Implementation of Moral Uncertainty in Intelligent Machines

Kyle Bogosian
Minds and Machines
December 2017, Volume 27, Issue 4, pp 591–608

Abstract

The development of artificial intelligence will require systems of ethical decision making to be adapted for automatic computation. However, projects to implement moral reasoning in artificial moral agents so far have failed to satisfactorily address the widespread disagreement between competing approaches to moral philosophy. In this paper I argue that the proper response to this situation is to design machines to be fundamentally uncertain about morality. I describe a computational framework for doing so and show that it efficiently resolves common obstacles to the implementation of moral philosophy in intelligent machines.

Introduction

Advances in artificial intelligence have led to research into methods by which sufficiently intelligent systems, generally referred to as artificial moral agents (AMAs), can be guaranteed to follow ethically defensible behavior. Successful implementation of moral reasoning may be critical for managing the proliferation of autonomous vehicles, workers, weapons, and other systems as they increase in intelligence and complexity.

Approaches towards moral decisionmaking generally fall into two camps, “top-down” and “bottom-up” approaches (Allen et al 2005). Top-down morality is the explicit implementation of decision rules into artificial agents. Schemes for top-down decision-making that have been proposed for intelligent machines include Kantian deontology (Arkoudas et al 2005) and preference utilitarianism (Oesterheld 2016). Bottom-up morality avoids reference to specific moral theories by developing systems that can implicitly learn to distinguish between moral and immoral behaviors, such as cognitive architectures designed to mimic human intuitions (Bello and Bringsjord 2013). There are also hybrid approaches which merge insights from the two frameworks, such as one given by Wiltshire (2015).

The article is here.