With software, we don't act ourselves, we just specify rules to be executed. With ML, even these rules are delegated to an external model.
A core ethical issue is that machine learning increases the distance between our explicit choices and their real-world consequences.
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I'd say you have it backwards. ML shortens the distance between goal and solution.
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Have you read the recent 'Weapons of Math Destruction', by Cathy O'Neil? If so what do you think about it? https://www.amazon.com/Weapons-Math-Destruction-Increases-Inequality/dp/0553418815 …
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smoking, over-eating, investing, burning hydrocarbons, etc. we deal with latency when perturbing all large systems.
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@dmimno In its defense, the degree to which machine learning does this is similar to "with a computer" or "with bureaucracy." -
The important conclusion here is that we need to plan for and mitigate abstraction of accountability & outcomes...
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@asociologist Seems like data visualizers have a big job to do helping people interrogate the innards of machine-learning models.Thanks. Twitter will use this to make your timeline better. UndoUndo
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@nicolasperony is there a parallel to the notion of externalities in economics?Thanks. Twitter will use this to make your timeline better. UndoUndo
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when you can explain the an ml or even dl model, then the distance becomes minimal. Have you ever heard of
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"Machine learning is like money-laundering for ethics."
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