Project — Legal accountability in AI-based robot-agents’ user interfaces
How can autonomous or self-learning AI provide ex-ante and ex-post controls. Using an ML system does not mean that it cannot be constrained by an additional layer of rules-based safety and accountability mechanisms. The behaviour of these constraints can then be explained, thus making the robot-agents both legally and technically robust and reliable.
Determining exactly why a particular decision was made by a robot-agent is often difficult. But the whitelisting-based accountability algorithm can greatly help by easily providing an answer to the question of who enabled making a particular decision in the first place. Whitelisting enables the accountability and humanly manageable safety features of robot-agents with learning capability. The behaviour of these constraints can be controlled and explained by utilising specialised, humanly comprehensible user interfaces, resulting in clearer distinctions of accountability between the manufacturers, owners, and operators, thus making the robot-agents both legally and technically robust and reliable.