Algorithmic Fair Use

By Dan L. Burk. 2017

22 citations recorded by the source corpus.

Subfields: AI Ethics, Fairness & Society

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In AI Ethics, Fairness & Society

1. Percy Liang
2. Dan Jurafsky
3. Mark A. Lemley
4. Tatsunori B. Hashimoto
5. Xuechen Li
6. Mike Ananny
7. Alexandra Sasha Luccioni
8. Peter Henderson

Citations in the corpus

• Foundation Models and Fair Use (2023, background): “On the legal side, a large body of work has covered potential legal risks and challenges of machine learning (Sobel, 2017; Burk, 2019; Lemley & Casey, 2019; Gillotte, 2019; Lemley & Casey, 2020; Franceschelli & Musolesi, 2022; Guadamuz, 2017; Grimmelmann, 2015; McCann, 2021; McJohn & McJohn, 2020; L”
• A Framework for Deprecating Datasets: Standardizing Documentation, Identification, and Communication (2021, background): “…have also been brought against image dataset providers; however, they have yet to succeed, especially in the United States where various fair use legal decisions have created broad permissions for mass digitization and analysis of creative works for machine learning purposes [1–3, 20, 54].”