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We thought data to be simply given, but reality tells otherwise; it iscostly, situation-dependent, and muddled with dilemmas, constantly requiringhuman intervention. The ML community's focus on quality data is increasing inthe same vein, as good data is vital for successful ML systems. Nonetheless,few works have investigated the dataset builders and the specifics of what theydo and struggle to make good data. In this study, through semi-structuredinterviews with 19 ML experts, we present what humans actually do and considerin each step of the data construction pipeline. We further organize theirstruggles under three themes: 1) trade-offs from real-world constraints; 2)harmonizing assorted data workers for consistency; 3) the necessity of humanintuition and tacit knowledge for processing data. Finally, we discuss why suchstruggles are inevitable for good data and what practitioners aspire, towardproviding systematic support for data works.

The Grind for Good Data: Understanding ML Practitioners' Struggles and Aspirations in Making Good Data