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Exactly this. If you're going to use an algorithm to discriminate between people in order to advantage some over others (by granting a loan to them, or admittin
by justinjlynn 4y ago
Exactly this. If you're going to use an algorithm to discriminate between people in order to advantage some over others (by granting a loan to them, or admitting them to a university, for example) you'd better be able to fully and independently verifiably explain how your algorithm works and doesn't discriminate among people due to their protected attributes or proxies thereof. Current machine learning technologies render this assurance all but impossible and so they must be regarded with the most extreme suspicion until this can be provably remedied. Given how difficult it is validating traditionally programmed inference systems we're a long way from it.
- RandomLensman 4y agoIf a person and not an algorithm decides,there is no full and verifiable explanation. If we want that we need to move to fully algorithmic decisions to start with.
- actionfromafar 4y agoBut a person can (and will, sometimes) be held accountable.
- RandomLensman 4y agoNothing stops us from holding algorithm users accountable.
- actionfromafar 4y agoIt certainly would help then, if the algorithm was scrutable.
- RandomLensman 4y agoIt could be judged on outcome alone.
- commandlinefan 4y agoIf you already know what the outcome is, why bother with the model in the first place?
- RandomLensman 4y agoSorry, not the outcome of the model, but the outcome of the use of the model, i.e., the ex-post effect.
- darawk 4y agoWho is holding loan officers accountable?
- onos 4y agoI agree. Also it’s worth noting that there are multiple, reasonable definitions of “fair” and separately following these will not result in equivalent outcomes. So no matter what is done, there will be room for criticism.
- anonymoushn 4y agoWe totally do discriminate based on protected attributes for university admissions though
- commandlinefan 4y ago> due to their protected attributes or proxies thereof By definition, if it's algorithmic, it's not discriminating due to protected (or any other) attributes - it's discriminating very strictly on ability to repay with absolutely no bias, because it's literally an unthinking, unfeeling machine. Forcing ML models to find specific results rather than actually letting the models do what they were designed and asked to do renders them useless, but that may be the goal.