Abstract
Some artificial intelligence provisioning models that function as tools for human users or rely on labor arbitrage can present challenges for organizations, such as managing personnel rather than task outcomes and introducing data security risks. An architecture is described for an outcome-based synthetic labor market in which autonomous computational agents can be compensated based on verified task completion. The framework can leverage trusted execution environments to create secure hardware enclaves for processing sensitive data, which can render the data cryptographically inaccessible to a host system or agent provider. This approach can facilitate a secure, transactional market for autonomous professional execution, which may enable a shift from managing labor resources to procuring verified outcomes from a pool of specialized agents.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Kayande, Tanmay, "System for a Secure, Outcome-Based Synthetic Labor Market Using Trusted Execution Environments", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/9606