3 ms·

This whole concept makes the silly assumption that today's machine learning results in machine understanding. Without understanding human biases, as they are e

• jhanschoo 10y ago
  That's a bold, ill-defined, and unsubstantiated claim. Leaving machine understanding undefined, I can think of an example in which these biases are relevant. For example, if you're offering a search engine solution incorporating clustering, you'd be concerned that your search retrievals aren't associating racial minorities with pejorative prejudices.
  • wolfgke 10y ago
    > For example, if you're offering a search engine solution incorporating clustering, you'd be concerned that your search retrievals aren't associating racial minorities with pejorative prejudices. If in the data racial minorities are associated with pejorative prejudices, this is plain distorting of the truth. If you are concerned about this associations, don't shoot the messenger, but the people writing the original texts.
    • jhanschoo 10y ago
      > this is plain distorting of the truth. In the context of business and many other interests, it's expedient to present a more polite face. You're providing a service, not a mirror. In the context of a search engine service, say, a specialized, domain-specific search for a certain profession, such biases often distract and detract from the quality of the service you provide to your customers.