Epistemic Closure and the Irreversibility of Misalignment: Modeling Systemic Barriers to Alignment Innovation

• URL: http://arxiv.org/abs/2504.02058v1
• Date: Wed, 02 Apr 2025 18:35:15 GMT
• Title: Epistemic Closure and the Irreversibility of Misalignment: Modeling Systemic Barriers to Alignment Innovation
• Authors: Andy Williams,
• Abstract summary: Efforts to ensure the safe development of artificial general intelligence often rely on consensus-based alignment approaches.<n>This paper introduces a functional model of epistemic closure, in which cognitive, institutional, social, and infrastructural filters combine to make alignment proposals illegible.<n>We present a weighted closure model supported by both theoretical and empirical sources, including a meta-analysis performed by an AI system on patterns of rejection and non-engagement.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Efforts to ensure the safe development of artificial general intelligence (AGI) often rely on consensus-based alignment approaches grounded in axiomatic formalism, interpretability, and empirical validation. However, these methods may be structurally unable to recognize or incorporate novel solutions that fall outside their accepted epistemic frameworks. This paper introduces a functional model of epistemic closure, in which cognitive, institutional, social, and infrastructural filters combine to make many alignment proposals illegible to existing evaluation systems. We present a weighted closure model supported by both theoretical and empirical sources, including a meta-analysis performed by an AI system on patterns of rejection and non-engagement with a framework for decentralized collective intelligence (DCI). We argue that the recursive failure to assess models like DCI is not just a sociological oversight but a structural attractor, mirroring the very risks of misalignment we aim to avoid in AGI. Without the adoption of DCI or a similarly recursive model of epistemic correction, we may be on a predictable path toward irreversible misalignment. The development and acceptance of this paper, first through simulated review and then through formal channels, provide a case study supporting its central claim: that epistemic closure can only be overcome by recursive modeling of the constraints that sustain it.

Related papers

• Identifiable Multi-View Causal Discovery Without Non-Gaussianity [63.217175519436125]
  We propose a novel approach to linear causal discovery in the framework of multi-view Structural Equation Models (SEM)<n>We prove the identifiability of all the parameters of the model without any further assumptions on the structure of the SEM other than it being acyclic.<n>The proposed methodology is validated through simulations and application on real data, where it enables the estimation of causal graphs between brain regions.
  arXiv  Detail & Related papers  (2025-02-27T14:06:14Z)
• Causality can systematically address the monsters under the bench(marks) [64.36592889550431]
  Benchmarks are plagued by various biases, artifacts, or leakage.<n>Models may behave unreliably due to poorly explored failure modes.<n> causality offers an ideal framework to systematically address these challenges.
  arXiv  Detail & Related papers  (2025-02-07T17:01:37Z)
• On the Reasoning Capacity of AI Models and How to Quantify It [0.0]
  Large Language Models (LLMs) have intensified the debate surrounding the fundamental nature of their reasoning capabilities. While achieving high performance on benchmarks such as GPQA and MMLU, these models exhibit limitations in more complex reasoning tasks. We propose a novel phenomenological approach that goes beyond traditional accuracy metrics to probe the underlying mechanisms of model behavior.
  arXiv  Detail & Related papers  (2025-01-23T16:58:18Z)
• Self-Healing Machine Learning: A Framework for Autonomous Adaptation in Real-World Environments [50.310636905746975]
  Real-world machine learning systems often encounter model performance degradation due to distributional shifts in the underlying data generating process. Existing approaches to addressing shifts, such as concept drift adaptation, are limited by their reason-agnostic nature. We propose self-healing machine learning (SHML) to overcome these limitations.
  arXiv  Detail & Related papers  (2024-10-31T20:05:51Z)
• Distribution-consistency Structural Causal Models [6.276417011421679]
  We introduce a novel textitdistribution-consistency assumption, and in alignment with it, we propose the Distribution-consistency Structural Causal Models (DiscoSCMs) To concretely reveal the enhanced model capacity, we introduce a new identifiable causal parameter, textitthe probability of consistency, which holds practical significance within DiscoSCM alone.
  arXiv  Detail & Related papers  (2024-01-29T06:46:15Z)
• Answering Causal Queries at Layer 3 with DiscoSCMs-Embracing Heterogeneity [0.0]
  This paper advocates for the Distribution-consistency Structural Causal Models (DiscoSCM) framework as a pioneering approach to counterfactual inference.
  arXiv  Detail & Related papers  (2023-09-17T17:01:05Z)
• Advancing Counterfactual Inference through Nonlinear Quantile Regression [77.28323341329461]
  We propose a framework for efficient and effective counterfactual inference implemented with neural networks. The proposed approach enhances the capacity to generalize estimated counterfactual outcomes to unseen data. Empirical results conducted on multiple datasets offer compelling support for our theoretical assertions.
  arXiv  Detail & Related papers  (2023-06-09T08:30:51Z)
• Learning a Structural Causal Model for Intuition Reasoning in Conversation [20.243323155177766]
  Reasoning, a crucial aspect of NLP research, has not been adequately addressed by prevailing models. We develop a conversation cognitive model ( CCM) that explains how each utterance receives and activates channels of information. By leveraging variational inference, it explores substitutes for implicit causes, addresses the issue of their unobservability, and reconstructs the causal representations of utterances through the evidence lower bounds.
  arXiv  Detail & Related papers  (2023-05-28T13:54:09Z)
• A Meta-heuristic Approach to Estimate and Explain Classifier Uncertainty [0.4264192013842096]
  This work proposes a set of class-independent meta-heuristics that can characterize the complexity of an instance in terms of factors are mutually relevant to both human and machine learning decision-making. The proposed measures and framework hold promise for improving model development for more complex instances, as well as providing a new means of model abstention and explanation.
  arXiv  Detail & Related papers  (2023-04-20T13:09:28Z)
• Non-Linear Spectral Dimensionality Reduction Under Uncertainty [107.01839211235583]
  We propose a new dimensionality reduction framework, called NGEU, which leverages uncertainty information and directly extends several traditional approaches. We show that the proposed NGEU formulation exhibits a global closed-form solution, and we analyze, based on the Rademacher complexity, how the underlying uncertainties theoretically affect the generalization ability of the framework.
  arXiv  Detail & Related papers  (2022-02-09T19:01:33Z)
• Discovering Latent Causal Variables via Mechanism Sparsity: A New Principle for Nonlinear ICA [81.4991350761909]
  Independent component analysis (ICA) refers to an ensemble of methods which formalize this goal and provide estimation procedure for practical application. We show that the latent variables can be recovered up to a permutation if one regularizes the latent mechanisms to be sparse.
  arXiv  Detail & Related papers  (2021-07-21T14:22:14Z)

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.