The urgency to recognize the origin of digital content is spawning many detection solutions. As generative artificial intelligence detectors overcome current limitations, they will attempt to keep pace with sophisticated generative model development.
As society increasingly relies on digital services, identity management becomes increasingly vital. Decentralized identity offers a novel approach to address today’s identity challenges, putting users in control of their own digital identities and personal data.
As the cap abilities of artificial intelligence(AI) systems constantly grow, so too does their complexity. The explainability toward their users is gaining attention, becoming a requirement that these systems should satisfy. We articulate user requirements for explainable AI systems.
Digital interactions are taking on a more human-like appearance and behavior, but could or should they become our digital selves?
We are on the quest for mainstream
adoption of privacy-preserving computation solutions. As present challenges are solved, homomorphic encryption and related privacy-preserving techniques will change
the face of information technology, security,
privacy, and policy.
Generative artificial intelligence can make powerful artifacts when used at scale, but developing trust in these artifacts and controlling their creation are essential for user adoption.