Who Owns the Data Behind AI? A Legal Perspective
Artificial intelligence has become part of everyday conversations, whether it’s chatbots writing emails or image generators creating artwork that looks like it belongs in a gallery. Behind all this clever tech sits one big question that doesn’t get talked about enough: who actually owns the data that makes these systems tick?
AI doesn’t spring out of thin air. It learns from massive piles of data, everything from books and news articles to social media posts and code libraries. That raises plenty of legal headaches, because much of that information belongs to someone. Businesses, artists, programmers, even small creators who share their work online all have rights over what they produce.
When AI companies gather up these data sets, copyright law and ownership disputes start to loom in the background. For anyone working in tech or running a business that wants to use AI tools, it pays to understand the basics of how these legal questions play out.
How AI Uses Data (The Tech Side)
Think of AI as a student sitting in a library that never closes. Instead of reading one or two books, it races through millions, spotting patterns in language, images, or code. Those patterns get baked into its “brain”, which is the model itself. When you ask it a question or give it a prompt, it responds based on all those patterns it has absorbed.
Where does the data come from? That depends on the AI company. Some use open-source databases that are meant to be shared freely. Others collect information by scraping websites, scanning books, or pulling in material from social media and forums. It is an enormous and messy mixture, which is part of the problem.
A lot of the time, there is no clear record of whether the person who created the original material gave permission for it to be used in this way. That might not matter much when you are asking AI to summarise an article. But when you are using AI to generate marketing content, code for a product, or artwork for sale, the question of ownership starts to get pretty serious.
Who Owns the Data? (The Legal Side)
At the heart of it all sits copyright law. In most countries, the basic idea is simple: the person who creates a piece of work, whether it’s a photo, a novel, or a block of code, owns the rights to it. That ownership means they control how it can be copied, shared, or changed.
Now, if AI companies train their systems on huge collections of this material, they are copying it in some form. That’s where disputes start. Artists, writers, and coders argue that their work is being used without consent, while AI developers often reply that the material is transformed enough to fall under legal exceptions like “fair use” in the US or “fair dealing” in places like Australia and the UK.
The trouble is that these exceptions were written long before modern AI existed. They are meant for things like research, news reporting, or parody. Courts are only now beginning to wrestle with whether training a machine on millions of works counts as fair use or whether it is straight-up copyright infringement.
We’ve already seen lawsuits filed by big names in the publishing, music, and tech industries. Some of these cases could reshape how AI is allowed to use data in the years ahead. For businesses today, it creates a murky landscape where what’s legal isn’t always clear.
Can AI Outputs Infringe Copyright?
Even if the training data itself is a legal grey area, the next layer of complexity comes with the outputs. When an AI produces something that looks suspiciously like an existing work, it can cross the line into infringement.
Take an AI art generator, for example. If it produces an image in the distinct style of a living artist, that artist may argue their creative identity has been misused. The same goes for AI that generates code resembling a licensed software library or a piece of writing that echoes large chunks of a published book.
Not every output is a problem. In many cases the material is different enough that it would not count as a copy. But the boundaries are far from settled, which makes things risky if businesses start selling AI-generated work without paying attention to how close it gets to existing creations.
Bias and Responsibility
AI models are not neutral, no matter how polished their outputs look. They reflect the biases hidden in the data they are trained on. If a training set contains stereotypes about gender, race, or culture, those patterns show up in the results.
From a legal standpoint, this can open a whole new can of worms. If a company relies on biased AI outputs in hiring, advertising, or decision-making, they may face discrimination claims. If AI-generated content spreads false or defamatory information, the business using it could end up responsible.
Unlike a human employee, an AI system can’t be held accountable. Responsibility tends to fall back on the company deploying it, or in some cases, on the developers who built it. That makes it vital for organisations to understand the risks before leaning too heavily on automated outputs.
What Businesses Should Keep in Mind
For companies keen to use AI, the safest path is a cautious one. Treat AI as a powerful tool, but not one that wipes away the need for good governance.
It helps to know what kind of data sets your chosen AI system was trained on. Some vendors are clearer than others about whether their training data is licensed or scraped from the open web. Reading the fine print in contracts is key, especially clauses about liability if a copyright dispute arises.
Businesses should also think about internal policies. When is AI-generated content acceptable to use, and when should staff rely on original human work? How should outputs be checked for accuracy, bias, or potential infringement? These practical steps make a big difference in managing risk.
Governments are starting to step in with new laws and guidance on AI, but the rules are still patchy. Keeping an eye on regulatory updates is smart, especially in sectors like finance, healthcare, or education where data protection and discrimination laws already bite hard.
We Asked a Legal Expert
To help unpack some of the trickier parts, we spoke with the team at Podmore Legal https://podmorelegal.com/. Their view is that ownership questions in AI are far from settled. Courts are still figuring out whether existing copyright rules stretch neatly over modern AI systems, or whether new categories of law will need to be created.
They explained that for businesses, the real risk is not only whether training an AI model is lawful, but whether relying on its outputs could lead to disputes. If an AI produces material too close to an original work, the company using it could find itself caught in the middle.
Podmore Legal recommend businesses look closely at the terms of service for AI tools. Many contracts try to shift responsibility away from the provider, which can leave users exposed. Having proper legal guidance before relying heavily on AI-generated work is the best way to stay on safe ground.
Looking Ahead
AI is not going away, and neither are the questions around who owns the data that feeds it. Right now, the law lags behind the technology, leaving plenty of uncertainty for businesses, creators, and developers.
What is clear is that the tension between innovation and ownership will shape the next few years. As more lawsuits unfold and governments begin to draft clearer regulations, we will see sharper boundaries on what is allowed. Until then, the safest approach is to stay informed, treat AI outputs with caution, and get legal advice when moving into grey areas.
The promise of AI is huge, but so are the responsibilities that come with it. Owning the risks as well as the rewards is what will separate the businesses that thrive with AI from those that stumble into disputes.