It's Not Privacy, and It's Not Fair | Stanford Law Review
Classification is the foundation of targeting and tailoring information and experiences to individuals. Big data promises—or threatens—to bring classification to an increasing range of human activity. While many companies and government agencies foster an illusion that classification is (or should be) an area of absolute algorithmic rule—that decisions are neutral, organic, and even automatically rendered […]
Classification is the foundation of targeting and tailoring information and experiences to individuals. Big data promises—or threatens—to bring classification to an increasing range of human activity. While many companies and government agencies foster an illusion that classification is (or should be) an area of absolute algorithmic rule—that decisions are neutral, organic, and even automatically rendered without human intervention—reality is a far messier mix of technical and human curating. Both the datasets and the algorithms reflect choices, among others, about data, connections, inference
related reading
- The Limits of Dataissues.org
- Microsoft Word - 03_Trites Volume 11 issue 2 Final.docxgmj-canadianedition.ca
- Differential Privacy: Issues for Policymakerssimons.berkeley.edu
- Classificationfairmlbook.org
- Algorithmic Accountabilitydatasociety.net
- Statistical Fairness - Turing Commonsalan-turing-institute.github.io
- Americans and Privacy in 2019 - Concerned, Confused and Feeling Lack of Control Over Their Personal Informationpewresearch.org
- Big data may not know your name, but it knows everything elsenews.ycombinator.com
- Big Data is Deadmotherduck.com
- 0cfc9404f89400c5ed897035e0d3748c-Paper-Conference.pdfproceedings.neurips.cc
- Privacy and Information Technology (Stanford Encyclopedia of Philosophy)plato.stanford.edu
- [arxiv] an algorithmic framework for fairness elicitationarxiv.org