When It’s Your Data But Another’s Stack, Who Owns The Trained AI Model?

Cloud-based machine learning algorithms, made available as a service, have opened up the world of artificial intelligence to companies without the resources to organically develop their own AI models. Tech companies that provide these services promise to help companies extract insights from the company’s unique customer, employee, product, business process, and other data, and to use those insights to improve decisions, recommendations, and predictions without the company having an army of data scientists and full stack developers. Simply open an account, provide data to the service’s algorithms, train and test an algorithm, and then incorporate the final model into the company’s toolbox.

While it seems reasonable to assume a company owns a model it develops with its own data–even one based on an algorithm residing on another’s platform–the practice across the industry is not universal. Why this matters is simple: a company’s model (characterized in part by model parameters, network architecture, and architecture-specific hyperparameters associated with the model) may provide the company with an advantage over competitors. For instance, the company may have unique and proprietary data that its competitors do not have. If a company wants to extract the most value from its data, it should take steps to not only protect its valuable data, but also the models created based on that data.

How does a company know if it has not given away any rights to its own data uploaded to another’s cloud server, and that it owns the models it created based on its data? Conversely, how can a company confirm the cloud-based machine learning service has not reserved any rights to the model and data for its own use? The answer, of course, is likely embedded in multiple terms of service, privacy, and user license agreements that apply to the use of the service. If important provisions are missing, vague, or otherwise unfavorable, a company may want to look at alternative cloud-based platforms.

Consider the following example. Suppose a company wants to develop an AI model to improve an internal production process, one the company has enhanced over the years and that gives it a competitive advantage over others. Maybe its unique data set derives from a trade secret process or reflects expertise that its competitors could not easily replicate. With data in hand, the company enters into an agreement with a cloud-based machine learning service, uploads its data, and builds a unique model from the service’s many AI technologies, such as natural language processing (NLP), computer vision classifiers, and supervised learning tools. Once the best algorithms are selected, the data is used to train them and a model is created. The model can then be used in the company’s operations to improve efficiency and cut costs.

Now let us assume the cloud service provider’s terms of service (TOS) states something like the following hypothetical:

“This agreement does not impliedly or otherwise grant either party any rights in or to the other’s content, or in or to any of the other’s trade secret or rights under intellectual property laws. The parties acknowledge and agree that Company owns all of its existing and future intellectual property and other rights in and concerning its data, the applications or models Company creates using the services, and Company’s project information provided as part of using the service, and Service owns all of its existing and future intellectual property and other rights in and to the services and software downloaded by Company to access the services. Service will not access nor use Company’s data, except as necessary to provide the services to Company.”

These terms would appear to generally protect certain of the company’s rights and interest in its data and any models created using the company’s data, and further the terms indicate the machine learning service will not use the company’s data and the model trained using the data, except to provide the service. That last part–the exception–needs careful attention, because how a company defines the services it performs can be stated broadly.

Now consider the following additional hypothetical TOS:

“Company acknowledges that Service may access Company’s data submitted to the service for the purpose of developing and improving the service, and any other of Service’s current, future, similar, or related services, and Company agrees to grant Service, its licensees, affiliates, assigns, and agents an irrevocable, perpetual right and permission to use Company’s data, because without those rights and permission Service cannot provide or offer the services to Company.”

The company may not be comfortable agreeing to those terms, unless the terms are superseded with other, more favorable terms in another applicable agreement related to using the cloud-based service.

So while AI may be “the new electricity” powering large portions of the tech sector today, data is an important commodity all on its own, and so are the models behind an AI company’s products. So don’t forget to review the fine print before uploading company data to a cloud-based machine learning service.

Artificial Intelligence Won’t Achieve Legal Inventorship Status Anytime Soon

Imagine a deposition in which an inventor is questioned about her conception and reduction to practice of an invention directed to a chemical product worth billions of dollars to her company. Testimony reveals how artificial intelligence software, assessing huge amounts of data, identified the patented compound and the compound’s new uses in helping combat disease. The inventor states that she simply performed tests confirming the compound’s qualities and its utility, which the software had already determined. The attorney taking the deposition moves to invalidate the patent on the basis that the patent does not identify the true inventor. The true inventor, the attorney argues, was the company’s AI software.

