the ones that make us powerful agents (note the similarity between categories and stereotypes), so, at least at this moment, it's unclear how we can segregate desired from undesired behaviour.

To combine the points mode above, it seems we can only either:

  1. Remove "bad content" by curating the data by hand or by some metric that we don't know of yet

  2. Accept that our methods will produce AI as "bad as we are", because that's what we are, and let it operate under the knowledge that it might produce undesired behavior sometimes.

Unless we have some crazy new theory of mind that we can begin to analyze this more rigorously, it seems like there is no clear cut solution.


The field of statistical psychology offers many methodologies to remove bias from datasets, or rather gather a dataset with minimal unknown biases. This will be the responsibility of the programmer. An AI that learns from datasets will not be able to find bias in those datasets.

  • $\begingroup$ In other words, fudge the data so it gives the results that the researcher wants instead of the "real" results. It seems like this is the where the future of scientific research is heading. $\endgroup$
    – Dunk
    Jul 10, 2017 at 21:46
  • $\begingroup$ @Dunk Biased data is already fudged: otherwise there'd be no need to debias it. $\endgroup$ Sep 25, 2019 at 2:16
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    $\begingroup$ @JeremyList - Fair enough, the data could be biased and it is reasonable to 'debias' the data, as long as a concrete explanation justifying why the data is biased can be determined first. Otherwise, debiasing data just to give more desirable results is essentially falsifying the data to give the answers the researcher is looking for. All the computer is going to do is find patterns and the most significant relationships in the data. Just because someone doesn't like what the computer finds doesn't automatically mean the data is biased. $\endgroup$
    – Dunk
    Sep 25, 2019 at 13:49

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