Typology of Risks of Generative Text-to-Image Models

• URL: http://arxiv.org/abs/2307.05543v1
• Date: Sat, 8 Jul 2023 20:33:30 GMT
• Title: Typology of Risks of Generative Text-to-Image Models
• Authors: Charlotte Bird and Eddie L. Ungless and Atoosa Kasirzadeh
• Abstract summary: This paper investigates the direct risks and harms associated with modern text-to-image generative models, such as DALL-E and Midjourney. Our review reveals significant knowledge gaps concerning the understanding and treatment of these risks despite some already being addressed. We identify 22 distinct risk types, spanning issues from data bias to malicious use.
• Score: 1.933681537640272
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper investigates the direct risks and harms associated with modern text-to-image generative models, such as DALL-E and Midjourney, through a comprehensive literature review. While these models offer unprecedented capabilities for generating images, their development and use introduce new types of risk that require careful consideration. Our review reveals significant knowledge gaps concerning the understanding and treatment of these risks despite some already being addressed. We offer a taxonomy of risks across six key stakeholder groups, inclusive of unexplored issues, and suggest future research directions. We identify 22 distinct risk types, spanning issues from data bias to malicious use. The investigation presented here is intended to enhance the ongoing discourse on responsible model development and deployment. By highlighting previously overlooked risks and gaps, it aims to shape subsequent research and governance initiatives, guiding them toward the responsible, secure, and ethically conscious evolution of text-to-image models.

Related papers

• Towards Trustworthy Retrieval Augmented Generation for Large Language Models: A Survey [92.36487127683053]
  Retrieval-Augmented Generation (RAG) is an advanced technique designed to address the challenges of Artificial Intelligence-Generated Content (AIGC) RAG provides reliable and up-to-date external knowledge, reduces hallucinations, and ensures relevant context across a wide range of tasks. Despite RAG's success and potential, recent studies have shown that the RAG paradigm also introduces new risks, including privacy concerns, adversarial attacks, and accountability issues.
  arXiv  Detail & Related papers  (2025-02-08T06:50:47Z)
• Safety at Scale: A Comprehensive Survey of Large Model Safety [299.801463557549]
  We present a comprehensive taxonomy of safety threats to large models, including adversarial attacks, data poisoning, backdoor attacks, jailbreak and prompt injection attacks, energy-latency attacks, data and model extraction attacks, and emerging agent-specific threats. We identify and discuss the open challenges in large model safety, emphasizing the need for comprehensive safety evaluations, scalable and effective defense mechanisms, and sustainable data practices.
  arXiv  Detail & Related papers  (2025-02-02T05:14:22Z)
• Supervision policies can shape long-term risk management in general-purpose AI models [0.0]
  We develop a simulation framework parameterized by features extracted from the diverse landscape of risk, incident, or hazard reporting ecosystems. We evaluate four supervision policies: non-prioritized (first-come, first-served), random selection, priority-based (addressing the highest-priority risks first), and diversity-prioritized (balancing high-priority risks with comprehensive coverage across risk types) Our results indicate that while priority-based and diversity-prioritized policies are more effective at mitigating high-impact risks, they may inadvertently neglect systemic issues reported by the broader community.
  arXiv  Detail & Related papers  (2025-01-10T17:52:34Z)
• DODGE: Ontology-Aware Risk Assessment via Object-Oriented Disruption Graphs [0.9387233631570749]
  The Common Ontology of Value and Risk (COVER) highlights how the role of objects and their relationships remains pivotal to performing transparent, complete and accountable risk assessment. We operationalize some of the notions proposed by COVER by presenting a new framework for risk assessment: DODGE.
  arXiv  Detail & Related papers  (2024-12-18T15:44:04Z)
• A Formal Framework for Assessing and Mitigating Emergent Security Risks in Generative AI Models: Bridging Theory and Dynamic Risk Mitigation [0.3413711585591077]
  As generative AI systems, including large language models (LLMs) and diffusion models, advance rapidly, their growing adoption has led to new and complex security risks. This paper introduces a novel formal framework for categorizing and mitigating these emergent security risks. We identify previously under-explored risks, including latent space exploitation, multi-modal cross-attack vectors, and feedback-loop-induced model degradation.
  arXiv  Detail & Related papers  (2024-10-15T02:51:32Z)
• Risks and NLP Design: A Case Study on Procedural Document QA [52.557503571760215]
  We argue that clearer assessments of risks and harms to users will be possible when we specialize the analysis to more concrete applications and their plausible users. We conduct a risk-oriented error analysis that could then inform the design of a future system to be deployed with lower risk of harm and better performance.
  arXiv  Detail & Related papers  (2024-08-16T17:23:43Z)
• On the Societal Impact of Open Foundation Models [93.67389739906561]
  We focus on open foundation models, defined here as those with broadly available model weights. We identify five distinctive properties of open foundation models that lead to both their benefits and risks.
  arXiv  Detail & Related papers  (2024-02-27T16:49:53Z)
• C-RAG: Certified Generation Risks for Retrieval-Augmented Language Models [57.10361282229501]
  We propose C-RAG, the first framework to certify generation risks for RAG models. Specifically, we provide conformal risk analysis for RAG models and certify an upper confidence bound of generation risks. We prove that RAG achieves a lower conformal generation risk than that of a single LLM when the quality of the retrieval model and transformer is non-trivial.
  arXiv  Detail & Related papers  (2024-02-05T16:46:16Z)
• 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)
• Membership Inference Attacks Against Text-to-image Generation Models [23.39695974954703]
  This paper performs the first privacy analysis of text-to-image generation models through the lens of membership inference. We propose three key intuitions about membership information and design four attack methodologies accordingly. All of the proposed attacks can achieve significant performance, in some cases even close to an accuracy of 1, and thus the corresponding risk is much more severe than that shown by existing membership inference attacks.
  arXiv  Detail & Related papers  (2022-10-03T14:31:39Z)

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.