In the race to develop the most sophisticated artificial intelligence (AI) tools, major technology companies have been deploying increasingly aggressive tactics to secure the resources they need: data, data and more data. From sourcing vast troves of user-generated content on popular sites to even allegedly torrenting copyrighted material, these organisations appear willing to push ethical and legal boundaries in pursuit of a competitive edge. Some have even straight-up deleted pledges to not use AI for harm. This raises difficult questions about privacy, consent, and respect for intellectual property. Who truly benefits from these data-driven pursuits? And what does this fervent dash for data mean for the rest of us, especially when the AI era is still in its early stages?
The driving force behind the most advanced AI models is an extraordinary volume of data, which is processed to identify patterns, train machine learning algorithms, and refine predictive capabilities. Let’s not forget about the GPUs, but then again, DeepSeek sure turned things around on that front.
In such a landscape, a large quantity of quality data is more than just an advantage—it can make or break a company’s prospects in AI research. For tech businesses seeking to remain relevant, or for start-ups hungry to make their mark, the race to amass quality datasets is relentless. The problem arises when this ambition leads companies to adopt strategies that run counter to established ethical norms or even legal statutes.
One method that has received growing attention is the partnership model. Websites such as Quora, Reddit, and other popular forums offer treasure troves of user-generated content—opinions, personal anecdotes, and detailed knowledge on myriad topics. By forming partnerships or purchasing access to these platforms’ data, companies can funnel enormous amounts of textual content into their training pipelines. While many of these agreements might be covered by terms and conditions that users nominally accept, it remains questionable whether users are fully aware that their contributions could ultimately be used to power a profit-driven AI system.
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The repercussions of such partnerships are multifaceted. On one hand, there may be legitimate grounds for collaboration if users have consented to their data being processed and the platforms enforce proper regulations. On the other hand, the sheer scope of data collection on these forums raises concerns about whether the granularity of user consent truly accounts for AI’s sophisticated data-processing methods.
More troubling still are allegations that some companies or research groups resort to outright illegal activities, such as torrenting copyrighted books and articles to expand their datasets. In some cases, entire libraries of copyrighted works—fiction, non-fiction, academic studies—are believed to be informally scraped or downloaded to serve as training material. Because AI models benefit from diverse textual inputs, these repositories are especially valuable, allowing the technology to “learn” from a broad variety of writing styles and content.
However, the line is sharply drawn by law. Copyright exists to protect creators and publishers, and there are stringent rules about reproducing or redistributing their work without permission. The question is whether existing regulations are robust enough to cover the complexities of machine learning, where data is not merely copied but used to train algorithms that then generate new outputs.
Legislation often lags behind technological breakthroughs, leading to grey areas where companies exploit loopholes to push ethical boundaries.
These tactics lay bare a host of ethical and legal issues. First, there is the fundamental matter of privacy. Even if some personal data is nominally anonymised or aggregated, it is not always straightforward to remove identifying details from large datasets. People who post on forums often share personal stories or sensitive information, potentially leaving them vulnerable to unwelcome scrutiny if their words are harvested and used without transparent permission.
Then there is the copyright question. Authors, journalists, and other creators invest considerable effort and resources in their work. For them to see their content appropriated, sometimes illegally, infringes upon their rights and diminishes the value of their efforts. This scenario also generates doubts about accountability, particularly when AI models inadvertently replicate or transform copyrighted passages into their outputs.
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Finally, current regulations rarely account for the sophisticated ways in which data can be collected and processed for AI. Legislation often lags behind technological breakthroughs, leading to grey areas where companies exploit loopholes to push ethical boundaries.
One immediate consequence of these questionable data practices is a growing sense of distrust in the tech sector. Users who become aware that their personal data or creative works are being scooped up without explicit, informed consent may rethink their engagement with online platforms. This creates a chilling effect, discouraging people from posting freely or participating in discussions they once found enriching.
Beyond the erosion of trust, the long-term implications for society could be profound. If AI tools are shaped by ethically or legally dubious data acquisition, they might inherit biases or blind spots. Such models risk perpetuating skewed perspectives, especially if they train on sources that fail to reflect the true diversity of opinions or experiences.
The frenzy to secure the best data for AI has propelled some tech companies to stretch and sometimes breach the limits of ethical and legal behaviour. Although innovation often involves risk, the potential fallout for individual users and society at large cannot be taken lightly. Losing sight of privacy, consent, and intellectual property rights jeopardises not only the trust of consumers but also the credibility and sustainability of AI as a transformative force.
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Mithun Mohandas
Mithun Mohandas is an Indian technology journalist with 10 years of experience covering consumer technology. He is currently employed at Digit in the capacity of a Managing Editor. Mithun has a background in Computer Engineering and was an active member of the IEEE during his college days. He has a penchant for digging deep into unravelling what makes a device tick. If there's a transistor in it, Mithun's probably going to rip it apart till he finds it. At Digit, he covers processors, graphics cards, storage media, displays and networking devices aside from anything developer related. As an avid PC gamer, he prefers RTS and FPS titles, and can be quite competitive in a race to the finish line. He only gets consoles for the exclusives. He can be seen playing Valorant, World of Tanks, HITMAN and the occasional Age of Empires or being the voice behind hundreds of Digit videos. View Full Profile
