Abstract
As Artificial Intelligence (AI) becomes more pervasive in our society, it is crucial to develop, deploy, and assess Responsible and Trustworthy AI (RTAI) models, i.e., those that consider not only accuracy but also other aspects, such as explainability, fairness, and energy efficiency. Workflow provenance data have historically enabled critical capabilities towards RTAI. Provenance data derivation paths contribute to responsible workflows through transparency in tracking artifacts and resource consumption. Provenance data are well-known for their trustworthiness helping explainability, reproducibility, and accountability. However, there are complex challenges to achieve RTAI, which are further complicated by the heterogeneous infrastructure in the computing continuum (Edge-Cloud-HPC) used to develop and deploy models. As a result, a significant research and development gap remains between workflow provenance data management and RTAI. In this paper, we present a vision of the pivotal role of workflow provenance in supporting RTAI and discuss related challenges. We present a schematic view between RTAI and provenance, and highlight open research directions.
| Original language | English |
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| Title of host publication | Proceedings - 2024 IEEE 20th International Conference on e-Science, e-Science 2024 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9798350365610 |
| DOIs | |
| State | Published - 2024 |
| Event | 20th IEEE International Conference on e-Science, e-Science 2024 - Osaka, Japan Duration: Sep 16 2024 → Sep 20 2024 |
Publication series
| Name | Proceedings - 2024 IEEE 20th International Conference on e-Science, e-Science 2024 |
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Conference
| Conference | 20th IEEE International Conference on e-Science, e-Science 2024 |
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| Country/Territory | Japan |
| City | Osaka |
| Period | 09/16/24 → 09/20/24 |
Funding
This manuscript has been authored in part by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a non-exclusive, paid up, irrevocable, worldwide license to publish or reproduce the published form of the manuscript, or allow others to do so, for U.S. Government purposes. The DOE will provide public access to these results in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).
Keywords
- AI Lifecycle
- AI workflows
- Artificial Intelligence
- Energy-efficient AI
- ML workflows
- Machine Learning
- Provenance
- Reproducibility
- Responsible AI
- Trustworthy AI