Computer Says No: Part 1 Algorithmic Bias and Part 2 Explainability

These two (of a planned three) posts take an interesting approach to the ethical problems of algorithmic decision making, resulting in a much more optimistic view than most who write on this. It’s very much worth reading even though the arguments don’t seem quite as strong as they are made to appear.

Part 1 essentially side steps the problem of bias in decision making by asserting that automated decision systems don’t actually make decisions (humans still mostly do that), but should instead be thought of as prediction systems – and the test of a prediction system is in the quality of its predictions, not in the operations of its black box. The human dimension is a bit of a red herring as it’s not hard to think of examples where in practice the prediction outputs are all the decision maker has to go on, even if in theory the system is advisory. More subtly, there is an assumption that prediction quality can easily be assessed and an assertion that machine predictions can be made independent of the biases of those who create them, both of which are harder problems than the post implies.

The second post goes on to address explainability, with the core argument being that it is a red herring (an argument Ed Felten has developed more systematically): we don’t really care whether a decision can be explained, we care whether it can be justified, and the source of justification is in its predictive power, not in the detail of its generation. There are two very different problems with that. One is that not all individual decisions are testable in that way: if I am turned down for a mortgage, it’s hard to falsify the prediction that I wouldn’t have kept up the payments. The second is that the thing in need of explanation may be different for AI decisions from that for human decisions. The recent killing of a pedestrian by an autonomous Uber car illustrates the point: it is alarming precisely because it is inexplicable (or at least so far unexplained), but whatever went wrong, it seems most unlikely that a generally low propensity to kill people will be thought sufficiently reassuring.

None of that should be taken as a reason for not reading these posts. Quite the opposite: the different perspective is a good challenge to the emerging conventional wisdom on this and is well worth reflecting on.

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