What is it about?

Data provenance, a record that describes the origins and processing of data, offers new promises in the increasingly important role of artificial intelligence (AI)-based systems in guiding human decision making. This study outlines existing biases and discusses possible implementations of data provenance to mitigate them. We first review biases stemming from the data's origins and pre-processing. We then discuss the current state of practice, the challenges it presents, and corresponding recommendations to address them. We present a summary highlighting how our recommendations can help establish data provenance and thereby mitigate biases stemming from the data's origins and pre-processing to realize responsible AI-based systems. We conclude with a research agenda suggesting further research avenues.

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Why is it important?

To avoid disastrous outcomes that can result from bias-laden AI systems, responsible AI builds on four important characteristics: fairness, accountability, transparency, and explainability. While the establishment of data provenance may increase short-term costs for organizations, it can provide long-term benefits by instilling trust in the implemented system and its recommendations. Our recommendations are intended to help establish data provenance and mitigate biases stemming from the data's origins and pre-processing to realize responsible AI-based systems.

Perspectives

Writing this article was a great pleasure as it has co-authors with whom I have had great collaborations. I trust that people enjoy reading this article when learning more about the role of data and its provenance when designing AI-based systems.

Karl Werder
University of Cologne

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This page is a summary of: Establishing Data Provenance for Responsible Artificial Intelligence Systems, ACM Transactions on Management Information Systems, June 2022, ACM (Association for Computing Machinery), DOI: 10.1145/3503488.
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