CaTE Data Curation for Trustworthy AI

TL;DR

This paper offers practical guidance and a structured approach for data scientists to enhance trustworthiness in AI systems through effective data curation, integrating tools, methods, and best practices from academic literature.

Contribution

It provides a comprehensive, step-by-step framework for data curation aimed at improving AI trustworthiness, including analysis of tools, approaches, and open-source implementations.

Findings

Structured data curation steps for trustworthy AI

Evaluation of open-source tools for data quality and bias mitigation

Guidelines for integrating trustworthiness practices into AI development

Abstract

This report provides practical guidance to teams designing or developing AI-enabled systems for how to promote trustworthiness during the data curation phase of development. In this report, the authors first define data, the data curation phase, and trustworthiness. We then describe a series of steps that the development team, especially data scientists, can take to build a trustworthy AI-enabled system. We enumerate the sequence of core steps and trace parallel paths where alternatives exist. The descriptions of these steps include strengths, weaknesses, preconditions, outcomes, and relevant open-source software tool implementations. In total, this report is a synthesis of data curation tools and approaches from relevant academic literature, and our goal is to equip readers with a diverse yet coherent set of practices for improving AI trustworthiness.