The human factor in the age of AI: digital humanities and computational linguists

AI is commonly associated with computer science and engineering. However, the humanities also play a crucial role in shaping its development and ethical application. This humanistic perspective is essential to ensure that AI integration into daily life is carried out responsibly and in a way that benefits individuals and society as a whole.

Disciplines such as philosophy, linguistics, sociology, and communication help us understand human behavior, ethical values, and social structures. They contextualize AI's impact and guide how it should be managed. The humanities offer tools for critical judgment and AI system enhancement. Their insights are therefore vital to fostering a reflective and holistic approach to AI—whether it's assessing algorithmic bias, understanding how machines interpret and generate human language, or bringing these technologies closer to society.

We’ve compiled a selection of humanities-related professions that have particularly strong connections to AI, and explored how their knowledge is helping to shape the convergence between technology and humanity.

Philosophy

Philosophy tackles fundamental questions about consciousness, ethics, morality, epistemology (how we know), ontology (what exists), and values through critical reasoning.

Relationship with AI

• AI Ethics: Philosophers can help define ethical boundaries in AI development and use, addressing dilemmas such as algorithmic bias, privacy, machine autonomy, and the social impact of labor displacement.
• Consciousness and Intelligence: They explore the nature of consciousness and intelligence—key to understanding whether AI might one day be truly sentient or whether it should always be regarded as a sophisticated tool.
• Epistemology and Machine Learning: Philosophy contributes to evaluating how machine learning models “know” the world and to what extent we can trust their outputs.

—Example: Explainable AI (XAI) reveals how AI models work and makes their decisions more transparent and accessible for users—fostering trust and supporting the development of ethical and governance frameworks for AI.

Linguistics

The scientific study of human language—its structure, use, acquisition, and evolution at all levels (phonetics, phonology, morphology, syntax, semantics, and pragmatics)—is key to advancing natural language models.

Relationship with AI

• Natural Language Processing (NLP): Linguistic expertise is essential for improving NLP algorithms' accuracy and capability, which enable AI to interpret, generate, and interact with human language across applications like machine translation, sentiment analysis, chatbots, and content generation.
• Contextual Understanding: Linguists help AI systems grasp how words and phrases change with context, a core requirement for natural human-machine interaction.
• User Interface Design: Their insights contribute to designing language-based user interfaces that feel intuitive and accessible.
  
  —Example: Enhancing NLP systems and developing algorithms capable of comprehending human language in all its complexity leads to more natural interactions between people and machines.

Sociology

The scientific study of society—its structures, institutions, relationships, and transformations—helps us understand social behavior, collective dynamics, and how technologies like AI shape human life.

Relationship with AI

• Social Impact of AI: Sociology examines how AI affects employment, inequality, privacy, democracy, and other societal factors.
• Algorithmic Bias: It uncovers how social biases—including gender bias—are embedded in training data, and offers solutions for mitigating such distortions.
• Public Understanding: Studying public attitudes and beliefs about AI is crucial for fostering social acceptance and responsible adoption.
  
  —Example: Investigating the social impact of AI and its effects across different sectors or demographics generates actionable insights to reduce inequality and ensure a fair distribution of AI’s benefits.

History

The study of the human past—including events, ideas, cultures, and social transformations—offers a temporal lens for examining the evolution of technologies, systems, and societies.

Relationship with AI

• Lessons from the Past: History reveals how previous technological shifts were handled and the societal consequences they brought, offering valuable guidance for today’s AI landscape.
• Cultural Context: Understanding the cultural context in which AI is developed helps avoid imposing dominant values on other communities and encourages adaptation to local needs.
  
  —Example: Studying historical technological transitions—such as electrification and the inequality it exposed—can yield analogies that help us anticipate AI’s broader effects in the present.

Psychology

The scientific study of the mind, behavior, and mental processes provides crucial insights into cognition, emotion, and human interaction. Psychology also explores how we perceive and adapt to disruptive technologies like AI.

