Taxonomizing Representational Harms using Speech Act Theory
- URL: http://arxiv.org/abs/2504.00928v1
- Date: Tue, 01 Apr 2025 16:00:17 GMT
- Title: Taxonomizing Representational Harms using Speech Act Theory
- Authors: Emily Corvi, Hannah Washington, Stefanie Reed, Chad Atalla, Alexandra Chouldechova, P. Alex Dow, Jean Garcia-Gathright, Nicholas Pangakis, Emily Sheng, Dan Vann, Matthew Vogel, Hanna Wallach,
- Abstract summary: We present a framework that conceptualizes representational harms caused by generative language systems as the perlocutionary effects (i.e., real-world impacts) of particular types of illocutionary acts.<n>We then use our framework to develop a granular taxonomy of illocutionary acts that cause representational harms.
- Score: 38.428057679114424
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Representational harms are widely recognized among fairness-related harms caused by generative language systems. However, their definitions are commonly under-specified. We present a framework, grounded in speech act theory (Austin, 1962), that conceptualizes representational harms caused by generative language systems as the perlocutionary effects (i.e., real-world impacts) of particular types of illocutionary acts (i.e., system behaviors). Building on this argument and drawing on relevant literature from linguistic anthropology and sociolinguistics, we provide new definitions stereotyping, demeaning, and erasure. We then use our framework to develop a granular taxonomy of illocutionary acts that cause representational harms, going beyond the high-level taxonomies presented in previous work. We also discuss the ways that our framework and taxonomy can support the development of valid measurement instruments. Finally, we demonstrate the utility of our framework and taxonomy via a case study that engages with recent conceptual debates about what constitutes a representational harm and how such harms should be measured.
Related papers
- Large Language Models as Quasi-crystals: Coherence Without Repetition in Generative Text [0.0]
essay proposes an analogy between large language models (LLMs) and quasicrystals, systems that exhibit global coherence without periodic repetition, generated through local constraints.
Drawing on the history of quasicrystals, it highlights an alternative mode of coherence in generative language: constraint-based organization without repetition or symbolic intent.
This essay aims to reframe the current discussion around large language models, not by rejecting existing methods, but by suggesting an additional axis of interpretation grounded in structure rather than semantics.
arXiv Detail & Related papers (2025-04-16T11:27:47Z) - Gumbel Counterfactual Generation From Language Models [64.55296662926919]
We show that counterfactual reasoning is conceptually distinct from interventions.<n>We propose a framework for generating true string counterfactuals.<n>We show that the approach produces meaningful counterfactuals while at the same time showing that commonly used intervention techniques have considerable undesired side effects.
arXiv Detail & Related papers (2024-11-11T17:57:30Z) - Towards a Harms Taxonomy of AI Likeness Generation [0.0]
Generative artificial intelligence models, when trained on a sufficient number of a person's images, can replicate their identifying features in a photorealistic manner.
This paper explores philosophical and policy issues surrounding generated likeness.
We present a taxonomy of harms associated with generated likeness, derived from a comprehensive meta-analysis of relevant literature.
arXiv Detail & Related papers (2024-06-29T16:00:42Z) - Concept Arithmetics for Circumventing Concept Inhibition in Diffusion Models [58.065255696601604]
We use compositional property of diffusion models, which allows to leverage multiple prompts in a single image generation.
We argue that it is essential to consider all possible approaches to image generation with diffusion models that can be employed by an adversary.
arXiv Detail & Related papers (2024-04-21T16:35:16Z) - Beyond Behaviorist Representational Harms: A Plan for Measurement and Mitigation [1.7355698649527407]
This study focuses on an examination of current definitions of representational harms to discern what is included and what is not.
Our work highlights the unique vulnerabilities of large language models to perpetrating representational harms.
The overarching aim of this research is to establish a framework for broadening the definition of representational harms.
arXiv Detail & Related papers (2024-01-25T00:54:10Z) - An Investigation of Representation and Allocation Harms in Contrastive
Learning [55.42336321517228]
We demonstrate that contrastive learning (CL) tends to collapse representations of minority groups with certain majority groups.
We refer to this phenomenon as representation harm and demonstrate it on image and text datasets using the corresponding popular CL methods.
We provide a theoretical explanation for representation harm using a neural block model that leads to a representational collapse in a contrastive learning setting.
arXiv Detail & Related papers (2023-10-02T19:25:37Z) - Natural Language Decompositions of Implicit Content Enable Better Text Representations [52.992875653864076]
We introduce a method for the analysis of text that takes implicitly communicated content explicitly into account.
We use a large language model to produce sets of propositions that are inferentially related to the text that has been observed.
Our results suggest that modeling the meanings behind observed language, rather than the literal text alone, is a valuable direction for NLP.
arXiv Detail & Related papers (2023-05-23T23:45:20Z) - HyperMiner: Topic Taxonomy Mining with Hyperbolic Embedding [54.52651110749165]
We present a novel framework that introduces hyperbolic embeddings to represent words and topics.
With the tree-likeness property of hyperbolic space, the underlying semantic hierarchy can be better exploited to mine more interpretable topics.
arXiv Detail & Related papers (2022-10-16T02:54:17Z) - A Causal Analysis of Harm [18.7822411439221]
There is a growing need for a legal and regulatory framework to address when and how autonomous systems harm someone.
This paper formally defines a qualitative notion of harm that uses causal models and is based on a well-known definition of actual causality.
We show that our definition is able to handle the examples from the literature, and illustrate its importance for reasoning about situations involving autonomous systems.
arXiv Detail & Related papers (2022-10-11T10:36:24Z) - Exploring Discourse Structures for Argument Impact Classification [48.909640432326654]
This paper empirically shows that the discourse relations between two arguments along the context path are essential factors for identifying the persuasive power of an argument.
We propose DisCOC to inject and fuse the sentence-level structural information with contextualized features derived from large-scale language models.
arXiv Detail & Related papers (2021-06-02T06:49:19Z) - Models we Can Trust: Toward a Systematic Discipline of (Agent-Based)
Model Interpretation and Validation [0.0]
We advocate the development of a discipline of interacting with and extracting information from models.
We outline some directions for the development of a such a discipline.
arXiv Detail & Related papers (2021-02-23T10:52:22Z) - Thinking About Causation: A Causal Language with Epistemic Operators [58.720142291102135]
We extend the notion of a causal model with a representation of the state of an agent.
On the side of the object language, we add operators to express knowledge and the act of observing new information.
We provide a sound and complete axiomatization of the logic, and discuss the relation of this framework to causal team semantics.
arXiv Detail & Related papers (2020-10-30T12:16:45Z)
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.