Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructure

TL;DR

This paper proposes a comprehensive framework for increasing transparency and accountability in machine learning dataset development by applying best practices from software engineering to document and communicate each stage of the process.

Contribution

It introduces a structured, lifecycle-based framework for dataset development transparency, addressing an accountability gap in AI systems.

Findings

Framework supports decision-making and accountability

Documents facilitate communication among stakeholders

Highlights the importance of careful data work

Abstract

Rising concern for the societal implications of artificial intelligence systems has inspired demands for greater transparency and accountability. However the datasets which empower machine learning are often used, shared and re-used with little visibility into the processes of deliberation which led to their creation. Which stakeholder groups had their perspectives included when the dataset was conceived? Which domain experts were consulted regarding how to model subgroups and other phenomena? How were questions of representational biases measured and addressed? Who labeled the data? In this paper, we introduce a rigorous framework for dataset development transparency which supports decision-making and accountability. The framework uses the cyclical, infrastructural and engineering nature of dataset development to draw on best practices from the software development lifecycle. Each stage of the data development lifecycle yields a set of documents that facilitate improved communication and decision-making, as well as drawing attention the value and necessity of careful data work. The proposed framework is intended to contribute to closing the accountability gap in artificial intelligence systems, by making visible the often overlooked work that goes into dataset creation.