Traditional responsibility models require consciousness or intentionality—properties that AI systems lack. This creates a responsibility gap: when AI causes harm, no single agent can be held accountable under conventional frameworks, creating significant ethical and legal challenges. We propose distributing responsibility based on causal contribution rather than mental states. When actions lead to outcomes, responsibility extends to all contributing agents—both human and artificial. This model introduces causal responsibility as the foundation for accountability, derived from actual impact rather than metaphysical properties. By shifting from mental properties to causal contribution, distributed responsibility closes the accountability gap in AI systems, ensuring responsible deployment of autonomous technologies.