Abstract
Traceability is considered a key requirement for trustworthy artificial intelligence (AI), related to the need to maintain a complete account of the provenance of data, processes, and artifacts involved in the production of an AI model. Traceability in AI shares part of its scope with general purpose recommendations for provenance as W3C PROV, and it is also supported to different extents by specific tools used by practitioners as part of their efforts in making data analytic processes reproducible or repeatable. Here, we review relevant tools, practices, and data models for traceability in their connection to building AI models and systems. We also propose some minimal requirements to consider a model traceable according to the assessment list of the High-Level Expert Group on AI. Our review shows how, although a good number of reproducibility tools are available, a common approach is currently lacking, together with the need for shared semantics. Besides, we have detected that some tools have either not achieved full maturity, or are already falling into obsolescence or in a state of near abandonment by its developers, which might compromise the reproducibility of the research trusted to them.
Highlights
The High-Level Expert Group on artificial intelligence (AI) (AI HLEG) recently released a document with guidelines to attain “trustworthy AI” [1], mentioning seven key requirements: (1) human agency and oversight, (2) technical robustness and safety, (3) privacy and data governance,(4) transparency, (5) diversity, non-discrimination, and fairness, (6) environmental and societal well-being, and (7) accountability
In an attempt to contribute to advancing practice from automation to comprehensive traceability, we review in this paper the models and tools oriented to document AI systems under the light of the AI HLEG guidelines, contributing to the field by providing an overview of the strengths and weaknesses of such models and tools
We have addressed the main elements required for traceability if we aim for AI systems that are fully replicable and allow for comparison and contrast, which requires a degree of semantic interoperability
Summary
The High-Level Expert Group on AI (AI HLEG) recently released a document with guidelines to attain “trustworthy AI” [1], mentioning seven key requirements: (1) human agency and oversight, (2) technical robustness and safety, (3) privacy and data governance,. Reproducibility (Different team, different experimental setup): The measurement can be obtained with stated precision by a different team and a different measuring system, in a different location on multiple trials For computational experiments, this means that an independent group can obtain the same result using artifacts that they develop completely independently. While reproducibility could be considered as the final objective, the guidelines for trustworthy AI by the AI HLEG are firstly concerned about assuring its intermediate step, replicability, allowing different individuals or teams to replicate a well-documented experiment obtaining the same (or similar) result using the same data.
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