On Ensuring that Intelligent Machines Are Well-Behaved
  Machine learning algorithms are everywhere, ranging from simple data analysis
and pattern recognition tools used across the sciences to complex systems that
achieve super-human performance on various tasks. Ensuring that they are
well-behaved---that they do not, for example, cause harm to humans or act in a
racist or sexist way---is therefore not a hypothetical problem to be dealt with
in the future, but a pressing one that we address here. We propose a new
framework for designing machine learning algorithms that simplifies the problem
of specifying and regulating undesirable behaviors. To show the viability of
this new framework, we use it to create new machine learning algorithms that
preclude the sexist and harmful behaviors exhibited by standard machine
learning algorithms in our experiments. Our framework for designing machine
learning algorithms simplifies the safe and responsible application of machine
learning.
