Toward A Causal Framework for Modeling Perception

• URL: http://arxiv.org/abs/2401.13408v4
• Date: Wed, 06 Aug 2025 21:33:43 GMT
• Title: Toward A Causal Framework for Modeling Perception
• Authors: Jose M. Alvarez, Salvatore Ruggieri,
• Abstract summary: Perception remains understudied in machine learning (ML)<n>We present a first approach to modeling perception causally.<n>We define two kinds of probabilistic causal perception: structural and parametrical.
• Score: 22.596961524387233
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Perception occurs when individuals interpret the same information differently. It is a known cognitive phenomenon with implications for bias in human decision-making. Perception, however, remains understudied in machine learning (ML). This is problematic as modern decision flows, whether partially or fully automated by ML applications, always involve human experts. For instance, how might we account for cases in which two experts interpret differently the same deferred instance or explanation from a ML model? Addressing this and similar questions requires first a formulation of perception, particularly, in a manner that integrates with ML-enabled decision flows. In this work, we present a first approach to modeling perception causally. We define perception under causal reasoning using structural causal models (SCMs). Our approach formalizes individual experience as additional causal knowledge that comes with and is used by the expert decision-maker in the form of a SCM. We define two kinds of probabilistic causal perception: structural and parametrical. We showcase our framework through a series of examples of modern decision flows. We also emphasize the importance of addressing perception in fair ML, discussing relevant fairness implications and possible applications.

