Welcome to the Nexus of Ethics, Psychology, Morality, Philosophy and Health Care

Welcome to the nexus of ethics, psychology, morality, technology, health care, and philosophy
Showing posts with label Value Alignment. Show all posts
Showing posts with label Value Alignment. Show all posts

Wednesday, October 25, 2023

The moral psychology of Artificial Intelligence

Bonnefon, J., Rahwan, I., & Shariff, A.
(2023, September 22). 

Abstract

Moral psychology was shaped around three categories of agents and patients: humans, other animals, and supernatural beings. Rapid progress in Artificial Intelligence has introduced a fourth category for our moral psychology to deal with: intelligent machines. Machines can perform as moral agents, making decisions that affect the outcomes of human patients, or solving moral dilemmas without human supervi- sion. Machines can be as perceived moral patients, whose outcomes can be affected by human decisions, with important consequences for human-machine cooperation. Machines can be moral proxies, that human agents and patients send as their delegates to a moral interaction, or use as a disguise in these interactions. Here we review the experimental literature on machines as moral agents, moral patients, and moral proxies, with a focus on recent findings and the open questions that they suggest.

Conclusion

We have not addressed every issue at the intersection of AI and moral psychology. Questions about how people perceive AI plagiarism, about how the presence of AI agents can reduce or enhance trust between groups of humans, about how sexbots will alter intimate human relations, are the subjects of active research programs.  Many more yet unasked questions will only be provoked as new AI  abilities  develops. Given the pace of this change, any review paper will only be a snapshot.  Nevertheless, the very recent and rapid emergence of AI-driven technology is colliding with moral intuitions forged by culture and evolution over the span of millennia.  Grounding an imaginative speculation about the possibilities of AI with a thorough understanding of the structure of human moral psychology will help prepare for a world shared with, and complicated by, machines.

Friday, January 5, 2018

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.