Anthropomorphism of AI: The serious consequences of mixing humans with humans have already been demonstrated

In our rush to understand and relate to AI, we have fallen into a tempting trap: attributing human characteristics to these powerful but fundamentally non-human systems. This anthropomorphism of artificial intelligence is not just a harmless quirk of human nature, it is becoming an increasingly dangerous trend that may cloud our judgment in crucial ways. Business leaders compare AI learning to human learning to justify training practices to lawmakers who formulate policies based on flawed human-AI analogies. This tendency to humanize AI may unduly shape critical decisions across industries and regulatory frameworks.

Viewing AI through a human lens in business has led companies to overestimate the capabilities of AI or underestimate the need for human oversight, which has sometimes led to costly consequences. The stakes are particularly high in copyright law, where anthropomorphic thinking has led to problematic comparisons between human learning and AI training.

Language trap

Listen to the way we talk about artificial intelligence: we say that it “learns,” “thinks,” “understands,” and even “creates.” These human terms seem natural, but they are misleading. When we say that an AI model is “learning,” it is not gaining understanding like a human student. Instead, it performs complex statistical analyzes on huge amounts of data, adjusting the weights and parameters in its neural networks based on mathematical principles. There is no understanding, no eureka moment, no spark of creativity or actual understanding – just matching increasingly sophisticated patterns.

This linguistic prowess is more than just a semantic skill. As mentioned in the paper, The bogus generative AI argument for fair use: “Using anthropomorphic language to describe the development and functioning of AI models is distorting because it suggests that once a model is trained, it operates independently of the content of the actions on which it is trained.” This confusion has real consequences, especially when it affects legal and political decisions.

Cognitive detachment

Perhaps the most dangerous aspect of anthropomorphizing AI is how it obscures fundamental differences between human and machine intelligence. While some AI systems excel at specific types of logical and analytical tasks, the large language models (LLMs) that dominate AI discourse today—and the ones we focus on here—work through sophisticated pattern recognition.

These systems process vast amounts of data, identifying and learning statistical relationships between words, phrases, images and other inputs to predict what should come next in a sequence. When we say they “learn,” we are describing an athlete’s improvement process that helps them make increasingly accurate predictions based on their training data.

Consider this amazing example from his research Berglund and colleagues: A model trained on materials that say “A equals B” often cannot conclude, as a human would, that “B equals A.” If the AI ​​knew that Valentina Tereshkova was the first woman in space, it might correctly answer “Who is Valentina Tereshkova?” But the struggle with “Who was the first woman in space?” This constraint reveals the fundamental difference between pattern recognition and true reasoning, between predicting possible sequences of words and understanding their meaning.

The copyright dilemma

This anthropomorphic bias has particularly troubling implications for the current discussion about artificial intelligence and copyright. Microsoft CEO Satya Nadella AI training has recently been compared for human learning, suggesting that AI should be able to do the same if humans can learn from books without copyright implications. This comparison perfectly illustrates the danger of anthropomorphic thinking in discussions of ethical and responsible AI.

Some believe that this measurement needs to be revised to understand human learning and artificial intelligence training. When humans read books, we don’t make copies of them, we understand and internalize the concepts. AI systems, on the other hand, must make physical copies of works—often acquired without permission or payment—encrypt them into their architecture and maintain these encrypted versions to function. Business does not disappear after “learning,” as AI companies often claim; They remain embedded in System neural networks.

Business blind spot

The anthropomorphism of AI creates serious blind spots in business decision-making beyond simple operational inefficiencies. When executives and decision makers think of AI as “creative” or “intelligent” in human terms, it can lead to a series of risky assumptions and potential legal liabilities.

