A core ethical issue is that machine learning increases the distance between our explicit choices and their real-world consequences.
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.
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I'd say you have it backwards. ML shortens the distance between goal and solution.
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Nice to be self-critical. But finance increases this distance. ML is blameworthy so far as it is in service of finance.
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okay to quote these in my conf talk on ethics in ML & social data? Consequences of an Insightful Algorithm https://storify.com/cczona/consequences-of-an-insightful-algorithm …
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Which raises the question of intentionally. "Who" is responsible for the actions of AI controlled devices?
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, perhaps an office of censorship to control the datasets provided for the machines and dataset dealers in a black market :-).
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@deeplearning4j but with control systems we design differential equations that "are a model". In#machinelearning we simulate it. -
Not the case for adaptive control theory. Learning, as of now, is indirectly a subcateg of ada. con.
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Though rules are neither explicitly nor statically specified, the judgemental (i.e. cost fun) is finally externally predetermined.
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