Fixed typo
Signed-off-by: Jim Martens <github@2martens.de>
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@ -12,9 +12,9 @@ the design of so called algorithms, a term often used in public
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discourse for applied neural networks, is the question of
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algorithmic accountability\cite{Diakopoulos2014}.
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The charme of supervised neural networks, that they can learn from
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The charm of supervised neural networks, that they can learn from
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input-output relations and figure out by themselves what connections
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are necessary for that, is also their achilles heel. This feature
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are necessary for that, is also their Achilles heel. This feature
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makes them effectively black boxes. It is possible to question the
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training environment, like potential biases inside the data sets, or
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the engineers constructing the networks but it is not really possible
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