Text and data mining of in-copyright works

By P. Samuelson. 2021

11 citations recorded by the source corpus.

How copyright law might be an impediment to text and data mining research.

Subfields: Information Systems, Literature and Literary Theory, Marketing, Information Extraction, 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 Information Systems

1. Sham M. Kakade
2. Percy Liang
3. Dan Jurafsky
4. Mark A. Lemley
5. Tatsunori B. Hashimoto
6. Xuechen Li
7. Peter Henderson
8. Luke S. Zettlemoyer

In Literature and Literary Theory

1. Percy Liang
2. Dan Jurafsky

In Marketing

1. Percy Liang
2. Dan Jurafsky
3. Mark A. Lemley
4. Tatsunori B. Hashimoto
5. Xuechen Li
6. Peter Henderson

In Information Extraction

1. Sham M. Kakade
2. Percy Liang
3. Dan Jurafsky

In AI Ethics, Fairness & Society

1. Percy Liang
2. Dan Jurafsky
3. Mark A. Lemley
4. Tatsunori B. Hashimoto
5. Xuechen Li
6. 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”
• On Provable Copyright Protection for Generative Models (2023, background): “This work does not examine these fair use issues that arise in the training phase, and we refer the reader to the several legal precedents in this area [Samuelson, 2021].”