Automated technologies have been developed to help data subjects learn about digital privacy practices. These tools are generally focused on supporting the notice and consent data protection approach. Overall, however, that approach has proven poorly suited to a digital ecosystem in which networked services prevail and information is collected, analyzed, and shared on a mass scale without users’ awareness.
Use-based privacy, which focuses on regulating potentially harmful data uses, is an alternative approach. Use-based models address the information asymmetry between data subjects and the entities that collect and process data by shifting responsibility from the data subject to the data collector.
Artificial intelligence tools, including privacy assistants and active privacy management tools, can aid use-based privacy’s implementation. This panel explores the role these technologies have in a digital ecosystem where consent-based privacy models have failed and use-based models are emerging.
● What is use-based privacy?
● How are regulators and businesses working to implement this model, and what obstacles does it face?
● What role do artificial intelligence-based technological tools have in use-based privacy; where can artificial intelligence have a positive impact, and what are its limits?
● What artificial intelligence is currently being developed that can assist in the implementation of use-based privacy models?