Relationship with AI

• Human-AI Interaction: Psychology informs the design of AI systems that are accessible, intuitive, and responsive to individual needs and preferences.
• Cognitive and Emotional Impact: It investigates how AI influences users’ mental well-being, behavior, and cognitive abilities—and how negative effects can be mitigated.
• Trust and Adoption: Behavioral psychology examines how to build trust in AI systems, a cornerstone of widespread adoption.
  
  —Example: Responsible, user-centered design enables the creation of emotionally intelligent chatbots and virtual assistants that account for cultural diversity, foster empathy, reduce the Eliza effect, and lower user frustration.

Communication

In the AI context, communication studies how messages are created, transmitted, and interpreted—and how media shapes public understanding—making it vital to educate, inform, and promote awareness around emerging technologies.

Relationship with AI

• Communicating with AI: Understanding how to effectively communicate with AI—and how it communicates with us—is crucial for improving the user experience and breaking down technological barriers.
• Misinformation: Communication research examines AI's role in spreading false information and devises strategies to combat it.
• AI Narratives: Communication experts help craft narratives that support ethical and equitable AI uses. They also contribute to developing AI models capable of creating useful and creative content.
  
  —Example: Educating the public about how AI works—and what it means—requires science communicators and media professionals who can explain complex information clearly and accessibly, and lead initiatives that raise awareness of AI’s uses and challenges.

Bridging the humanities and technology gap

There are numerous resources and strategies to help humanities professionals transition into tech and AI.

One high-profile option is 42 Fundación Telefónica, a free programming school network recognized as one of the world’s ten most innovative universities by the WURI ranking. At campuses in Madrid, Urduliz Bizkaia, Barcelona, and Málaga, students gain highly sought-after digital and cross-disciplinary skills through a gamified, collaborative, 100% hands-on learning model—completely free of charge.

No prior technical knowledge is required, making it an ideal option for humanities graduates.

It’s also useful to develop foundational skills in technologies such as Python, R, SQL, or data analysis methodologies through online courses and tutorials. When paired with the critical and ethical mindset of the humanities, even basic technical literacy can make a significant difference.

Generative AI opens up new opportunities for continuous, self-directed learning by offering flexible environments, on-demand content, detailed explanations, and tailored feedback.

Joining interdisciplinary groups or communities focused on AI and its social implications is another effective way to learn, collaborate with researchers or companies, and contribute valuable perspectives.

■ Technical skills like Python and R programming and natural language processing algorithms design are increasingly valued. The report also emphasizes the importance of soft skills like teamwork and organizational ability, as well as the increasing collaboration between academia and industry to align training with labor market needs.

■ Complementing technical skills, the World Economic Forum’s Future of Jobs Report 2025 underscores the growing global relevance of creative thinking, resilience, flexibility, agility, curiosity, and continuous learning.

Conclusion

AI is not the sole domain of the science-based sciences. The humanities offer a vital lens for examining AI's ethical, social, cultural, and human dimensions. Their role is to ensure that AI is used in beneficial and responsible ways, and that the technological infrastructure we build reflects our shared values—promoting justice, inclusion, and societal well-being.

To make this possible, we need updated and forward-thinking education policies that encourage multidisciplinary training—integrating science, technology, and the humanities into academic programs. These efforts should include collaborative projects and problem-based learning to prepare future generations for AI's complex dilemmas.

Governments, companies, and academic institutions have a responsibility to drive this transformation by creating learning environments that connect humanistic critical thinking with technological solutions.

Multidisciplinary approaches are essential to building AI systems that address urgent societal challenges—from inequality to sustainable development.

The demand for humanities professionals in AI continues to rise as businesses, organizations, and policymakers increasingly recognize the need for ethical, social, cultural, and human-centered frameworks for technological progress.

These professionals bring the insight required to confront today's challenges—such as inclusion and bias mitigation—and play a key role in crafting the governance structures needed to guide AI toward an equitable and sustainable future grounded in democratic values and fundamental human rights.

Achieving this will require a collective effort to redefine how we conceive and develop technology—ensuring it answers global needs with responsibility and fairness.