Software is essential to modern vehicles, especially electric and autonomous vehicles (EVs and AVs). Software controls a myriad of vehicle functions, including suspension, braking, steering, power delivery, battery charging and discharging, user interfaces, media, Internet connectivity, autonomous driving, speed and fuel monitoring, accident avoidance, and lane positioning. Nearly a quarter of all modern vehicle commercial applications are based on open-source software (OSS), and this figure is only expected to grow to encompass many of these vehicle functions.
There are a number of open-source automotive initiatives around the world working on various projects for EVs and AVs, taking advantage of the low cost and flexibility of OSS. However, these advantages also come with major risks that automobile manufacturers and other developers in the industry must keep in mind as they continue to grow the suite of software that controls our vehicles.
OSS is computer software that is distributed with its source code, making it available for use, modification, and distribution. While it is “open,” OSS is still considered an original work of authorship subject to copyright and can only be used per the license terms imposed by the OSS copyright owner. Violations of that license could expose a company to breach of contract and copyright infringement claims, possibly leading to an injunction, monetary damages, or even contamination of a company’s proprietary code base with unlicensed third-party code. Even with the risks, OSS is essentially ubiquitous. In 2016, there were approximately 84 open-source components per commercial application. By 2020, that number had grown to 528.
OSS has many benefits. It is low or no cost, can be rapidly deployed, is modifiable and maintainable, and can be (and is) continually improved by the coding community. Manufacturers like using OSS because it can lower development costs, speed up production time, and accelerate innovation. However, as noted above, the risks of OSS can be substantial. For example, vulnerabilities in OSS can compromise applications and expose confidential information. Additionally, poor OSS license compliance can trigger litigation. Even more alarming, and of particular concern for EVs and AVs, poor OSS quality can impact vehicle and passenger safety.
To mitigate these risks, auto manufacturers and other developers can take proactive steps such as adopting policies and best practices to detect and address security vulnerabilities and avoid license conflicts. Companies should also train their developers on the risks of OSS and diligently track OSS use throughout the manufacturing pipeline.
In open-source litigation, there are three categories of plaintiffs: rights holders (those that own or have rights to copyrighted software subject to an open-source license), non-rights holders (those without rights but who claim injury based on noncompliance with an open-source license), and other third parties (anyone claiming to be harmed from the use of open-source software in violation of a statutory, regulatory, or other legal requirement). The key issues this litigation usually presents are standing, copyright preemption, covenants versus conditions, and damages (either monetary or some sort of injunctive relief or specific performance).
The past year has seen an explosion in the advancement of generative artificial intelligence (AI) programs such as ChatGPT, but the auto industry has been using traditional and generative AI for many years in a number of use cases – from vehicle navigation and driver assistance to design and manufacturing, fuel efficiency, and even predictive maintenance. But using this rapidly developing technology comes with legal liabilities. For example, when developing and training AI programs, companies should be cognizant of the source and any restrictions surrounding the data sets, which may have access restrictions and underlying third-party rights.
Auto manufacturers are also using generative AI to create new content, including product designs, driver interfaces, maps and other navigation aids, and even marketing collateral. Key legal questions are currently being hashed out in the courts surrounding this content, with the lawsuits generally focused on: infringement allegations in the use of data sets in AI training; infringement allegations against generated output; and ownership/enforcement of generated output. Within the copyright infringement claims, there are generally claims for both direct infringement (for use in training or for output substantially similar to copyrighted work) and secondary (contributory and vicarious) infringement. Aside from moving to dismiss the secondary infringement claims, defendants in those infringement cases have primarily argued that the use of protected content to train AI is a “fair use” under the Copyright Act.
As to whether AI output can be protected by copyright at all, both the Copyright Office and courts have weighed in that this depends on whether there has been sufficient human authorship. For example, in a recent matter involving the copyright registration of a graphic novel developed in collaboration with an AI program, the Copyright Office only allowed copyright protection for the human authored portions: the text and the overall compilation. The Copyright Office thereafter issued a policy statement that moving forward “the Office will consider whether the AI contributions are the result of ‘mechanical reproduction’ or an author’s ‘own original mental conception, to which [the author] gave visible form.’”
As this caselaw develops, auto manufacturers should remain thoughtful when using AI and consider implementing the following practices to reduce the risk of infringement:
When using AI tools:
The proliferation of OSS in modern vehicles, particularly EVs and AVs, and the rise of generative AI signify a transformative era for the automobile industry. As reliance on OSS grows, auto manufacturers will need to navigate the complex landscape of benefits and pitfalls associated with this collaborative model. Similarly, as more aspects of the manufacturing pipeline incorporate AI, companies should ensure proper policies are in place to avoid accidental infringement and ensure ownership and enforceability. Working with legal counsel and taking proactive measures will be key to mitigating these risks.
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