A taxonomy of epistemic injustice in the context of AI and the case for generative hermeneutical erasure

• URL: http://arxiv.org/abs/2504.07531v2
• Date: Fri, 11 Jul 2025 13:32:41 GMT
• Title: A taxonomy of epistemic injustice in the context of AI and the case for generative hermeneutical erasure
• Authors: Warmhold Jan Thomas Mollema,
• Abstract summary: Epistemic injustice related to AI is a growing concern.<n>In relation to machine learning models, injustice can have a diverse range of sources.<n>I argue that this injustice the automation of 'epistemicide', the injustice done to agents in their capacity for collective sense-making.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Epistemic injustice related to AI is a growing concern. In relation to machine learning models, epistemic injustice can have a diverse range of sources, ranging from epistemic opacity, the discriminatory automation of testimonial prejudice, and the distortion of human beliefs via generative AI's hallucinations to the exclusion of the global South in global AI governance, the execution of bureaucratic violence via algorithmic systems, and interactions with conversational artificial agents. Based on a proposed general taxonomy of epistemic injustice, this paper first sketches a taxonomy of the types of epistemic injustice in the context of AI, relying on the work of scholars from the fields of philosophy of technology, political philosophy and social epistemology. Secondly, an additional conceptualization on epistemic injustice in the context of AI is provided: generative hermeneutical erasure. I argue that this injustice the automation of 'epistemicide', the injustice done to epistemic agents in their capacity for collective sense-making through the suppression of difference in epistemology and conceptualization by LLMs. AI systems' 'view from nowhere' epistemically inferiorizes non-Western epistemologies and thereby contributes to the erosion of their epistemic particulars, gradually contributing to hermeneutical erasure. This work's relevance lies in proposal of a taxonomy that allows epistemic injustices to be mapped in the AI domain and the proposal of a novel form of AI-related epistemic injustice.

