Whose Text Is It Anyway? Exploring BigCode, Intellectual Property, and Ethics

Madiha Zahrah Choksi, David Goedicke
4/6/2023
cs.CYcs.AI
position

No confident code implementation found for this paper.

Abstract

Intelligent or generative writing tools rely on large language models that recognize, summarize, translate, and predict content. This position paper probes the copyright interests of open data sets used to train large language models (LLMs). Our paper asks, how do LLMs trained on open data sets circumvent the copyright interests of the used data? We start by defining software copyright and tracing its history. We rely on GitHub Copilot as a modern case study challenging software copyright. Our conclusion outlines obstacles that generative writing assistants create for copyright, and offers a practical road map for copyright analysis for developers, software law experts, and general users to consider in the context of intelligent LLM-powered writing tools.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Cite this paper

@article{choksi2023whose,
  title  = {Whose Text Is It Anyway? Exploring BigCode, Intellectual Property, and Ethics},
  author = {Madiha Zahrah Choksi and David Goedicke},
  year   = {2023},
  eprint = {2304.02839},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2304.02839v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2304.02839.svg)](https://codeofpaper.com/paper/2304.02839)

Discussion