The Epistemic and Ethical Limitations of AI: A Philosophical Critique

  The Epistemic and Ethical Limitations of AI: A Philosophical Critique

Rahul Ramya

09.02.2025

Patna, India

Artificial Intelligence, despite its remarkable capabilities, is fundamentally based on algorithms developed by a small and relatively homogeneous group of human developers. These developers, primarily concentrated in specific geographic regions and cultural environments like Silicon Valley, major tech hubs in the United States, Europe, and parts of Asia, represent only a fraction of global human diversity and experience.

This concentrated development creates an ontological limitation—AI, as a system of knowledge production, is bound by the epistemic horizons of its creators. Thinkers like Michel Foucault have argued that knowledge is inseparable from power structures, and AI development reflects this dynamic. The epistemic authority of AI does not arise organically but is shaped by corporate and cultural hegemony. The individuals designing these systems inevitably embed their own biases whether cultural, political, or economic—into the structure of AI decision-making. This results in an algorithmic monoculture incapable of capturing the full complexity of global human experience.

The diversity of human existence spans thousands of cultures, hundreds of languages, varying economic conditions, different political systems, and countless local traditions and practices that have evolved over centuries. AI, however, does not construct knowledge in the way humans do; it functions by aggregating and statistically modeling pre-existing data. Habermas theory of communicative action helps illustrate this gap: humans create meaning through discourse, debate, and shared understanding, while AI lacks this dialogical capacity. AI is not an autonomous agent capable of critical reflection but a technological instrument shaped by external forces.

AI’s Epistemic Deficiency and the Limits of Computational Knowledge

This disparity between the limited perspective of AI developers and the rich complexity of global human experience results in AI systems that, while sophisticated in certain aspects, remain deficient in epistemic adaptability. While AI excels in formal rule-based reasoning, it struggles with:

Contextual Fluidity: Human knowledge is situated knowledge (as theorized by Donna Haraway), meaning it is embedded in specific social, historical, and cultural contexts. AI lacks situated cognition and cannot adapt meaningfully to unfamiliar social realities.

Moral Ambiguity: AI operates on probabilistic determinism, whereas human morality is often shaped by conflicting ethical imperatives. Hannah Arendt’s discussions on moral judgment highlight why mechanical decision-making cannot replace human ethical reasoning.

Ontological Innovation: AI cannot generate new categories of knowledge but only rearrange pre-existing data. Unlike humans, who create entirely new paradigms of thought (as seen in scientific revolutions), AI is structurally incapable of paradigm shifts.

Furthermore, this limitation extends beyond mere cultural understanding to include the nuanced ways different societies process information, make decisions, and interpret reality. For example, AI trained primarily on Western legal frameworks struggles to navigate pluralistic legal traditions like those found in India, Africa, or the Middle East. The AI systems, therefore, might excel in specific tasks but fail to grasp or appropriately respond to the multifaceted nature of human cognition.

The Corporate Capture of AI and the Commodification of Knowledge

The corporate model of AI development represents a significant impediment, as it views knowledge creation primarily as a profit-driven venture rather than an intellectual pursuit or a public good. The Frankfurt School’s critique of the culture industry is particularly relevant here technologies that could have served emancipatory purposes are instead shaped by corporate interests.

The development of AI models requires massive financial investments, making corporations prioritize profit maximization over genuine knowledge creation. This capitalist mode of AI production has led to the development of algorithms that actively enable fake news, hate speech, trolling, deep fakes, and surveillance systems—symptoms of corporate greed and power consolidation. The spread of misinformation, for instance, is not an unintended consequence but a feature of engagement-driven AI models that optimize for profit, as seen in the amplification of divisive content on social media.

Furthermore, the commodification of AI knowledge has resulted in a technological aristocracy, where a small elite controls the means of algorithmic production. This reinforces existing inequalities, as AI is not democratized knowledge but a privatized epistemic system. The consequence is a recursive cycle of exclusion—those who are already marginalized by social, economic, and racial disparities are further excluded from shaping AI’s development.

The Human-AI Divide: Deterministic Machines vs. Unstructured Human Life

Human life is inherently unstructured shaped by the complexities of cognition, emotion, and socialization. Unlike AI, which functions within predefined parameters, human actions emerge from conflicting dualities:

Unpredictable responses to predictable situations

Deviations from and conformations to norms

Struggles between determinism and ambiguity

Conflicts between emotion and rationality

These paradoxes define human intelligence and differentiate it fundamentally from artificial intelligence. AI systems rely on deterministic statistical models that operate within structured frameworks. They function by processing vast amounts of data to recognize patterns, predict outcomes, and optimize efficiency. However, they lack the organic, experience-driven, and often contradictory nature of human decision-making.

Philosophically, this recalls Maurice Merleau-Ponty’s  theory of embodied cognition”human intelligence is not just about processing data but about being-in-the-world, shaped by sensory experiences, bodily engagement, and social interactions. AI, in contrast, has no lived experience, no phenomenological awareness, and no existential stake in the world. While AI can simulate human behavior based on probabilistic models, it does not possess the ability to question, doubt, or experience existential uncertainty.

The contrast between human cognition and AI highlights a fundamental epistemological gap:

AI operates within algorithmic reason, whereas human intelligence includes intuitive, moral, and existential reasoning.

AI relies on pattern recognition, whereas human intelligence includes imagination, abstract thought, and spontaneous creativity.

AI processes information passively, whereas humans construct meaning through interaction, socialization, and cultural experience.

The Digital Divide: Literacy, Numeracy, and Exclusion from AI Knowledge

The understanding and effective use of AI depend on two fundamental capabilities:

1. Literacy and Numeracy

2. Computer Literacy

However, a significant portion of the global population lacks proficiency in these areas, creating a major barrier to AI awareness, adoption, and participation in the digital economy. This issue is further compounded by the lack of access to digital devices, preventing millions from engaging with AI-driven tools.

There are stark regional disparities in these capabilities:

Sub-Saharan Africa & South Asia: Low literacy and numeracy rates (UNESCO: ~60%-70% literacy in some areas).

 Latin America & Southeast Asia: Moderate literacy but poor computer literacy, limiting AI interaction.

 North America & Western Europe: High literacy and widespread digital access, creating an AI knowledge advantage.

Beyond regional inequalities, caste-based, racial, gender-based, and class-based disparities further exacerbate the digital divide. In India, for example, Dalits and Adivasis have historically lower literacy rates, while in conservative societies, women and girls have significantly reduced access to digital education. This exclusion risks reinforcing AI as a tool of elite control, rather than a democratizing force.

Conclusion

AI remains epistemically constrained, structurally deterministic, and politically monopolized. While it excels in computational efficiency, it lacks the existential depth, ethical reasoning, and human unpredictability that define true intelligence. Moreover, its development within a corporate profit model risks deepening global inequalities, rather than solving them. Without a fundamental shift towards democratized AI development, ethical transparency, and inclusion, AI will continue to serve as a tool of power rather than progress.