TY - GEN
T1 - Workflow Provenance in the Computing Continuum for Responsible, Trustworthy, and Energy-Efficient AI
AU - Souza, Renan
AU - Caino-Lores, Silvina
AU - Coletti, Mark
AU - Skluzacek, Tyler J.
AU - Costan, Alexandru
AU - Suter, Frederic
AU - Mattoso, Marta
AU - Da Silva, Rafael Ferreira
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - AI Lifecycle
KW - AI workflows
KW - Artificial Intelligence
KW - Energy-efficient AI
KW - Machine Learning
KW - ML workflows
KW - Provenance
KW - Reproducibility
KW - Responsible AI
KW - Trustworthy AI
UR - http://www.scopus.com/inward/record.url?scp=85205946778&partnerID=8YFLogxK
U2 - 10.1109/e-Science62913.2024.10678731
DO - 10.1109/e-Science62913.2024.10678731
M3 - Conference contribution
AN - SCOPUS:85205946778
T3 - Proceedings - 2024 IEEE 20th International Conference on e-Science, e-Science 2024
BT - Proceedings - 2024 IEEE 20th International Conference on e-Science, e-Science 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 20th IEEE International Conference on e-Science, e-Science 2024
Y2 - 16 September 2024 through 20 September 2024
ER -