While there's not much meat to the article, the topic of whether and how to police algorithmic decision-making is an interesting one. On the one hand, biased r

While there's not much meat to the article, the topic of whether and how to police algorithmic decision-making is an interesting one. On the one hand, biased resource allocation can be insidious and giving a free pass to any discrimination that's implemented within a black box labeled "algorithm" seems foolish. On the other hand, none of the alternatives I see: * banning a predefined list of analytical practices, * outlawing a list of input types that may be used, * second-guessing the quality of not-transparently-unjust logic to assign negligence, or * defining a "fair" outcome that's not to be deviated from seem both administrable and sufficient, especially as the stakes go up and complexity necessary to improve decision outcomes becomes more complex (turn left-right-straight given a LIDAR image, predict likelihood to reject an organ given patient history and donor/patient DNA profiles).