AI Lacks Emotions and Imagination (As of Now)

The evolution of artificial intelligence (AI) has been remarkable. As of 2025, AI can write text, generate images, and perform complex tasks. However, no matter how advanced its processing capabilities become, AI fundamentally lacks two critical elements: “emotions” and “imagination.” Properly understanding this reality is extremely important for appropriately utilizing AI and redefining the role of humans.

The Essential Reasons Why AI Does Not Have “Emotions”

The Difference Between Pattern Recognition and Emotions

AI learns from vast amounts of data and can mimic human emotional expressions. It can generate encouraging words and return empathetic responses. However, this does not mean AI “has” emotions—it merely “reproduces” patterns of emotional language.

For example, when AI responds with “That must have been difficult for you,” it is not actually feeling sympathy. Rather, it has learned from massive conversational datasets that empathetic words are appropriate in such situations and outputs that pattern.

The Absence of Subjective Experience

Human emotions involve subjective experiences such as joy, sadness, anger, and fear. We are moved by beautiful sunsets or shed tears at parting with loved ones. This is not mere information processing but an internal experience as conscious beings.

Current AI lacks such subjective experience. AI can analyze an image of a sunset and label it as “beautiful,” but it cannot “feel” that beauty. This difference represents a fundamental gap that cannot be bridged by technological advancement alone.

This distinction aligns with current philosophical debates in consciousness studies, where the “hard problem of consciousness”—explaining why subjective experience exists at all—remains unresolved. Leading researchers in neuroscience and AI ethics emphasize that without phenomenological experience, AI responses are fundamentally different from human emotional states.

Why AI Lacks “Imagination”

Creation Within the Boundaries of Training Data

AI’s creative outputs often surprise humans. It can propose new designs and generate original text. However, AI’s creativity is essentially a product of “combination.”

AI can combine elements from learned data in new ways, but it cannot create concepts that are completely absent from its training data. For instance, AI cannot perform the kind of creation a blind painter who has never seen a photograph might achieve when painting a landscape purely from imagination.

The Absence of Leaps into the “Unknown”

The essence of human imagination lies in the ability to envision “things that do not yet exist” beyond the known world. Flight was once considered impossible, but humans observed birds and imagined “what if humans could fly too,” eventually inventing the airplane.

This type of imagination requires emotional drivers such as dissatisfaction with the current state, hope for a better future, and the will to realize it. While AI can derive optimal solutions from data, it cannot make value judgments about “how things should be” or hold desires for “how wonderful it would be if this happened.”

Practical Implications for AI-Human Collaboration

Areas Where AI Excels

Precisely because AI lacks emotions and imagination, there are domains where it demonstrates superior capabilities.

Consistent Processing Capacity

AI never tires and can work continuously 24 hours a day. Unaffected by emotional fluctuations, it can process tasks according to consistent standards. In monotonous repetitive work and tasks requiring the processing of large amounts of information in a short time, AI far surpasses humans.

Important Caveats About Objectivity

However, the perception that “AI is objective and free from bias” requires careful attention. Research through 2025 has revealed that AI reflects and sometimes amplifies biases present in its training data. For example, cases have been reported where medical diagnostic AI functions unevenly across certain demographic groups, or financial risk assessment AI inherits existing social biases.

From a regulatory perspective, the EU AI Act (which began phased implementation in 2024) classifies AI systems based on risk levels and mandates transparency and bias monitoring for high-risk applications. ISO/IEC 42001:2023, the international standard for AI management systems, requires organizations to establish processes for identifying and mitigating bias throughout the AI lifecycle.

When utilizing AI, ensuring transparency in its decisions and conducting continuous monitoring and correction of biases is essential. It is important to recognize that while AI is a “tireless processor,” it is not a “completely impartial judge.”

What Only Humans Can Do

Conversely, roles that only humans with emotions and imagination can fulfill are also clear.

Creating Genuinely New Value

When facing unprecedented challenges, ideas beyond existing data and patterns become necessary. This is the domain of humans who have emotions and can make subjective value judgments. However, research from 2024 to 2025 has revealed that AI collaboration’s impact on creativity is complex. While creativity improves at the individual level, there is an observed tendency for society as a whole to produce similar works, with decreasing diversity.

The true value of human creativity lies in the value judgment of “what should be created” and the attribution of meaning to “why it matters.” This aspect is a role that only humans with emotions and imagination can fulfill.

Under emerging regulatory frameworks, this human role is increasingly codified. The EU AI Act requires human oversight for high-risk AI systems, explicitly recognizing that final decisions—especially those with significant impact on individuals—must involve meaningful human judgment. Similarly, the OECD AI Principles emphasize human-centered values and the need for AI systems that respect human autonomy.

Ethical Judgment and Empathy

Even in an era where we “entrust” tasks to AI, humans must take responsibility for final decisions, especially on ethical matters. Ethical reasoning requires value judgments and understanding of emotional context—capabilities that AI lacks. Additionally, genuine empathy is essential for building deep trust relationships with customers and colleagues.

What is crucial is that AI systems disclose their artificial nature and maintain transparency. In emotionally charged contexts especially, AI should be positioned as a supplement, not a replacement, for human support.

