Trustworthy AI and Data Lineage

Abstract

AI trustworthiness properties are at the top of concerns for industry, governments, and academia. However, the AI and its models are only as good as the data used to train it. Data lineage could be tracked in many ways, including using metadata, from its generation usage, deployment, and verification. New standards, blueprints, best practices, and repositories for data are required to address requirements for data trustworthiness, such as sustainability, scale, and responsiveness but also ethics, diversity, equity, and inclusion. In this special issue of IEEE Internet Computing, we feature three articles. The first one addresses certification for trustworthy machine-learning-based applications, the second one is on the topic of data and configuration variances in deep learning, and the third one explores balancing trustworthiness and efficiency in AI Systems. We hope that this special issue will increase the community's awareness of the importance of AI trustworthiness through data lineage.