Related papers

• Do Large Language Models Show Biases in Causal Learning? [3.0264418764647605]
  Causal learning is the cognitive process of developing the capability of making causal inferences based on available information.<n>This research investigates whether large language models (LLMs) develop causal illusions.
  arXiv  Detail & Related papers  (2024-12-13T19:03:48Z)
• Disentangling Memory and Reasoning Ability in Large Language Models [97.26827060106581]
  We propose a new inference paradigm that decomposes the complex inference process into two distinct and clear actions.<n>Our experiment results show that this decomposition improves model performance and enhances the interpretability of the inference process.
  arXiv  Detail & Related papers  (2024-11-20T17:55:38Z)
• Failure Modes of LLMs for Causal Reasoning on Narratives [51.19592551510628]
  We investigate the interaction between world knowledge and logical reasoning.<n>We find that state-of-the-art large language models (LLMs) often rely on superficial generalizations.<n>We show that simple reformulations of the task can elicit more robust reasoning behavior.
  arXiv  Detail & Related papers  (2024-10-31T12:48:58Z)
• Investigating Context Effects in Similarity Judgements in Large Language Models [6.421776078858197]
  Large Language Models (LLMs) have revolutionised the capability of AI models in comprehending and generating natural language text. We report an ongoing investigation on alignment of LLMs with human judgements affected by order bias.
  arXiv  Detail & Related papers  (2024-08-20T10:26:02Z)
• PHAnToM: Persona-based Prompting Has An Effect on Theory-of-Mind Reasoning in Large Language Models [25.657579792829743]
  We empirically evaluate how role-playing prompting influences Theory-of-Mind (ToM) reasoning capabilities. We propose the mechanism that, beyond the inherent variance in the complexity of reasoning tasks, performance differences arise because of socially-motivated prompting differences.
  arXiv  Detail & Related papers  (2024-03-04T17:34:34Z)
• Decoding Susceptibility: Modeling Misbelief to Misinformation Through a Computational Approach [61.04606493712002]
  Susceptibility to misinformation describes the degree of belief in unverifiable claims that is not observable. Existing susceptibility studies heavily rely on self-reported beliefs. We propose a computational approach to model users' latent susceptibility levels.
  arXiv  Detail & Related papers  (2023-11-16T07:22:56Z)
• MoCa: Measuring Human-Language Model Alignment on Causal and Moral Judgment Tasks [49.60689355674541]
  A rich literature in cognitive science has studied people's causal and moral intuitions. This work has revealed a number of factors that systematically influence people's judgments. We test whether large language models (LLMs) make causal and moral judgments about text-based scenarios that align with human participants.
  arXiv  Detail & Related papers  (2023-10-30T15:57:32Z)
• A Semantic Approach to Decidability in Epistemic Planning (Extended Version) [72.77805489645604]
  We use a novel semantic approach to achieve decidability. Specifically, we augment the logic of knowledge S5$_n$ and with an interaction axiom called (knowledge) commutativity. We prove that our framework admits a finitary non-fixpoint characterization of common knowledge, which is of independent interest.
  arXiv  Detail & Related papers  (2023-07-28T11:26:26Z)
• Why not both? Complementing explanations with uncertainty, and the role of self-confidence in Human-AI collaboration [12.47276164048813]
  We conduct an empirical study to identify how uncertainty estimates and model explanations affect users' reliance, understanding, and trust towards a model. We also discuss how the latter may distort the outcome of an analysis based on agreement and switching percentages.
  arXiv  Detail & Related papers  (2023-04-27T12:24:33Z)
• Logic-Based Explainability in Machine Learning [0.0]
  The operation of the most successful Machine Learning models is incomprehensible for human decision makers. In recent years, there have been efforts on devising approaches for explaining ML models. This paper overviews the ongoing research efforts on computing rigorous model-based explanations of ML models.
  arXiv  Detail & Related papers  (2022-10-24T13:43:07Z)
• Machine Explanations and Human Understanding [31.047297225560566]
  Explanations are hypothesized to improve human understanding of machine learning models. empirical studies have found mixed and even negative results. We show how human intuitions play a central role in enabling human understanding.
  arXiv  Detail & Related papers  (2022-02-08T19:00:38Z)
• Empirical Estimates on Hand Manipulation are Recoverable: A Step Towards Individualized and Explainable Robotic Support in Everyday Activities [80.37857025201036]
  Key challenge for robotic systems is to figure out the behavior of another agent. Processing correct inferences is especially challenging when (confounding) factors are not controlled experimentally. We propose equipping robots with the necessary tools to conduct observational studies on people.
  arXiv  Detail & Related papers  (2022-01-27T22:15:56Z)
• Observing Interventions: A logic for thinking about experiments [62.997667081978825]
  This paper makes a first step towards a logic of learning from experiments. Crucial for our approach is the idea that the notion of an intervention can be used as a formal expression of a (real or hypothetical) experiment. For all the proposed logical systems, we provide a sound and complete axiomatization.
  arXiv  Detail & Related papers  (2021-11-25T09:26:45Z)
• On Interpretability and Similarity in Concept-Based Machine Learning [2.3986080077861787]
  We discuss how notions from cooperative game theory can be used to assess the contribution of individual attributes in classification and clustering processes in concept-based machine learning. To address the 3rd question, we present some ideas on how to reduce the number of attributes using similarities in large contexts.
  arXiv  Detail & Related papers  (2021-02-25T07:57:28Z)
• Leveraging Expert Consistency to Improve Algorithmic Decision Support [62.61153549123407]
  We explore the use of historical expert decisions as a rich source of information that can be combined with observed outcomes to narrow the construct gap. We propose an influence function-based methodology to estimate expert consistency indirectly when each case in the data is assessed by a single expert. Our empirical evaluation, using simulations in a clinical setting and real-world data from the child welfare domain, indicates that the proposed approach successfully narrows the construct gap.
  arXiv  Detail & Related papers  (2021-01-24T05:40:29Z)
• When Does Uncertainty Matter?: Understanding the Impact of Predictive Uncertainty in ML Assisted Decision Making [68.19284302320146]
  We carry out user studies to assess how people with differing levels of expertise respond to different types of predictive uncertainty. We found that showing posterior predictive distributions led to smaller disagreements with the ML model's predictions. This suggests that posterior predictive distributions can potentially serve as useful decision aids which should be used with caution and take into account the type of distribution and the expertise of the human.
  arXiv  Detail & Related papers  (2020-11-12T02:23:53Z)
• Machine Common Sense [77.34726150561087]
  Machine common sense remains a broad, potentially unbounded problem in artificial intelligence (AI) This article deals with the aspects of modeling commonsense reasoning focusing on such domain as interpersonal interactions.
  arXiv  Detail & Related papers  (2020-06-15T13:59:47Z)
• Causal Interpretability for Machine Learning -- Problems, Methods and Evaluation [30.664546254344593]
  Most machine learning models are black-boxes, and it is obscure how the decisions are made by them. To provide insights into the decision making processes of these models, a variety of traditional interpretable models have been proposed. In this work, models that aim to answer causal questions are referred to as causal interpretable models.
  arXiv  Detail & Related papers  (2020-03-09T06:16:00Z)

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.