International standards support this approach. ISO/IEC 42001 mandates that organizations deploying AI systems establish clear accountability structures and ensure appropriate human involvement in decision-making processes. The IEEE’s Ethically Aligned Design framework similarly emphasizes transparency and the need for AI systems to explicitly acknowledge their limitations.

Future Outlook

Research Trends in Affective AI

Researchers are working on developing “affective AI”—systems that recognize human emotions from facial expressions and voice tones and respond appropriately. However, this is technology that “detects” emotional expression patterns rather than “understands” emotions.

Whether AI will truly possess emotions and imagination in the future is related to the philosophical question of the nature of consciousness and remains an unresolved challenge. Current approaches to artificial general intelligence (AGI) remain far from replicating human consciousness, and leading AI researchers maintain that the emergence of subjective experience in AI systems is not imminent.

The field of consciousness studies suggests that even if AI could perfectly simulate emotional responses, the presence or absence of genuine subjective experience (qualia) would remain fundamentally unknowable from an external perspective—a problem known as “the other minds problem.”

The Path of Co-evolution

What is important is not to view the absence of emotions and imagination in AI as a deficiency, but to recognize it as a difference in characteristics between humans and AI. AI contributes through consistent processing capabilities and high-speed information processing, while humans leverage emotions and imagination to create new value.

However, we must not overestimate AI’s “objectivity.” Because AI reflects the biases in its training data, continuous monitoring and final human confirmation of its judgments are necessary. This complementary relationship and collaboration based on understanding each other’s limitations represent the ideal form for the coming era.

Regulatory frameworks increasingly reflect this balanced perspective. The EU AI Act’s risk-based approach and requirements for human oversight in high-risk applications acknowledge both AI’s capabilities and its limitations. As we move forward, compliance with these standards will not only be legally necessary but will also represent best practices for responsible AI deployment.

Comparison of AI and Human Capabilities

To better understand the complementary nature of AI and human capabilities, consider the following framework:

Capability Dimension	AI Strengths	AI Limitations	Human Strengths	Regulatory Considerations (2025)
Processing Speed	Processes vast data sets in seconds; 24/7 operation without fatigue	Cannot prioritize based on intuition or contextual nuance	Can quickly identify what matters most in complex situations	EU AI Act requires performance monitoring; ISO/IEC 42001 mandates quality management
Consistency	Applies rules uniformly; maintains standards across millions of decisions	Reproduces training data biases systematically	Can adapt judgments based on exceptional circumstances	High-risk AI systems must document decision-making criteria and undergo regular bias audits
Pattern Recognition	Identifies correlations in multidimensional data invisible to humans	Limited to patterns present in training data	Can recognize novel patterns and paradigm shifts	Transparency requirements mandate explainable AI for critical applications
Emotional Intelligence	Can detect emotional cues (facial expressions, tone)	Cannot genuinely feel or be motivated by emotions	Provides authentic empathy; builds trust through shared experience	Emotional AI applications must disclose artificial nature per OECD guidelines
Creativity	Combines existing elements in novel ways; generates multiple variations rapidly	Cannot imagine beyond training data; no intrinsic motivation	Creates entirely new concepts; driven by values and vision	Copyright and intellectual property frameworks still evolving for AI-generated content
Ethical Reasoning	Can apply encoded ethical rules consistently	Lacks moral agency; cannot weigh competing values contextually	Makes nuanced ethical judgments; takes moral responsibility	EU AI Act mandates human oversight for decisions affecting fundamental rights
Learning	Improves through exposure to more data and fine-tuning	Requires extensive data; prone to catastrophic forgetting	Learns from single examples; integrates diverse experiences	Data governance standards (ISO/IEC 38505) require transparency in training data sourcing

This framework illustrates that optimal outcomes emerge not from replacing humans with AI, but from strategic collaboration that leverages each party’s distinctive strengths while mitigating their respective limitations.

Conclusion

As of now, AI lacks emotions and imagination. This represents not so much a technological limitation as a fundamental difference between AI and humans. By properly understanding this reality, it becomes clear what we should entrust to AI and what humans should focus on.

As AI evolves from something we “use” to something we “entrust,” what is required of humans is creative activity utilizing emotions and imagination. Demonstrating uniquely human qualities that AI cannot replicate will be the key to thriving in the coming era. Rather than fearing technological evolution, we should build a future where humans and AI grow together, each leveraging their respective strengths.

In this collaborative future, regulatory compliance will not be a burden but a framework ensuring that AI deployment enhances rather than diminishes human flourishing. As international standards continue to evolve, organizations and individuals who embrace transparency, accountability, and human-centered design will be best positioned to realize the benefits of AI while safeguarding essential human values.

Note on Current Regulatory Landscape (2025):

The global AI regulatory environment continues to mature, with several key frameworks now in effect:

• EU AI Act: Phased implementation began in 2024, with full enforcement of high-risk system requirements by 2026. Mandates transparency, human oversight, and bias monitoring.
• ISO/IEC 42001:2023: International standard for AI management systems, providing a framework for responsible AI development and deployment.
• OECD AI Principles: Widely adopted guidelines emphasizing human-centered values, transparency, robustness, and accountability.
• IEEE Ethically Aligned Design: Technical standards for embedding ethical considerations in AI system design.

Organizations deploying AI systems should ensure compliance with applicable regulations and standards, recognizing that responsible AI practices are both an ethical imperative and increasingly a legal requirement.