Prior review of the ethical challenges facing AI has identified six types of concerns that can be traced to the operational parameters of decision-making algorithms and AI systems. The map reproduced and adapted in Figure 1 takes into account:

“decision-making algorithms (1) turn data into evidence for a given outcome (henceforth conclusion), and that this outcome is then used to (2) trigger and motivate an action that (on its own, or when combined with other actions) may not be ethically neutral. This work is performed in ways that are complex and (semi-)-autonomous, which (3) complicates apportionment of responsibility for effects of actions driven by algorithms.”

From these operational characteristics, three epistemological and two normative types of ethical concerns can be identified based on how algorithms process data to produce evidence and motivate actions. The proposed five types of concerns can cause failures involving multiple human, organisational, and technological agents. This mix of human and technological actors leads to difficult questions concerning how to assign responsibility and liability for the impact of AI behaviours. These difficulties are captured in traceability as a final, overarching, type of concern.

Types of concerns
Ethical Challenges