Generative Policy Framework for AI Training Data Curation

Abstract Policy-based mechanisms are used to implement desired autonomic behavior of a managed system in a distributed environment. For modern dynamically changing systems, policy-based mechanisms tend to be too rigid, and quickly lose their efficacy when conditions of the autonomous system change during its operation. In this paper, we propose a generative policy framework that can generate policies for an autonomous system when conditions change. For changed conditions, the policy generation manager dynamically generates new set of policies optimized for the new situation. As a use case, we demonstrate how our generative policy framework generates policies for selecting optimal data for an AI model training. The policies are dynamically generated based on the availability and trustworthiness of data in a coalition environment.
  • Valentina Salapura (IBM US)
  • David Wood (IBM US)
  • Shonda Witherspoon (IBM US)
  • Keith Grueneberg (IBM US)
  • Elisa Bertino (Purdue)
  • Amani Abu Jabal (Purdue)
  • Seraphin Calo (IBM US)
Date Jun-2019
Venue 2019 IEEE International Conference on Smart Computing (SMARTCOMP)