Nithya Sambasivan, Research Scientist, Google Research
Data is a foundational aspect of machine learning (ML) that can impact performance, fairness, robustness, and scalability of ML systems. Paradoxically, while building ML models is often highly prioritized, the work related to data itself is often the least prioritized aspect. This data work can require multiple roles (such as data collectors, annotators, and ML developers) and often involves multiple teams (such as database, legal, or licensing teams) to power a data infrastructure, which adds complexity to any data-related project. As such, the field of human-computer interaction (HCI), which is focused on making technology useful and usable for people, can help both to identify potential issues and to assess the impact on models when data-related work is not prioritized.
In “‘Everyone wants to do the model work, not the data work’: Data Cascades in High-Stakes AI”, published at the 2021 ACM CHI Conference, we study and validate downstream effects from data issues that result in technical debt over time (defined as “data cascades”). Specifically, we illustrate the phenomenon of data cascades with the data practices and challenges of ML practitioners across the globe working in important ML domains, such as cancer detection, landslide detection, loan allocation and more — domains where ML systems have enabled progress, but also where there is opportunity to improve by addressing data cascades. This work is the first that we know of to formalize, measure, and discuss data cascades in ML as applied to real-world projects. We further discuss the opportunity presented by a collective re-imagining of ML data as a high priority, including rewarding ML data work and workers, recognizing the scientific empiricism in ML data research, improving the visibility of data pipelines, and improving data equity around the world.
Origins of Data Cascades
We observe that data cascades often originate early in the lifecycle of an ML system, at the
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