Moving beyond “algorithmic bias is a data problem”
Many here are in the throws of this &quot;data mythology&quot;...<p>It is trivial for algorithms to introduce novel racism: consider processing text analysing minority causes. These texts contain statistical associations between politically sensitive terms, negative terms, (and so on). However they are not racist, are not born of any racist project, and most humans reading them would be moved to more positively regard racial terms. The meaning text express consists in what they want their readers to do. It is an activity of coordination (writer&#x2F;reader); and it here it has no racist aspect.<p>A machine if it should have learned anything at all from this text, should learn to associate minority terms with virtuous political projects; it should have acquired an understanding of the many types of associative relation: needing, wanting, opposing, supporting, trusting, advocating...<p>Yet a machine performing an action based on statistical associations will not learn these; and so, will act prejudicially. It will merely expect terms to co-occur, wholly unable to determine why. We expect charity workers to be greedy? No, we expect them to morally oppose greed. We expect movies to smell like candy? No, we expect them to be sold together. (And so on.)<p>It is somewhat alarming that statistical associations in terms in historical texts are being seen as characteristic of human communication, meaning, belief, association, ethics, action. I think it has led here to a great moral oversight: that absent the ability to understand text, machines here are introducing novel prejudice where none existed prior.