The moral psychology of Artificial Intelligence

Bonnefon, J., Rahwan, I., & Shariff, A.

(2023, September 22). 

https://doi.org/10.1146/annurev-psych-030123-113559

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.

Reverse-engineering the self

Paul, L., Ullman, T. D., De Freitas, J., & Tenenbaum, J.

(2023, July 8). PsyArXiv

https://doi.org/10.31234/osf.io/vzwrn

Abstract

To think for yourself, you need to be able to solve new and unexpected problems. This requires you to identify the space of possible environments you could be in, locate yourself in the relevant one, and frame the new problem as it exists relative to your location in this new environment. Combining thought experiments with a series of self-orientation games, we explore the way that intelligent human agents perform this computational feat by “centering” themselves: orienting themselves perceptually and cognitively in an environment, while simultaneously holding a representation of themselves as an agent in that environment. When faced with an unexpected problem, human agents can shift their perceptual and cognitive center from one location in a space to another, or “re-center”, in order to reframe a problem, giving them a distinctive type of cognitive flexibility. We define the computational ability to center (and re-center) as “having a self,” and propose that implementing this type of computational ability in machines could be an important step towards building a truly intelligent artificial agent that could “think for itself”. We then develop a conceptually robust, empirically viable, engineering-friendly implementation of our proposal, drawing on well established frameworks in cognition, philosophy, and computer science for thinking, planning, and agency.

The research is here.

The computational structure of the self is a key component of human intelligence. They propose a framework for reverse-engineering the self, drawing on work in cognition, philosophy, and computer science.

The authors argue that the self is a computational agent that is able to learn and think for itself. This agent has a number of key abilities, including:

• The ability to represent the world and its own actions.
• The ability to plan and make decisions.
• The ability to learn from experience.
• The ability to have a sense of self.

The authors argue that these abilities can be modeled as a POMDP, a type of mathematical model that is used to represent sequential decision-making processes in which the decision-maker does not have complete information about the environment. They propose a number of methods for reverse-engineering the self, including:

• Using data from brain imaging studies to identify the neural correlates of self-related processes.
• Using computational models of human decision-making to test hypotheses about the computational structure of the self.
• Using philosophical analysis to clarify the nature of self-related concepts.

The authors argue that reverse-engineering the self is a promising approach to understanding human intelligence and developing artificial intelligence systems that are capable of thinking for themselves.