Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructure (2010.13561v2)

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.

• The paper presents a structured lifecycle for dataset development, covering specification, design, implementation, testing, and maintenance.
• It adopts software engineering practices to ensure robust documentation and mitigate biases in ML datasets.
• The approach fosters transparency and stakeholder engagement, paving the way for automated tools and future empirical validation.

Essay: Advancing Accountability in Machine Learning Datasets through Engineering Practices

The paper "Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructure" provides a comprehensive framework to address the accountability challenges of dataset creation in ML. As AI systems continue to integrate into high-stakes domains, the importance of robust dataset development and management practices becomes more pronounced. This work draws on methodologies from software engineering to propose a cycle of dataset development that enhances transparency and accountability.

The central thesis of the paper is the pivotal role played by datasets as a form of technical infrastructure within AI systems. By framing datasets as infrastructure, the authors underscore the need for methodological rigor comparable to that applied in software engineering. The paper suggests a complete lifecycle for dataset development, including requirements specification, design, implementation, testing, and maintenance. This structured approach mirrors the well-established software development lifecycle, with a heavy emphasis on documentation at every stage.

Key Contributions

The authors introduce several critical documents analogous to those used in software engineering to better manage datasets:

1. Dataset Requirements Specification: This document outlines the needs and intended use-cases for a dataset before its creation, ensuring that data-driven solutions are well-motivated and systematically planned.
2. Dataset Design Document: Design decisions and trade-offs are meticulously recorded to provide transparency about how a dataset is built, aligning with the specified requirements.
3. Dataset Implementation Diary: Continuous documentation of the implementation process captures unforeseen challenges and decisions, aiding future audits and evaluations.
4. Dataset Testing Report: This document details the processes used to verify that the dataset meets specified requirements and uncovers potential biases and limitations early in the lifecycle.
5. Dataset Maintenance Plan: This anticipates the data lifecycle post-deployment, outlining strategies for updates and corrections as new knowledge and challenges emerge.

Implications

The implications of this framework are manifold. In practice, it provides a structured approach to ensure datasets are developed with accountability, reducing the likelihood of biased or unverified data impacting ML models. Theoretically, it emphasizes treating datasets as critical engineering components, challenging the current undervaluation and underdocumented nature of dataset work in the broader ML research ecosystem.

The authors link dataset practices to established infrastructure governance models, underscoring the importance of stakeholder engagement and decision transparency. Drawing parallels between dataset development and infrastructure projects, they assert that robust governance mechanisms can preemptively manage risks and foster public trust.

Future Directions

While the paper provides a solid foundation for dataset accountability, future research could explore the development of automated tools for each stage of the dataset lifecycle. Additionally, empirical studies evaluating the impact of these documentation practices on ML outcomes would substantiate this framework's utility. There is also room for increased focus on cross-disciplinary collaborations to enrich datasets with perspectives from data ethics, social sciences, and domain-specific experts.

In conclusion, this paper advocates for a pivotal cultural and methodological shift in how datasets are approached within ML. Emphasizing careful documentation and robust design practices, it aligns with broader movements toward responsible AI development. As AI systems become more complex, the approach detailed in this paper offers a scalable solution to one of the field's most pressing ethical challenges.