Lessons from Archives: Strategies for Collecting Sociocultural Data in Machine Learning. Gebru, T. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, of KDD '20, pages 3609, New York, NY, USA, August, 2020. Association for Computing Machinery. ![Lessons from Archives: Strategies for Collecting Sociocultural Data in Machine Learning [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@inproceedings{gebru2020,
address = {New York, NY, USA},
series = {{KDD} '20},
title = {Lessons from {Archives}: {Strategies} for {Collecting} {Sociocultural} {Data} in {Machine} {Learning}},
isbn = {978-1-4503-7998-4},
shorttitle = {Lessons from {Archives}},
url = {https://doi.org/10.1145/3394486.3409559},
doi = {10.1145/3394486.3409559},
abstract = {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.},
urldate = {2024-01-02},
booktitle = {Proceedings of the 26th {ACM} {SIGKDD} {International} {Conference} on {Knowledge} {Discovery} \& {Data} {Mining}},
publisher = {Association for Computing Machinery},
author = {Gebru, Timnit},
month = aug,
year = {2020},
keywords = {ai, archives, ethics, fairness, machine learning, sociocultural data},
pages = {3609},
}
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