Arash Jafari, Dr.Kian Rostami, Mohamed Haaji Ali, 2026. "Unifying Data Provenance and Machine Learning for Reliable Intelligence in Large-Scale Systems", Journal of Machine Learning and Computational Intelligence (JMLCI) 1(1): 37-54.

As the scale of our digital systems have grown, unprecedented sizes of data are created and processed among several distributed computing environments. This data is now being transformed into intelligence for action, guiding decisions, automation and optimization via machine learning technologies of all kinds. Nonetheless, the success of ML systems greatly depends on how accurate, reliable explainable and truthful these data can be. The growing complexity and heterogeneity of data has raised the most crucial challenges to be addressed, in the scope of data integrity, lineage, accountability, reproducibility and governance problems. Data provenance shows how, when and where the data is captured along with its transformation history and ownership details and has become one of the key techniques to deal with these unique challenges. Abstract Provenance frameworks play an important role to assure transparency as they record the data from which process it is acquired, how it has been processed along with modifications performed and also how the data was used; these all lead in computing quality checks for analytical results. Data provenance + machine learning:An important frontier for trustworthy and explainable forms of intelligence, trustworthiness models, and ultimately metrics for resilience.
This paper explores unifying data provenance and machine learning to create trustworthy intelligence in large-scale environments. This study shows a way to combine provenance-aware architectures and knowledge representation frameworks with explainable artificial intelligence and advanced machine learning techniques for an improved quality assurance of intelligent decision-making processes. Through integrated provenance into learning workflows, organizations can implement model transparency, detect and identify data quality issues and biases early in the process, enforce reproducibility, and improve governance. Based on this evidence, it proceeds to investigate scalable provenance analytics, distributed intelligence frameworks, knowledge graph integration and autonomously learning systems and mindset neuro-symbolic reasoning based techniques for developing smart ecosystem beacons that can potentially enable the creation of responsible intelligent ecosystems. Attention will also be paid to emerging technologies such as Large Language Models, provenance-guided learning architectures and explainable machine intelligence with a deeper understanding of context & accountability up to October 2023. We demonstrate that the combination of data provenance and machine learning can make a convincing foundation for developing reliable intelligence systems which can operate at large scale in today{'}s dynamic environments where transparency, trust and sustainability matter over decades.

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