Manifold-Aligned Counterfactual Explanations for Neural Networks
Georgia Perakis, Wei Sun, et al.
AISTATS 2024
Data analytics and artificial intelligence are extensively used by enterprises today and they increasingly span organization boundaries. Such collaboration between organizations today happens in an ad hoc manner, with very little visibility and systemic control on who is accessing the data, how, and for what purpose. When sharing data and AI models with other organizations, the owners desire the ability to control access, have visibility into the entire data pipeline and lineage, and ensure integrity. In this work, we present a decentralized trusted data and model platform for collaborative AI, that leverages blockchain as an immutable metadata store of data and model resources and operations performed on them, to support and enforce ownership, authenticity, integrity, lineage and auditability properties. Smart contracts enforce policies specified on data, including hierarchical and composite policies that are uniquely enabled by the use of blockchain. We demonstrate that our system is light-weight and can support over 1000 transactions per second with sub-second latency, significantly lower than the time taken to execute data pipelines.
Georgia Perakis, Wei Sun, et al.
AISTATS 2024
Akifumi Wachi, Yanan Sui
ICML 2020
Ioana Baldini Soares, Chhavi Yadav, et al.
ACL 2023
Shashank Srikant, Sijia Liu, et al.
ICLR 2021