In financial recommendations, linking anonymous web sessions and logged-in app behavior requires explainability and privacy checks before performance gains.
TGHE proposes private graph inference around reusable local structures instead of global graph-dependent costs.
Why LLM agent privacy risks arise from data flows, memory, tools, logs, and delegated permissions in operation.
Why GUI agents should hand control to users on sensitive screens, beyond task success alone.
OncoSynth models causal chains in oncology synthetic data to reduce treatment effect estimation bias beyond predictive metrics.
A look at why employee activity data in AI training raises governance, privacy, and access control concerns.
Examines budget-constrained AI tutor routing through educational equity, validation, privacy, and accountability.
A New York pilot trades free cleaning and cooking for household data, raising robotics training and privacy concerns.
CAPED filters mobile screenshots before remote agents see them, reducing incidental privacy exposure while preserving task utility.
How LLMs can guide neural architecture search using only trial summaries while sensitive time-series data stays on-premises.
Agent memory shifts personal data from one-off chat to reusable records. Design deletion, expiry, and audit logs before storage.
Regulation is about evidence, not intent. Capture data flows, automated-decision logs, security measures, and under-14 consent as outputs.
Reduce family AI adoption friction with onboarding (accounts, access, recovery), safety rules, and task templates before persuasion.
Puma Browser runs local AI models offline on mobile devices using WebGPU to ensure user data privacy.
Analyze privacy issues from browser AI integration and explore user resistance and global policy frameworks.
