In our rush to grasp and relate to AI, now we have fallen right into a tempting trap: we attribute human characteristics to those robust but fundamentally non-human systems. This humanization of AI isn’t only a harmless quirk of human nature – it’s becoming an increasingly dangerous trend that might critically cloud our judgment. Business leaders are comparing AI learning to human education to justify training practices to lawmakers who craft policies based on flawed human-AI analogies. This trend toward humanizing AI could unduly influence critical decisions across industries and regulatory frameworks.
Viewing AI through a human lens in business has led firms to overestimate AI's capabilities or underestimate the necessity for human oversight, sometimes with costly consequences. The stakes are particularly high in copyright law, where anthropomorphic pondering has led to problematic comparisons between human learning and AI training.
The language trap
Listen to how we discuss AI: We say it “learns,” “thinks,” “understands,” and even “creates.” These human terms feel natural, but are misleading. When we are saying that an AI model “learns,” it doesn’t mean that it gains understanding like a human student. Instead, it performs complex statistical analyzes on massive amounts of knowledge, adjusting weights and parameters in its neural networks based on mathematical principles. There is not any understanding, no aha moment, no spark of creativity or actual understanding – just increasingly sophisticated pattern recognition.
This linguistic device is greater than just semantic. As mentioned within the paper, Generative AI’s illusory argument for fair use: “Using anthropomorphic language to explain the event and functioning of AI models is distorting since it suggests that the model, once trained, functions independently of the content of the works on which it was trained.” This confusion has real implications Consequences, especially in the event that they influence legal and political decisions.
The cognitive disconnect
Perhaps essentially the most dangerous aspect of the anthropomorphization of AI is that it obscures the elemental differences between human and machine intelligence. While some AI systems excel at certain kinds of reasoning and evaluation tasks, the big language models (LLMs) that dominate today's AI discourse – and which we deal with here – are based on sophisticated pattern recognition.
These systems process massive amounts of knowledge and discover and learn statistical relationships between words, phrases, images, and other inputs to predict what should come next in a sequence. When we are saying they “learn,” we’re describing a strategy of mathematical optimization that helps them make increasingly accurate predictions based on their training data.
Consider this powerful example from research by Berglund and his colleagues: A model trained on materials to say “A is the same as B” often cannot come to the conclusion that “B is the same as A” like a human. If an AI learns that Valentina Tereshkova was the primary woman in space, it would accurately answer “Who was Valentina Tereshkova?” but struggle with “Who was the primary woman in space?” This limitation highlights the elemental difference between pattern recognition and real one Thinking – between predicting likely sequences of words and understanding their meaning.
The copyright problem
This anthropomorphic tendency has particularly troubling implications for the continuing debate over AI and copyright. Microsoft CEO Satya Nadella recently compared AI training to human learning, suggesting that if humans can learn from books without copyright implications, AI should have the option to do the identical. This comparison perfectly illustrates the danger of anthropomorphic pondering in discussions about ethical and responsible AI.
Some argue that this analogy must be revised to grasp human learning and AI training. When people read books, we don't make copies of them – we understand and internalize concepts. AI systems, alternatively, must make actual copies of works – often obtained without permission or payment – ​​encode them into their architecture, and keep these encoded versions functional. The works don’t disappear after “learning,” as AI firms often claim; they continue to be embedded within the neural networks of the system.
The blind spot of business
Anthropomorphizing AI creates dangerous blind spots in business decision-making that transcend easy operational inefficiencies. When executives and decision-makers view AI as “creative” or “intelligent” within the human sense, it could possibly result in a cascade of dangerous assumptions and potential legal liability.
