OpenAI Faces Early Appeal in First AI Copyright Suit From Coders; Bloomberg Law, September 30, 2024

Isaiah Poritz , Bloomberg Law; OpenAI Faces Early Appeal in First AI Copyright Suit From Coders

"OpenAI Inc. and Microsoft Corp.‘s GitHub will head to the country’s largest federal appeals court to resolve their first copyright lawsuit from open-source programmers who claim the companies’ AI coding tool Copilot violates a decades-old digital copyright law.

Judge Jon S. Tigar granted the programmers’ request for a mid-case turn to the US Court of Appeals for the Ninth Circuit, which must determine whether OpenAI’s copying of open-source code to train its AI model without proper attribution to the programmers could be a violation of the Digital Millennium Copyright Act...

The programmers argued that Copilot fails to include authorship and licensing terms when it outputs code. Unlike other lawsuits against AI companies, the programmers didn’t allege that OpenAI and GitHub engaged in copyright infringement, which is different from a DMCA violation."

A new tool for copyright holders can show if their work is in AI training data; MIT Technology Review, July 25, 2024

• Melissa Heikkiläarchive page

, MIT Technology Review; A new tool for copyright holders can show if their work is in AI training data

"Since the beginning of the generative AI boom, content creators have argued that their work has been scraped into AI models without their consent. But until now, it has been difficult to know whether specific text has actually been used in a training data set. 

Now they have a new way to prove it: “copyright traps” developed by a team at Imperial College London, pieces of hidden text that allow writers and publishers to subtly mark their work in order to later detect whether it has been used in AI models or not. The idea is similar to traps that have been used by copyright holders throughout history—strategies like including fake locations on a map or fake words in a dictionary. 

These AI copyright traps tap into one of the biggest fights in AI. A number of publishers and writers are in the middle of litigation against tech companies, claiming their intellectual property has been scraped into AI training data sets without their permission. The New York Times’ ongoing case against OpenAI is probably the most high-profile of these.  

The code to generate and detect traps is currently available on GitHub, but the team also intends to build a tool that allows people to generate and insert copyright traps themselves." 

As Computer Coding Classes Swell, So Does Cheating; New York Times, May 29, 2017

Jess Bidgood and Jeremy B. Merrill, New York Times; 

As Computer Coding Classes Swell, So Does Cheating

"In interviews, professors and students said the causes were not hard to pin down.

To some students drawn to the classes, coding does not come easily. The coursework can be time-consuming. Troves of code online, on sites like GitHub, may have answers to the very assignment the student is wrestling with, posted by someone who previously took the course.

“You’ve got kids who were struggling with spending a third of their time on their problem sets with the option to copy from the internet,” said Jackson Wagner, who took the Harvard course in 2015 and was not accused of copying. “That’s the reason why people cheat.”

Complicating matters is the collaborative ethos among programmers, which encourages code-sharing in ways that might not be acceptable in a class. Professors also frequently allow students to discuss problems among themselves, but not to share actual code, a policy that some students say creates confusion about what constitutes cheating."