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added a recent article by steve Sailor
The incident points to the problem tech companies face as computers get smarter and are expected to take on more more tasks a human normally would do. Those areas of computer science -- such as artificial intelligence or machine learning -- are some of the biggest engineering focuses in Silicon Valley. But ''with that focus comes another task that computers have not traditionally tackled: grappling with the challenge of sensitivity''
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=== [http://internet.gawker.com/flickrs-auto-tagging-feature-accidentally-labeled-a-bla-1706045425 Flickr's Auto-Tagging Feature Accidentally Labeled a Black Man "Ape"]
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<p>Search for “ape” on Flickr and you’ll witness an endlessly scrolling cavalcade of primate photography, from monkeys glimpsed on safari to those held in captivity at the zoo. Until recently, you’d also see <a href="https://www.flickr.com/photos/thirteenthfloormedia/14570569401">a portrait of a middle-aged black man</a> named William. Flickr thought William was an ape, too.</p>
<p>All of the offending examples listed here have since been corrected, though the two portraits are still labeled with “animal,” which is I suppose technically accurate. And users can manually remove bad auto-tags from their pictures. As the <em>Guardian </em>notes, Flickr appears to have wisely removed “ape” entirely from its auto-tagger’s list of choices. Maybe leave this stuff to humans with eyes next time. </p>
</blockquote>* [http://www.theguardian.com/technology/2015/jul/01/google-sorry-racist-auto-tag-photo-app Google says sorry for racist auto-tag in photo app]
=== [http://money.cnn.com/2015/05/21/technology/flickr-racist-tags/ Flickr's new auto-tags are racist and offensive | CNN Money]===
Not surprisingly, the conventional wisdom increasingly believes Artificial Intelligence needs a dose of Artificial Stupidity to keep it from being as racist and sexist as Natural Intelligence. Otherwise, the Robot Permit Patties will run amok, says Nature:
[ https://www.nature.com/articles/d41586-018-05707-8?utm_source=briefing-wk&utm_medium=email&utm_campaign=briefing&utm_content=20180720 / AI can be sexist and racist — it’s time to make it fair]
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… As much as possible, data curators should provide the precise definition of descriptors tied to the data. For instance, in the case of criminal-justice data, appreciating the type of ‘crime’ that a model has been trained on will clarify how that model should be applied and interpreted. …
A related approach involves changing the learning algorithm to reduce its dependence on sensitive attributes, such as ethnicity, gender, income — and any information that is correlated with those characteristics.
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[[Category:Under construction]]
