Tabular datasets are ubiquitous across many industries, especially in vital sectors such as healthcare and finance. Such industrial datasets often contain sensitive information, raising privacy and confidentiality issues that preclude their public release and limit their analysis to methods that are compatible with an appropriate anonymization process. We can distinguish between two types of tabular data: static tabular data that corresponds to independent rows in a table, and dynamic tabular data that corresponds to tabular time series, also referred to also as multivariate time series. The machine learning and deep learning communities have devoted considerable effort to learning from static tabular data, as well as generating synthetic static tabular data that can be released as a privacy compliant surrogate of the original data. On the other hand, less effort has been devoted to the more challenging dynamic case, where it is important to also account for the temporal component of the data.

The accuracy and reliability of machine learning algorithms are an important concern for suppliers of artificial intelligence (AI) services, but considerations beyond accuracy, such as safety, security, and provenance, are also critical elements to engender consumers' trust in a service. In this paper, we propose a supplier's declaration of conformity (SDoC) for AI services to help increase trust in AI services. An SDoC is a transparent, standardized, but often not legally required, document used in many industries and sectors to describe the lineage of a product along with the safety and performance testing it has undergone. We envision an SDoC for AI services to contain purpose, performance, safety, security, and provenance information to be completed and voluntarily released by AI service providers for examination by consumers. Importantly, it conveys product-level rather than component-level functional testing. We suggest a set of declaration items tailored to AI and provide examples for two fictitious AI services.