A growing body of work shows that many problems in fairness, accountability, transparency, and ethics in machine learning systems are rooted in decisions surrounding the data collection and annotation process. We argue that a new specialization should be formed within machine learning that is focused on methodologies for data collection and annotation: efforts that require institutional frameworks and procedures. Specifically for sociocultural data, parallels can be drawn from archives and libraries.

Archives are the longest standing communal effort to gather human information and archive scholars have already developed the language and procedures to address and discuss many challenges pertaining to data collection such as consent, power, inclusivity, transparency, and ethics privacy. We discuss these five key approaches in document collection practices in archives that can inform data collection in sociocultural machine learning.

Timnit Gebru is the technical co-lead of the Ethical Artificial Intelligence Team at Google. She works on algorithmic bias and data mining. Timnit earned her doctorate under the supervision of Fei-Fei Li at Stanford University in 2017. She is an advocate for diversity in technology and is the cofounder of Black in AI, a community of black researchers working in artificial intelligence.