From Algorithmic Black Boxes to Adaptive White Boxes: Declarative
  Decision-Theoretic Ethical Programs as Codes of Ethics
  Ethics of algorithms is an emerging topic in various disciplines such as
social science, law, and philosophy, but also artificial intelligence (AI). The
value alignment problem expresses the challenge of (machine) learning values
that are, in some way, aligned with human requirements or values. In this paper
I argue for looking at how humans have formalized and communicated values, in
professional codes of ethics, and for exploring declarative decision-theoretic
ethical programs (DDTEP) to formalize codes of ethics. This renders machine
ethical reasoning and decision-making, as well as learning, more transparent
and hopefully more accountable. The paper includes proof-of-concept examples of
known toy dilemmas and gatekeeping domains such as archives and libraries.
