Towards a Harms Taxonomy of AI Likeness Generation

• URL: http://arxiv.org/abs/2407.12030v1
• Date: Sat, 29 Jun 2024 16:00:42 GMT
• Title: Towards a Harms Taxonomy of AI Likeness Generation
• Authors: Ben Bariach, Bernie Hogan, Keegan McBride,
• Abstract summary: 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative artificial intelligence models, when trained on a sufficient number of a person's images, can replicate their identifying features in a photorealistic manner. We refer to this process as 'likeness generation'. Likeness-featuring synthetic outputs often present a person's likeness without their control or consent, and may lead to harmful consequences. This paper explores philosophical and policy issues surrounding generated likeness. It begins by offering a conceptual framework for understanding likeness generation by examining the novel capabilities introduced by generative systems. The paper then establishes a definition of likeness by tracing its historical development in legal literature. Building on this foundation, we present a taxonomy of harms associated with generated likeness, derived from a comprehensive meta-analysis of relevant literature. This taxonomy categorises harms into seven distinct groups, unified by shared characteristics. Utilising this taxonomy, we raise various considerations that need to be addressed for the deployment of appropriate mitigations. Given the multitude of stakeholders involved in both the creation and distribution of likeness, we introduce concepts such as indexical sufficiency, a distinction between generation and distribution, and harms as having a context-specific nature. This work aims to serve industry, policymakers, and future academic researchers in their efforts to address the societal challenges posed by likeness generation.

Related papers

• Taxonomizing Representational Harms using Speech Act Theory [38.428057679114424]
  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. We then use our framework to develop a granular taxonomy of illocutionary acts that cause representational harms.
  arXiv  Detail & Related papers  (2025-04-01T16:00:17Z)
• On the Fairness, Diversity and Reliability of Text-to-Image Generative Models [49.60774626839712]
  multimodal generative models have sparked critical discussions on their fairness, reliability, and potential for misuse. We propose an evaluation framework designed to assess model reliability through their responses to perturbations in the embedding space. Our method lays the groundwork for detecting unreliable, bias-injected models and retrieval of bias provenance.
  arXiv  Detail & Related papers  (2024-11-21T09:46:55Z)
• Generative midtended cognition and Artificial Intelligence. Thinging with thinging things [0.0]
  "generative midtended cognition" explores the integration of generative AI with human cognition. Term "generative" reflects AI's ability to iteratively produce structured outputs, while "midtended" captures the potential hybrid (human-AI) nature of the process.
  arXiv  Detail & Related papers  (2024-11-11T09:14:27Z)
• 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)
• Mapping the Ethics of Generative AI: A Comprehensive Scoping Review [0.0]
  We conduct a scoping review on the ethics of generative artificial intelligence, including especially large language models and text-to-image models. Our analysis provides a taxonomy of 378 normative issues in 19 topic areas and ranks them according to their prevalence in the literature. The study offers a comprehensive overview for scholars, practitioners, or policymakers, condensing the ethical debates surrounding fairness, safety, harmful content, hallucinations, privacy, interaction risks, security, alignment, societal impacts, and others.
  arXiv  Detail & Related papers  (2024-02-13T09:38:17Z)
• Foundational Models Defining a New Era in Vision: A Survey and Outlook [151.49434496615427]
  Vision systems to see and reason about the compositional nature of visual scenes are fundamental to understanding our world. The models learned to bridge the gap between such modalities coupled with large-scale training data facilitate contextual reasoning, generalization, and prompt capabilities at test time. The output of such models can be modified through human-provided prompts without retraining, e.g., segmenting a particular object by providing a bounding box, having interactive dialogues by asking questions about an image or video scene or manipulating the robot's behavior through language instructions.
  arXiv  Detail & Related papers  (2023-07-25T17:59:18Z)
• The Creative Frontier of Generative AI: Managing the Novelty-Usefulness Tradeoff [0.4873362301533825]
  We explore the optimal balance between novelty and usefulness in generative Artificial Intelligence (AI) systems. Overemphasizing either aspect can lead to limitations such as hallucinations and memorization.
  arXiv  Detail & Related papers  (2023-06-06T11:44:57Z)
• Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Survey [50.58063811745676]
  This work provides a survey of practical methods for addressing potential threats and societal harms from language generation models. We draw on several prior works' of language model risks to present a structured overview of strategies for detecting and ameliorating different kinds of risks/harms of language generators.
  arXiv  Detail & Related papers  (2022-10-14T10:43:39Z)
• CausalCity: Complex Simulations with Agency for Causal Discovery and Reasoning [68.74447489372037]
  We present a high-fidelity simulation environment that is designed for developing algorithms for causal discovery and counterfactual reasoning. A core component of our work is to introduce textitagency, such that it is simple to define and create complex scenarios. We perform experiments with three state-of-the-art methods to create baselines and highlight the affordances of this environment.
  arXiv  Detail & Related papers  (2021-06-25T00:21:41Z)
• Is Disentanglement all you need? Comparing Concept-based & Disentanglement Approaches [24.786152654589067]
  We give an overview of concept-based explanations and disentanglement approaches. We show that state-of-the-art approaches from both classes can be data inefficient, sensitive to the specific nature of the classification/regression task, or sensitive to the employed concept representation.
  arXiv  Detail & Related papers  (2021-04-14T15:06:34Z)
• Lexically-constrained Text Generation through Commonsense Knowledge Extraction and Injection [62.071938098215085]
  We focus on the Commongen benchmark, wherein the aim is to generate a plausible sentence for a given set of input concepts. We propose strategies for enhancing the semantic correctness of the generated text.
  arXiv  Detail & Related papers  (2020-12-19T23:23:40Z)
• On the Binding Problem in Artificial Neural Networks [12.04468744445707]
  We argue that the underlying cause for this shortcoming is their inability to dynamically and flexibly bind information. We propose a unifying framework that revolves around forming meaningful entities from unstructured sensory inputs. We believe that a compositional approach to AI, in terms of grounded symbol-like representations, is of fundamental importance for realizing human-level generalization.
  arXiv  Detail & Related papers  (2020-12-09T18:02:49Z)

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.