Intellectual Property exists at every step of an AI project lifecycle. Find out why it must be protected....
Artificial intelligence and data have a symbiotic relationship. Both data and AI need each other to survive and thrive. AI leverages data to function, and data relies on AI for effective analyses.
While this relationship is defined by a simple transaction, there are complex implications to consider. In the era of data privacy, it is crucial to understand and adhere to the principles of intellectual property. At the beginning of every ML project, it’s necessary to identify what assets need to be safeguarded.
While some developers believe that copyright laws present barriers to the progression of AI, data protection and legal ownership are integral to the development of AI into the mainstream. As the metaverse becomes more and more data-centric, patented algorithms will be the way of the future. In this article, we will look at how to identify IP properties within any machine learning project.
Before undergoing the legal pursuit of protecting an algorithm, it’s worth knowing why we should patent AI projects. The answer is simple. Without intellectual property rights, developers would feel discouraged from developing new technologies.
Training datasets, machine learning algorithms, software, and output all require different degrees of protection to uphold industry integrity. Patented algorithms are also key to the monetisation of artificial intelligence projects. Without the ability to assign legal ownership of an ML algorithm, investors may feel less inclined to finance a project.
There is no industry-wide consensus about the role of IP in AI, however. Many argue that the prevalence of patent protection for AI projects could harm innovation and competition. Some argue that AI advances could lower the cost of innovation, resulting in a large number of patents. These patents may then be held only by a few industry heavyweights who have access to the best technology and data.
Whatever side of the fence you find yourself on, some level of protection is needed to comply with industry standards.
In Ireland, intellectual property law protects ownership of AI. However, The Department of Enterprise, Trade, and Employment noted the current regulatory gaps in the National AI strategy.
We expect a shift in the level of protection available to developers in coming years on a global scale as the World Intellectual Property Organisation continues to develop sanctions around AI development.
Artificial Intelligence can be protected by patents, copyrights, or as trade secrets:
It’s a common misconception that AI projects cannot be patented. In reality, IP exists at every step of an AI project lifecycle. The IP regulations that exist within any given ML project are innumerable. Since AI is notoriously indefinable and ‘unprotectable’ patent thicketing (the process by which a multi-layered patent system is introduced) is becoming increasingly common.
To meet IP compliance standards, developers need to know how to effectively classify each process within an ML project into a sect of IP. Generally speaking, the more significant and unique a process is, the more protection it will require. We’re going to take you through the four phases within every ML project that require IP identification:
The setup phase comprises collecting, classifying, and labelling data. It is made up four IP components; data, annotation protocol, labels, and label taxonomy.
During the models training phase, there are three main areas that necessitate IP protection:
The third phase within an ML project sees the models enter into a software pipeline. The pipeline is flooded with data to create the desired output. We have identified four elements within the models exploitation phase that require IP protection:
The deployment stage sees the integration of ML models into a production environment. There are five steps within the deployment stage before the ML life cycle can be fully realised.
Outside of these four phases, developers must consider the relevancy of background IP. Background IP encompassses all the work completed prior to or separately from the specific contract that may be used in the project.
Background IP is important for developers to consider when ascertaining ownership of a project. Essentially, background IP makes it easy to assign proprietorship within an ML project, recognising both past and present contributions.
The proliferation of artificial intelligence is undeniable. Soon the world as we know it will evolve into an AI-enabled ecosystem. As it evolves, so will the value of legal protections and intellectual property. Identifying key intellectual properties in an ML project is paramount in the upkeep of transparency and integrity in AI.
Author: Johanna Walsh