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On the genealogy of machine learning datasets: A critical history of ImageNet
| Content Provider | SAGE Publishing |
|---|---|
| Author | Denton, Emily Hanna, Alex Amironesei, Razvan Smart, Andrew Nicole, Hilary |
| Copyright Year | 2021 |
| Abstract | In response to growing concerns of bias, discrimination, and unfairness perpetuated by algorithmic systems, the datasets used to train and evaluate machine learning models have come under increased scrutiny. Many of these examinations have focused on the contents of machine learning datasets, finding glaring underrepresentation of minoritized groups. In contrast, relatively little work has been done to examine the norms, values, and assumptions embedded in these datasets. In this work, we conceptualize machine learning datasets as a type of informational infrastructure, and motivate a genealogy as method in examining the histories and modes of constitution at play in their creation. We present a critical history of ImageNet as an exemplar, utilizing critical discourse analysis of major texts around ImageNet’s creation and impact. We find that assumptions around ImageNet and other large computer vision datasets more generally rely on three themes: the aggregation and accumulation of more data, the computational construction of meaning, and making certain types of data labor invisible. By tracing the discourses that surround this influential benchmark, we contribute to the ongoing development of the standards and norms around data development in machine learning and artificial intelligence research. |
| Related Links | https://journals.sagepub.com/doi/pdf/10.1177/20539517211035955?download=true |
| ISSN | 20539517 |
| Issue Number | 2 |
| Volume Number | 8 |
| Journal | Big Data & Society (BDS) |
| e-ISSN | 20539517 |
| DOI | 10.1177/20539517211035955 |
| Language | English |
| Publisher | Sage Publications UK |
| Publisher Date | 2021-09-24 |
| Publisher Place | London |
| Access Restriction | Open |
| Rights Holder | © The Author(s) 2021 |
| Subject Keyword | artificial intelligence AI ethics algorithmic fairness big data Machine learning genealogy |
| Content Type | Text |
| Resource Type | Article |
| Subject | Information Systems and Management Library and Information Sciences Information Systems Computer Science Applications Communication |
