Building Trustworthy AI Pipelines for Big Data

Why TrustAI-BD?

Modern big data pipelines do not operate in isolation — they increasingly span teams, institutions, and organizational boundaries. Datasets, intermediate artifacts, trained models, and downstream decisions flow across organizational boundaries, between teams, and through multi-institution collaborations, as seen in federated healthcare analytics, financial risk platforms, and large-scale scientific consortia.

Unlike traditional deterministic query engines, learned models are inherently stochastic. When an AI-augmented pipeline flags a fraudulent transaction, recommends a treatment plan, or triggers an infrastructure response, the inability to audit the chain of data, model versions, and intermediate decisions is not an academic limitation — it is an operational risk.

This workshop addresses the foundational question: in a world of shared, AI-augmented data pipelines, how do we verify that results are correct, and how do we ensure they can be reproduced?