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There is increasing interplay between humans and artificial intelligent (AI) entities in online environments. With the growing autonomy and sophistication of these AI systems, the hybrid communities which are formed start to behave like the more-familiar, human-only social systems. This sets up the challenge to find systematic ways to ensure reliable governance for these interactions just as we do in human communities. This article proposes a novel approach to build governance for hybrid communities using what we call Conscientious Design (CD). There are two key aspects to CD: 1) the introduction of value categories that guides the identification of relevant stakeholder values, coupled with 2) a tripartite model for online institutions that serves to describe the interactions of hybrid communities of humans and artificial entities in a way that is consistent with the values of all stakeholders.

In this paper, we show how the impact of norms on the sequential decision making of agents can be formally modeled, computationally determined and quantitatively assessed. For this purpose, we put forward the Normative Markov Decision Process (NMDP) framework – an extension of Markov Decision Processes (MDPs). NMDPs provide an explicit declarative representation of obligation and prohibition as penalties associated to states and conditions on the accessibility of states. Furthermore, NMDPs make an explicit representation of the probability that whoever is responsible for enforcing the norms detects a violation, thus modeling enforcement effectiveness and cost. Then, we approach the problem of reasoning with the NMDP framework by proposing two types of agent: norm-compliant and self-interested . Using these agents, this paper shows how this framework may be employed to study the impact of norms on agent behavior by providing a quantitative measure of the cost of norm abidance and, by the same token, to what extent norms affect reasoning complexity. In particular, we illustrate the use of the NMDP framework through experimental analysis in a simulated environment where the chances of norm violation being detected and penalties are varied. [ABSTRACT FROM AUTHOR]