Overestimating the capabilities of artificial intelligence

One crucial area where anthropomorphism creates risk is in content creation and copyright compliance. When companies view AI as capable of “learning” like humans, they may incorrectly assume that AI-generated content is automatically free of copyright concerns. This misunderstanding can lead companies to:

• Deploy AI systems that unintentionally reproduce copyrighted material, exposing the company to infringement claims
• Failure to implement appropriate content filtering and moderation mechanisms
• We incorrectly assume that AI can reliably distinguish between public domain material and copyrighted material
• Underestimating the need for human review in content creation processes

The blind spot of cross-border compliance

Anthropomorphism bias in AI creates risks when we look at cross-border compliance. As explained by Daniel Jervis, Haralambous Marmanis, Noam Shemtov, and Catherine Zaller-Rowland in “The gist of the matter: Copyright, Artificial Intelligence Training, Masters in Law,“Copyright law operates according to strict territorial principles, with each jurisdiction maintaining its own rules on what constitutes infringement and which exceptions apply.

This territorial nature of copyright law creates a complex web of potential liability. Companies may wrongly assume that their AI systems can freely “learn” from copyrighted material across jurisdictions, and fail to realize that training activities that are legal in one country may constitute infringement in another. The EU has recognized this risk in its AI law, particularly through… Recital 106which requires that any general-purpose AI model introduced in the EU must be compliant with EU copyright law with respect to training data, regardless of where that training takes place.

This is important because the embodiment of AI capabilities can lead companies to downplay or misunderstand their cross-border legal obligations. The comforting fantasy about AI “learning” like humans obscures the reality that AI training involves complex copying and storage processes that give rise to different legal obligations in other jurisdictions. This fundamental misunderstanding of the actual performance of AI, combined with the regional nature of copyright law, creates significant risks for companies operating globally.

Human cost

One of the most troubling costs is the emotional toll caused by anthropomorphizing artificial intelligence. We are seeing increasing cases of people forming emotional attachments to AI chatbots, treating them as friends or confidants. This could be private Dangerous for vulnerable individuals Who may share personal information or rely on AI for emotional support it cannot provide. Although the AI’s responses appear empathic, they are sophisticated pattern matching based on training data – there is no real understanding or emotional connection.

This emotional vulnerability can also appear in professional settings. As AI tools become increasingly integrated into daily work, employees may develop inappropriate levels of trust in these systems, treating them as actual colleagues rather than tools. They may share confidential work information too freely or be reluctant to report errors because of a sense of misplaced loyalty. Although these scenarios remain isolated for now, they highlight how the embodiment of AI in the workplace can cloud governance and create unhealthy dependencies on systems that are unable, despite their sophisticated responses, to truly understand or care.

Freedom from the anthropomorphic trap

So how do we move forward? First, we need to be more precise in our language regarding artificial intelligence. Instead of saying that AI “learns” or “understands,” we can say that it “processes data” or “generates outputs based on patterns in its training data.” This is not just pedantry, it helps explain what these systems do.

Second, we should evaluate AI systems for what they are, not what we imagine them to be. This means acknowledging their remarkable abilities and their fundamental limitations. AI can process massive amounts of data and identify patterns that humans might miss, but it can’t understand, think, or create in the way humans do.

Finally, we must work to develop frameworks and policies that address the actual characteristics of AI rather than its imagined human-like qualities. This is particularly critical in copyright law, where anthropomorphic reasoning can lead to flawed analogies and inappropriate legal conclusions.

The way forward

As AI systems become more sophisticated at mimicking human output, the temptation to anthropomorphize them will grow stronger. This anthropomorphic bias affects everything from how we evaluate AI’s capabilities to how we evaluate its risks. As we have seen, this extends to significant practical challenges related to copyright law and trade compliance. When we attribute human learning capabilities to AI systems, we must understand their fundamental nature and the technical reality of how information is processed and stored.

Understanding AI for what it is – sophisticated information processing systems, not learners like humans – is critical to all aspects of AI governance and deployment. By going beyond anthropomorphic thinking, we can better address the challenges of AI systems, from ethical considerations and safety risks to cross-border copyright compliance and training data management. This more nuanced understanding will help companies make more informed decisions while supporting better policy development and public discourse around AI.

The sooner we embrace the true nature of AI, the better equipped we will be to deal with its profound societal impacts and practical challenges in our global economy.

Rawani Levy is a licensing and legal consultant at CCC.