Seem farfetched? Maybe not in today’s AI world. AI tools can spot cancer and other problems in diagnostic images, as well as identify patient-specific treatments. AI software can identify workable drug combinations for effectively combating pests. AI can predict biological events emerging in hotspots on the other side of the world, even before they’re reported by local media and officials. And lawyers are becoming more aware of AI through use of machine learning tools to predict the relevance of case law, answer queries about how a judge might respond to a particular set of facts, and assess the strength of contracts, among other tools. So while the above deposition scenario is hypothetical, it seems far from unrealistic.

One thing is for sure, however; an AI program will not be named as an inventor or joint inventor on a patent any time soon. At least not until Congress amends US patent laws to broaden the definition of “inventor” and the Supreme Court clarifies what “conception” of an invention means in a world filled with artificially-intelligent technologies.

That’s because US patent laws are intended to protect the natural intellectual output of humans, not the artificial intelligence of algorithms. Indeed, Congress left little wiggle room when it defined “inventor” to mean an “individual,” or in the case of a joint invention, the “individuals” collectively who invent or discover the subject matter of an invention. And the Supreme Court has endorsed a human-centric notion of inventorship. This has led courts overseeing patent disputes to repeatedly remind us that “conception” is the touchstone of inventorship, where conception is defined as the “formation in the mind of the inventor, of a definite and permanent idea of the complete and operative invention, as it is hereafter to be applied in practice.”

But consider this. What if “in the mind of” were struck from the definition of “conception” and inventorship? Under that revised definition, an AI system might indeed be viewed as conceiving an invention.

By way of example, let’s say the same AI software and the researcher from the above deposition scenario were participants behind the partition in a classic Turing Test. Would an interrogator be able to distinguish the AI inventor from the natural intelligence inventor if the test for conception of the chemical compound invention is reduced to examining whether the chemical compound idea was “definite” (not vague), “permanent” (fixed), “complete,” “operative” (it works as conceived), and has a practical application (real world utility)? If you were the interrogator in this Turing Test, would you choose the AI software or the researcher who did the follow-up confirmatory testing?

Those who follow patent law may see the irony of legally recognizing AI software as an “inventor” if it “conceives” an invention, when the very same software would likely face an uphill battle being patented by its developers because of the apparent “abstract” nature of many software algorithms.

In any case, for now the question of whether inventorship and inventions should be assessed based on their natural or artificial origin may merely be an academic one. But that may need to change when artificial intelligence development produces artificial general intelligence (AGI) that is capable of performing the same intellectual tasks that a human can.

Inaugural Post – AI Tech and the Law

Welcome. I am excited to present the first of what I hope will be many useful and timely posts covering issues arising at the crossroads of artificial intelligence technology and the law. My goal with this blog is to provide insightful discussion concerning the legal issues expected to affect individuals and businesses as they develop and interact with AI products and services. I also hope to engage with AI thought leaders in the legal industry as new AI technology-specific issues emerge. Join me by sharing your thoughts about AI and the law. If you’d like to see a particular issue discussed on these pages, I invite you to send me an email.

Much has already been written about the promises of AI and its ever-increasing role in daily life. AI technologies are unquestionably making their presence known in many impactful ways. Three billion smartphones in use worldwide, and many of them use one form of AI or another. Voice assistants driven by AI are appearing on kitchen countertops everywhere. Online search engines, powered by AI, deliver your search results. Select like/love/dislike/thumbs-down on your music streaming or news aggregating apps empowers AI algorithms to make recommendations for you.

Today’s tremendous AI industry expansion, driven by big data and enhanced computational power, will continue at an unprecedented rate in the future. We are seeing investors fund AI-focused startups across the globe. As Marc Cuban predicted earlier this year, the world’s first trillionaire will be an AI entrepreneur.

Not everyone, however, shares the same positive outlook concerning AI. Elon Musk, Bill Gates, Stephen Hawking and others have raised concerns. Many foresee problems arising as AI becomes ubiquitous, especially if businesses are left to develop AI systems without guidance. The media have written about displaced employees due to autonomous systems; bias, social justice, and civil rights concerns in big data; AI consumer product liability; privacy and data security; superintelligent systems, and other issues. Some have even predicted dire consequences from unchecked AI.

But with all the talk about AI–both positive and negative–businesses are operating in a vacuum of laws, regulations, and court opinions dealing directly with AI. Indeed, with only a few exceptions, most businesses today have little in the way of legal guidance about acceptable practices when it comes to developing and deploying their AI systems. While some advocate for a common law approach to dealing with AI problems on a case-by-case basis, others would like to see a more structured regulatory framework.

I look forward to considering these and others issues in the months to come.

Brian Higgins