  We study a class of {\em aggregation rules} that could be applied to ethical
AI decision-making. These rules yield the decisions to be made by automated
systems based on the information of profiles of preferences over possible
choices. We consider two different but very intuitive notions of preferences of
an alternative over another one, namely {\it pairwise majority} and {\it
position} dominance. Preferences are represented by permutation processes over
alternatives and aggregation rules are applied to obtain results that are
socially considered to be ethically correct. In this setting, we find many
aggregation rules that satisfy desirable properties for an autonomous system.
We also address the problem of the stability of the aggregation process, which
is important when the information is variable. These results are a contribution
for an AI designer that wants to justify the decisions made by an autonomous
system.\\ \textit{Keywords:} Aggregation Operators; Permutation Process;
Decision Analysis.