Overestimation of AI capabilities
A critical area where anthropomorphization poses risks is content creation and copyright compliance. When firms assume that AI can “learn” like humans, they might incorrectly assume that AI-generated content is robotically freed from copyright concerns. This misunderstanding could cause firms to:
- Deploy AI systems that inadvertently reproduce copyrighted material, exposing the corporate to claims for damages
- It isn’t possible to implement proper content filtering and monitoring mechanisms
- There is a false assumption that AI can reliably distinguish between public domain and copyrighted material
- Underestimate the necessity for human review when creating content
The blind spot in cross-border compliance
The anthropomorphic bias in AI poses dangers once we consider cross-border compliance. As explained by Daniel Gervais, Haralambos Marmanis, Noam Shemtov and Catherine Zaller Rowland in “The crux of the matter: copyright, AI training and LLMs,“Copyright relies on strict territorial principles, with each jurisdiction applying its own rules about what constitutes infringement and what exceptions apply.”
This territorial nature of copyright creates a posh web of potential liability. Companies may incorrectly assume that their AI systems can freely “learn” from copyrighted material in all jurisdictions, failing to understand that training activities which are legal in a single country could also be infringing in one other. The EU has recognized this risk particularly in its AI law Recital 106which requires any general-purpose AI model offered within the EU to comply with EU copyright law regarding training data, no matter where the training took place.
This is essential because humanizing AI's capabilities could cause firms to underestimate or misunderstand their legal obligations across borders. The convenient fiction that AI “learns” like humans obscures the truth that AI training involves complex copying and storage operations that carry different legal obligations in other jurisdictions. This fundamental misunderstanding of how AI actually works, combined with the territorial nature of copyright law, poses significant risks for global firms.
The human cost
One of essentially the most concerning costs is the emotional toll of humanizing AI. We are increasingly seeing people form an emotional bond with AI chatbots and treat them as friends or confidants. This might be special dangerous for vulnerable people who may share personal information or depend on the emotional support AI cannot provide. While the AI's responses look like empathetic, they’re based on sophisticated pattern matching based on training data – there is no such thing as a real understanding or emotional connection.
This emotional vulnerability could also present itself in an expert environment. As AI tools change into more integrated into day by day work, employees may develop an inappropriate level of trust in these systems and treat them as real colleagues somewhat than tools. They may share confidential work information too freely or be reluctant to report errors out of a misplaced sense of loyalty. While these scenarios currently remain isolated, they illustrate how the humanization of AI within the workplace could cloud judgment and create unhealthy dependencies on systems that, despite their sophisticated responses, are unable to offer true understanding or care.
Breaking out of the anthropomorphic trap
So how can we move forward? First, we must be more precise in our language about AI. Instead of claiming that an AI “learns” or “understands,” lets say that it “processes data” or “generates output based on patterns in its training data.” This isn't just pedantic – it also helps make clear what these systems do.
Second, we want to guage AI systems for what they’re, not what we imagine them to be. This means acknowledging each their impressive abilities and their fundamental limitations. AI can process massive amounts of knowledge and recognize patterns that humans might miss, but it surely cannot understand, reason, or create within the ways in which humans do.
Finally, we want to develop frameworks and guidelines that address the actual characteristics of AI somewhat than imagined human-like characteristics. This is especially crucial in copyright law, where anthropomorphic pondering can result in faulty analogies and inappropriate legal conclusions.
The way forward
As AI systems change into more sophisticated at mimicking human outcomes, the temptation to humanize them becomes stronger. This anthropomorphic bias affects every little thing from how we evaluate AI's capabilities to how we assess its risks. As now we have seen, it involves significant practical challenges around copyright and business compliance. If we attribute human learning capabilities to AI systems, we must understand their fundamental nature and the technical reality of how they process and store information.
For all points of AI governance and deployment, it’s critical to grasp AI for what it truly is – sophisticated information processing systems, not human-like learners. By moving beyond anthropomorphic pondering, we are able to higher address the challenges of AI systems, from ethical considerations and security risks to cross-border copyright compliance and data management training. This deeper understanding will help firms make more informed decisions while supporting higher policy development and public discourse on AI.
The sooner we recognize the true nature of AI, the higher equipped we shall be to deal with its profound societal impacts and practical challenges in our global economy.
