Issue Liability allocation in hybrid human-AI failure

Summary

This issue asks who is responsible when a system that combines people and AI goes wrong. EY points out that assigning liability becomes complicated once decisions and actions are shared between human and machine components. It argues that organizations need frameworks that set out clearly what each human and each machine part is responsible for. Such frameworks must also keep people in charge of overseeing critical decisions. EY places this within a wider set of social, policy and regulatory challenges raised by human-machine hybrids, alongside neural data privacy and unequal access to enhancement. Without clear rules on accountability, hybrid systems may be hard to trust or to scale.