Overview

Data integrity, transparency, and responsible use are crucial for the development of reliable AI applications and models. At Valyu, we are dedicated to fostering trusted AI through trusted data. This GitBook delves into the datacard standard developed here at Valyu, and used within the Valyu Open Data Provenance dashboard. The datacard is structured into three main components, each designed to provide a holistic view of the dataset's lineage, characteristics, and provenance, ensuring a thorough understanding of its origins, transformations, and current state.

Significance of Trusted Data in AI Applications

The reliability and integrity of data are paramount for Artificial Intelligence. Trusted data forms the backbone of effective AI models, ensuring their accuracy, fairness, and robustness. A full lineage, as detailed in the datacard, allows for the tracing of potential biases back to the data origins, offering insights into the dataset's creation and evolution. This traceability is crucial for diagnosing and rectifying biases in AI models, thereby enhancing their performance and reliability.

Importance of Provenance

Provenance plays a vital role in establishing the trustworthiness of data. By providing a detailed account of the dataset's history, including its sources, transformations, and annotations, provenance ensures transparency and accountability in data handling. Through detailed documentation of provenance, characteristics, and lineage, the datacards offer a transparent and auditable trail of a dataset's journey, enabling stakeholders to assess its quality, origin, and transformations. This transparency is instrumental in building trust in data, ensuring its ethical use, and facilitating compliance with regulatory standards.

Overview of the Datacard Components

The datacard encompasses several key components, each contributing to a deep understanding of the dataset:

• Provenance: Outlines the dataset's history, from its origins to its present form, including any modifications and the parties involved in its creation and curation.

• Characteristics: Describes the dataset's attributes, such as language, format, and metrics relevant to text-based datasets, with plans to extend these metrics to other data modalities like video and audio.

• Lineage: Provides a detailed account of the dataset's journey, capturing every transformation and transfer it undergoes, which is essential for tracking data quality and integrity.

As a result of these components, the datacard presents a comprehensive picture of the dataset, enabling data practitioners to assess its suitability for various AI tasks and ensuring the development of trustworthy AI models.