@article{2021:buhmann:tackling_t,
title = {Tackling the Grand Challenge of Algorithmic Opacity Through Principled Robust Action},
year = {2021},
note = {Organizations increasingly delegate agency to artificial intelligence.
However, such systems can yield unintended negative effects as they
may produce biases against users or reinforce social injustices. What
pronounces them as a unique grand challenge, however, are not their
potentially problematic outcomes but their fluid design. Machine
learning algorithms are continuously evolving; as a result, their
functioning frequently remains opaque to humans. In this article, we
apply recent work on tackling grand challenges though robust action to
assess the potential and obstacles of managing the challenge of
algorithmic opacity. We stress that although this approach is
fruitful, it can be gainfully complemented by a discussion regarding
the accountability and legitimacy of solutions. In our discussion, we
extend the robust action approach by linking it to a set of principles
that can serve to evaluate organisational approaches of tackling grand
challenges with respect to their ability to foster accountable
outcomes under the intricate conditions of algorithmic opacity.},
journal = {Morals & Machines},
pages = {74--85},
author = {Buhmann, Alexander and Fieseler, Christian},
volume = {1},
number = {1}
}