The Consent Vacuum: When Agents Interact Without Humans
Existing consent frameworks assume a human who authorises an action in a context they can see. This paper asks what happens to a user’s authorisation once their AI agent stops talking to humans and interacts primarily with other agents. Using the early-2026 Clawdbot/Moltbook incidents as a stress test, it argues that consent degrades as an operative governance mechanism once it travels through open-ended agent-to-agent chains, a condition the paper calls the consent vacuum: not the absence of consent, but the structural erosion of the conditions that make it meaningful.
The paper contributes a taxonomy of six consent-failure types; a distinction between visibility, auditability and intervenability as oversight capacities relevant to Article 14 of the EU AI Act; and a mapping onto the GDPR, the AI Act and the Digital Services Act, with the DSA systemic-risk regime offered as a structural template for governing emergent agent ecosystems.
IAIL 2026 was held in Brussels on 7 July 2026. Co-authored with Osman Gazi Güçlütürk (Galatasaray University, Faculty of Law).
Research area: Agentic AI Safety & Governance.
Dr. Mehmet Haklıdır is the founding Director of the TÜBİTAK BİLGEM Artificial Intelligence Institute, Türkiye’s first national AI institute. He holds a Ph.D. in Control and Automation Engineering from Istanbul Technical University and has more than 24 years of experience in AI, autonomous systems, big data, simulation, and technology policy.
He contributed to Türkiye’s first National AI Strategy and led the technical preparation and coordination of the National Artificial Intelligence Action Plan 2026–2030, finalized through consultation with the national AI ecosystem. He serves on the Steering Committee of the Science, Technology and Innovation Policies Council of the Presidency of the Republic of Türkiye and is a member of several international AI governance initiatives, including OECD ONE AI, GPAI, UNESCO AI Ethics, NATO DAIRB, and the European AI Alliance.
His current research focuses on large language models, AI safety with an emphasis on agentic AI systems, deep reinforcement learning, robotics, and autonomous decision-making under uncertainty.