Building Safe and Beneficial AI — DeepMind Safety Research

DeepMind's safety research overview describes Hassabis's approach to the alignment problem — not as a separate concern from capability research, but as an integrated requirement. The page summarizes the main safety research threads: specification (how do you describe what you want in a way a powerful optimizer can't misinterpret?), robustness (does the system behave safely under distribution shift?), assurance (how do you verify a system is safe before deploying it?), and reward modeling. Hassabis has consistently argued that 'beneficial AI' requires more than just technically capable AI — the system must have values that align with human wellbeing, not just human expressed preferences. He distinguishes this from 'value alignment' as typically framed: rather than aligning AI to what humans say they want, the goal is AI that genuinely understands human wellbeing and can fill in gaps where human-expressed preferences are inconsistent or poorly specified. The safety page also describes DeepMind's Constitutional AI-adjacent work (prior to the Anthropic version), interpretability tools, and oversight mechanisms designed to maintain human control as AI systems become more capable.