Max Kanwal, along with co-authors Caryn Tran and Patrick Mineault, introduced a novel framework called Bounded Morality on April 1, 2026. This framework aims to redefine how artificial intelligence systems approach moral decision-making, emphasizing the limitations faced by finite agents.
Understanding Bounded Morality in AI
The concept of Bounded Morality extends the idea of bounded rationality proposed by Herbert Simon. It posits that moral cognition has often been viewed through fixed ethical theories such as deontology, consequentialism, and virtue ethics. Instead of adhering strictly to these theories, the framework presents a dual-dimensional analysis: moral breadth and moral depth.
Moral breadth refers to the range of entities considered morally relevant, while moral depth pertains to the level of inferential integration necessary for evaluating the interactions among these entities. The framework highlights that the constraints of limited resources force a trade-off between these two dimensions, thereby shaping the feasible space of moral computation.
The Implications of Bounded Morality
Within the Bounded Morality framework, ethical theories are not seen as competing for moral truth but rather as locally efficient strategies tailored to various demand regimes. This perspective allows for a more adaptable approach to moral reasoning in artificial systems. The implications are significant: moral alignment in AI does not merely depend on replicating human judgments but on optimizing the scaling and allocation of moral reasoning capacities.
