  It is widely acknowledged that transparency of automated decision making is
crucial for deployability of intelligent systems, and explaining the reasons
why some decisions are "good" and some are not is a way to achieving this
transparency. We consider two variants of decision making, where "good"
decisions amount to alternatives (i) meeting "most" goals, and (ii) meeting
"most preferred" goals. We then define, for each variant and notion of
"goodness" (corresponding to a number of existing notions in the literature),
explanations in two formats, for justifying the selection of an alternative to
audiences with differing needs and competences: lean explanations, in terms of
goals satisfied and, for some notions of "goodness", alternative decisions, and
argumentative explanations, reflecting the decision process leading to the
selection, while corresponding to the lean explanations. To define
argumentative explanations, we use assumption-based argumentation (ABA), a
well-known form of structured argumentation. Specifically, we define ABA
frameworks such that "good" decisions are admissible ABA arguments and draw
argumentative explanations from dispute trees sanctioning this admissibility.
Finally, we instantiate our overall framework for explainable decision-making
to accommodate connections between goals and decisions in terms of decision
graphs incorporating defeasible and non-defeasible information.
