Accountability
Related papers: 20
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Accountability in robotics and AI refers to the clear assignment of responsibility for the actions, decisions, and outcomes produced by automated systems. As robots and AI take on consequential roles — performing surgery, delivering social services, operating as autonomous weapons, or caring for children and the elderly — questions arise about who bears moral, legal, and regulatory responsibility when things go wrong. Accountability mechanisms include transparency measures (such as ethical black boxes that log robot behavior), meaningful human oversight, and legal frameworks that define liability across developers, operators, and the systems themselves. In practice, accountability shapes how AI and robotic systems are designed, deployed, and audited, ensuring that affected individuals have recourse when harmed. It matters because autonomous systems can cause real-world harm — physical, social, or psychological — yet diffuse responsibility among many stakeholders. Without robust accountability structures, deployment of AI and robots risks eroding public trust, perpetuating inequities, and leaving victims without remedy, particularly in high-stakes domains like healthcare, welfare, and armed conflict.
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