The Free Software Foundation Rejects RAIL
The Free Software Foundation (FSF) has officially classified RAIL as nonfree. The organization cites use-based restrictions as a direct violation of the core tenets of software freedom. According to the FSF Licensing and Compliance Lab, any license that imposes conditions on how a program is used—such as prohibiting specific “anti-social” activities—fails to meet the definition of free software. The foundation argues that such terms deny users the fundamental freedom to run software for any purpose.
The Philosophy of Freedom 0
At the heart of the dispute is “freedom 0,” the freedom to use the program for any purpose. While RAIL are marketed as “ethical” for attempting to prevent harmful machine learning applications, the FSF contends these restrictions are fundamentally incompatible with free software.
By conditioning software use on user behavior, these licenses grant licensors the authority to monitor and judge conduct. The FSF warns this creates a dangerous precedent where software creators act as private arbiters of law. Because these restrictions are often vaguely defined, they risk being used to protect the interests of the licensor rather than the public, effectively outsourcing legal authority to private entities instead of democratically elected bodies.
Operational Risks and Legal Friction
Beyond philosophical conflicts, the FSF highlights practical dangers. If use-restricted licenses become standard, the burden on users would increase significantly. Organizations and individuals would face the requirement of continuous audits to ensure compliance with shifting, subjective policies, creating a high risk of copyright litigation.
Furthermore, the FSF notes that individuals intent on using software for harmful purposes are unlikely to be deterred by license language. If existing laws enforced by governments fail to stop specific bad actors, a copyright license is unlikely to succeed where the legal system has not. Instead, these licenses create friction for legitimate users and prevent the open sharing of code between proprietary and free software ecosystems.
Demanding Transparency Over Restriction
The FSF emphasizes that RAIL do not address the most critical ethical issues facing machine learning. Even if a license includes “anti-harm” clauses, it does not guarantee that the software is transparent or user-controlled. True ethical machine learning, the FSF argues, requires:
* Complete training inputs.
* Training configuration settings.
* Access to the trained model.
* The source code used for training, testing, and running the tools.
Without these, users remain locked into proprietary systems. The FSF also critiques the industry’s use of the term “artificial intelligence,” arguing that it obscures the reality that these programs lack actual intelligence or understanding. This marketing, the FSF suggests, misleads less technical users into placing undue trust in automated outputs.
Advocating for Copyleft Safeguards
To address social injustices, the FSF advocates for the continued use of strong copyleft licenses, such as the GNU General Public License (GNU GPL). Unlike RAIL, which restrict the user, copyleft licenses ensure that software remains free for everyone. This prevents any single entity from gaining control over the technology or the user.
The FSF maintains that the most effective path forward is to support freedom-respecting tools and to entrust copyrights to organizations committed to the public good. By keeping software free and interoperable, the community ensures that developers can collaborate on solutions to real-world problems without the threat of restrictive, privately-enforced usage policies.