  How can humans remain in control of artificial intelligence (AI)-based
systems designed to perform tasks autonomously? Such systems are increasingly
ubiquitous, creating benefits - but also undesirable situations where moral
responsibility for their actions cannot be properly attributed to any
particular person or group. The concept of meaningful human control has been
proposed to address responsibility gaps and mitigate them by establishing
conditions that enable a proper attribution of responsibility for humans;
however, clear requirements for researchers, designers, and engineers are yet
inexistent, making the development of AI-based systems that remain under
meaningful human control challenging. In this paper, we address the gap between
philosophical theory and engineering practice by identifying, through an
iterative process of abductive thinking, four actionable properties for
AI-based systems under meaningful human control, which we discuss making use of
two applications scenarios: automated vehicles and AI-based hiring. First, a
system in which humans and AI algorithms interact should have an explicitly
defined domain of morally loaded situations within which the system ought to
operate. Second, humans and AI agents within the system should have appropriate
and mutually compatible representations. Third, responsibility attributed to a
human should be commensurate with that human's ability and authority to control
the system. Fourth, there should be explicit links between the actions of the
AI agents and actions of humans who are aware of their moral responsibility. We
argue that these four properties will support practically-minded professionals
to take concrete steps toward designing and engineering for AI systems that
facilitate meaningful human control.
