Toward ensuring ethical behavior from autonomous systems: a case-supported principle-based paradigm

This paper aims to propose a paradigm of case-supported principle-based behavior (CPB) to help ensure ethical behavior of autonomous machines. The requirements, methods, implementation and evaluation components of the CPB paradigm are detailed.

The authors argue that ethically significant behavior of autonomous systems can be guided by explicit ethical principles abstracted from a consensus of ethicists. Particular cases of ethical dilemmas where ethicists agree on the ethically relevant features and the right course of action are used to help discover principles needed for ethical guidance of the behavior of autonomous systems.

Such a consensus, along with its corresponding principle, is likely to emerge in many areas in which autonomous systems are apt to be deployed and for the actions they are liable to undertake, as we are more likely to agree on how machines ought to treat us than on how human beings ought to treat one another.

Principles are comprehensive and comprehensible declarative abstractions that succinctly represent this consensus in a centralized, extensible and auditable way. Systems guided by such principles are likely to behave in a more acceptably ethical manner, permitting a richer set of behaviors in a wider range of domains than systems not so guided, and will exhibit the ability to defend this behavior with pointed logical explanations.

A new threshold has been reached where machines are being asked to make decisions that can have an ethically relevant impact on human beings. It can be argued that such machine ethics ought to be the driving force in determining the manner and extent to which autonomous systems should be permitted to interact with them.

Developing and employing principles for this use is a complex process, and new tools and methodologies will be needed by engineers to help contend with this complexity. The authors offer the CPB paradigm as an abstraction to help mitigate this complexity.