Achieving Long-Term Fairness in Sequential Decision Making

In this paper, we propose a framework for achieving long-term fair sequential decision making. By conducting both the hard and soft interventions, we propose to take path-specific effects on the time-lagged causal graph as a quantitative tool for measuring long-term fairness. The problem of fair sequential decision making is then formulated as a constrained optimization problem with the utility as the objective and the long-term and short-term fairness as constraints. We show that such an optimization problem can be converted to a performative risk optimization. Finally, repeated risk minimization (RRM) is used for model training, and the convergence of RRM is theoretically analyzed. The empirical evaluation shows the effectiveness of the proposed algorithm on synthetic and semi-synthetic temporal datasets

On Mechanism, Process and Polity: An Agent-Based Modeling and Simulation Approach

The present approach provides a theoretical account of political culture-based modeling of political change phenomena. Our approach is an agent-based simulation model inspired by a social-psychological account of the relation between the individual agents (citizens) and the polity. It includes political culture as a fundamental modeling dimension. On this background, we reconsider the operational definitions of agent, mechanism, process, and polity so as to specify the role they play in the modeling of political change phenomena. We evaluate our previous experimental simulation experience in corruption emergence and political attitude change. The paper approaches the artificial polity as a political culture-based model of a body politic. It involves political culture concepts to account for the complexity of domestic political phenomena, going from political attitude change at the individual level up to major political change at the societal level. Architecture, structure, unit of interaction, generative mechanisms and processes are described. Both conceptual and experimental issues are described so as to highlight the differences between the simulation models of society and polity

And the Robot Asked "What do you say I am?" Can Artificial Intelligence Help Theologians and Scientists Understand Free Moral Agency?

Concepts of human beings as free and morally responsible agents are shared culturally by scientists and Christian theologians. Accomiplishments of the "artificial intelligence" (AI) branch of computer science now suggest the possibility of an advanced robot mimicking behaviors associated with free and morally responsible agency. The author analyzes some specific features theology has expected of such agency, inquiring whether appropriate AI resources are available for incorporating the features in robots. Waiving questions of whether such extraordinary robots will be constructed, the analysis indicates that they could be, furnishing useful new scientific resources for understanding moral agency