Related papers

• Noosemia: toward a Cognitive and Phenomenological Account of Intentionality Attribution in Human-Generative AI Interaction [4.022364531869169]
  This paper introduces and formalizes Noosemia, a novel cognitive-phenomenological phenomenon emerging from human interaction with generative AI systems.<n>We propose a multidisciplinary framework to explain how, under certain conditions, users attribute intentionality, agency, and even interiority to these systems.
  arXiv  Detail & Related papers  (2025-08-04T17:10:08Z)
• The AI Ethical Resonance Hypothesis: The Possibility of Discovering Moral Meta-Patterns in AI Systems [0.0]
  The paper proposes that advanced AI systems may emerge with the ability to identify subtle moral patterns that are invisible to the human mind.<n>The paper explores the possibility that by processing and synthesizing large amounts of ethical contexts, AI systems may discover moral meta-patterns that transcend cultural, historical, and individual biases.
  arXiv  Detail & Related papers  (2025-07-13T08:28:06Z)
• AI Automatons: AI Systems Intended to Imitate Humans [54.19152688545896]
  There is a growing proliferation of AI systems designed to mimic people's behavior, work, abilities, likenesses, or humanness.<n>The research, design, deployment, and availability of such AI systems have prompted growing concerns about a wide range of possible legal, ethical, and other social impacts.
  arXiv  Detail & Related papers  (2025-03-04T03:55:38Z)
• Causal Responsibility Attribution for Human-AI Collaboration [62.474732677086855]
  This paper presents a causal framework using Structural Causal Models (SCMs) to systematically attribute responsibility in human-AI systems. Two case studies illustrate the framework's adaptability in diverse human-AI collaboration scenarios.
  arXiv  Detail & Related papers  (2024-11-05T17:17:45Z)
• Epistemic Injustice in Generative AI [6.966737616300788]
  generative AI can potentially undermine the integrity of collective knowledge and the processes we rely on to acquire, assess, and trust information. We identify four key dimensions of this phenomenon: amplified and manipulative testimonial injustice, along with hermeneutical ignorance and access injustice. We propose strategies for resistance, system design principles, and two approaches that leverage generative AI to foster a more equitable information ecosystem.
  arXiv  Detail & Related papers  (2024-08-21T08:51:05Z)
• Converging Paradigms: The Synergy of Symbolic and Connectionist AI in LLM-Empowered Autonomous Agents [55.63497537202751]
  Article explores the convergence of connectionist and symbolic artificial intelligence (AI) Traditionally, connectionist AI focuses on neural networks, while symbolic AI emphasizes symbolic representation and logic. Recent advancements in large language models (LLMs) highlight the potential of connectionist architectures in handling human language as a form of symbols.
  arXiv  Detail & Related papers  (2024-07-11T14:00:53Z)
• Exploiting the Margin: How Capitalism Fuels AI at the Expense of Minoritized Groups [0.0]
  This paper explores the relationship between capitalism, racial injustice, and artificial intelligence (AI) It argues that AI acts as a contemporary vehicle for age-old forms of exploitation. The paper promotes an approach that integrates social justice and equity into the core of technological design and policy.
  arXiv  Detail & Related papers  (2024-03-10T22:40:07Z)
• Emergent Explainability: Adding a causal chain to neural network inference [0.0]
  This position paper presents a theoretical framework for enhancing explainable artificial intelligence (xAI) through emergent communication (EmCom) We explore the novel integration of EmCom into AI systems, offering a paradigm shift from conventional associative relationships between inputs and outputs to a more nuanced, causal interpretation. The paper discusses the theoretical underpinnings of this approach, its potential broad applications, and its alignment with the growing need for responsible and transparent AI systems.
  arXiv  Detail & Related papers  (2024-01-29T02:28:39Z)
• Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond [0.0]
  This comprehensive research article rigorously investigates the ethical dimensions intricately linked to the rapid evolution of AI technologies. Central to this article is the proposition of a conscientious AI framework, meticulously crafted to accentuate values of transparency, equity, answerability, and a human-centric orientation. The article unequivocally accentuates the pressing need for globally standardized AI ethics principles and frameworks.
  arXiv  Detail & Related papers  (2023-08-31T18:12:12Z)
• Human-AI Coevolution [48.74579595505374]
  Coevolution AI is a process in which humans and AI algorithms continuously influence each other. This paper introduces Coevolution AI as the cornerstone for a new field of study at the intersection between AI and complexity science.
  arXiv  Detail & Related papers  (2023-06-23T18:10:54Z)
• Attacks in Adversarial Machine Learning: A Systematic Survey from the Life-cycle Perspective [69.25513235556635]
  Adversarial machine learning (AML) studies the adversarial phenomenon of machine learning, which may make inconsistent or unexpected predictions with humans. Some paradigms have been recently developed to explore this adversarial phenomenon occurring at different stages of a machine learning system. We propose a unified mathematical framework to covering existing attack paradigms.
  arXiv  Detail & Related papers  (2023-02-19T02:12:21Z)
• Fairness in Agreement With European Values: An Interdisciplinary Perspective on AI Regulation [61.77881142275982]
  This interdisciplinary position paper considers various concerns surrounding fairness and discrimination in AI, and discusses how AI regulations address them. We first look at AI and fairness through the lenses of law, (AI) industry, sociotechnology, and (moral) philosophy, and present various perspectives. We identify and propose the roles AI Regulation should take to make the endeavor of the AI Act a success in terms of AI fairness concerns.
  arXiv  Detail & Related papers  (2022-06-08T12:32:08Z)
• Normative Epistemology for Lethal Autonomous Weapons Systems [0.0]
  This chapter discusses higher-order design principles to guide the design, evaluation, deployment, and iteration of Lethal Autonomous Weapons Systems. Epistemic models consider the role of accuracy, likelihoods, beliefs, competencies, capabilities, context, and luck in the justification of actions and attribution of knowledge.
  arXiv  Detail & Related papers  (2021-10-25T13:13:42Z)
• An interdisciplinary conceptual study of Artificial Intelligence (AI) for helping benefit-risk assessment practices: Towards a comprehensive qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
  This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence. The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
  arXiv  Detail & Related papers  (2021-05-07T12:01:31Z)
• A general framework for scientifically inspired explanations in AI [76.48625630211943]
  We instantiate the concept of structure of scientific explanation as the theoretical underpinning for a general framework in which explanations for AI systems can be implemented. This framework aims to provide the tools to build a "mental-model" of any AI system so that the interaction with the user can provide information on demand and be closer to the nature of human-made explanations.
  arXiv  Detail & Related papers  (2020-03-02T10:32:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.