Enterprise AI Analysis
A taxonomy of epistemic injustice in the context of AI and the case for generative hermeneutical erasure
Author: W. J. T. (Thomas) Mollema (wjt.mollema@gmail.com)
This paper presents a comprehensive taxonomy of epistemic injustice in the context of Artificial Intelligence (AI). It categorizes various forms, from epistemic opacity in machine learning and discriminatory automation to the distortion of human beliefs by generative AI's hallucinations. A novel concept, "generative hermeneutical erasure," is introduced, highlighting the automation of 'epistemicide' through the suppression of non-Western epistemologies by large language models (LLMs). The work aims to provide a unified theoretical framework for understanding and addressing AI-related epistemic injustices.
Key Insights at a Glance
Understand the core impact and strategic implications of epistemic injustice in AI development and deployment.
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General Injustice Types Identified
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Novel AI-Specific Injustices Taxonomized
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Novel Injustice Proposed
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Generative Erasure Risk
Deep Analysis & Enterprise Applications
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General Epistemic Injustice
AI-Related Manifestations
Generative Hermeneutical Erasure (Novel)
The foundational framework of epistemic injustice identifies wrongs against a person in their capacity as a knower. This section outlines the core types, including testimonial, hermeneutical, participatory, and contributory injustices, and how they manifest in social interactions.
Here we explore how traditional forms of epistemic injustice are amplified or transformed in the context of AI systems. This includes algorithmic biases causing testimonial prejudice, and AI's 'view from nowhere' contributing to hermeneutical marginalization.
This novel form of injustice describes the automation of 'epistemicide' by generative AI. It occurs when LLMs, trained on dominant datasets, suppress non-Western epistemologies, leading to a loss of diverse conceptual frameworks and collective sense-making.
Pathway to Generative Hermeneutical Erasure
Dominant (WEIRD) Training Data
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AI 'View from Nowhere' Epistemology
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Statistical Marginalization of Non-Western Concepts
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Suppression of Epistemic Difference
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Generative Hermeneutical Erasure
Epistemicide
The process of knowledge destruction, automated by generative AI, leading to the loss of diverse ways of knowing.
Real-World Impact: Akan Ontology Example
The paper highlights how AI systems, by default, fail to utilize or even acknowledge concepts from non-Western ontologies, such as the Akan people's unique terms for life ('Okra'), activating principles ('Sunsum'), and social nature ('Mogya'). This absence in AI-generated content contributes to the gradual suppression and potential erasure of these vital cultural concepts.
This demonstrates how AI reinforces epistemic colonization by prioritizing a singular, Western worldview.
| Injustice Type | AI Manifestation | Key Consequence |
|---|---|---|
| Testimonial Injustice | Algorithmic credibility surplus, fact hallucination |
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| Hermeneutical Injustice | AI opacity, algorithmic profiling, 'view from nowhere' |
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| Zetetic Injustice | AI-mediated knowledge dissemination, lack of sourcing |
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| Generative Hermeneutical Erasure | Statistical marginalization of non-Western epistemologies |
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Your AI Implementation Roadmap
A typical phased approach to integrating advanced AI, designed for minimal disruption and maximum impact.
Phase 1: Discovery & Strategy
Comprehensive audit of existing systems and workflows, identification of AI opportunities, risk assessment, and strategic alignment with business objectives. Focus on understanding current epistemic flows and potential points of injustice.
Phase 2: Pilot & Development
Development of custom AI models, data preparation, and iterative testing with a focus on bias detection and mitigation. Implementation of small-scale pilot projects to validate impact and refine models, particularly sensitive to diverse epistemologies.
Phase 3: Integration & Scaling
Seamless integration of validated AI solutions into enterprise infrastructure. Scaled deployment across relevant departments with continuous monitoring, performance optimization, and ongoing ethical review, including the impact on collective sense-making.
Phase 4: Training & Governance
Comprehensive training for your team, establishing robust AI governance frameworks, and fostering an AI-literate culture. Ensuring mechanisms are in place to counteract epistemic injustices and promote inclusive knowledge production.
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