Computer Modeling of Personal Autonomy and Legal Equilibrium

TL;DR

This paper proposes models for personal autonomy and legal equilibrium, exploring automated judicial decision-making, robot rights, and the need for stronger legislation to protect autonomous devices.

Contribution

It introduces three models of personal autonomy, including a linear model, a judicial decision-making algorithm, and a machine learning-based robot rights framework.

Findings

Legal equilibrium relates freedom and responsibility for community sustainability.

Judicial decision-making can be partly automated using the proposed algorithm.

Robots should be granted legal rights and protections similar to humans.

Abstract

Empirical studies of personal autonomy as state and status of individual freedom, security, and capacity to control own life, particularly by independent legal reasoning, are need dependable models and methods of precise computation. Three simple models of personal autonomy are proposed. The linear model of personal autonomy displays a relation between freedom as an amount of agent's action and responsibility as an amount of legal reaction and shows legal equilibrium, the balance of rights and duties needed for sustainable development of any community. The model algorithm of judge personal autonomy shows that judicial decision making can be partly automated, like other human jobs. Model machine learning of autonomous lawyer robot under operating system constitution illustrates the idea of robot rights. Robots, i.e. material and virtual mechanisms serving the people, deserve some legal guarantees of their rights such as robot rights to exist, proper function and be protected by the law. Robots, actually, are protected as any human property by the wide scope of laws, starting with Article 17 of Universal Declaration of Human Rights, but the current level of human trust in autonomous devices and their role in contemporary society needs stronger legislation to guarantee the robot rights.