Generative AI Models: Opportunities and Risks for Industry and Authorities
- URL: http://arxiv.org/abs/2406.04734v2
- Date: Mon, 03 Feb 2025 11:03:02 GMT
- Title: Generative AI Models: Opportunities and Risks for Industry and Authorities
- Authors: Tobias Alt, Andrea Ibisch, Clemens Meiser, Anna Wilhelm, Raphael Zimmer, Jonas Ditz, Dominique Dresen, Christoph Droste, Jens Karschau, Friederike Laus, Oliver Müller, Matthias Neu, Rainer Plaga, Carola Plesch, Britta Sennewald, Thomas Thaeren, Kristina Unverricht, Steffen Waurick,
- Abstract summary: Generative AI models are capable of performing a wide variety of tasks that have traditionally required creativity and human understanding.
During training, they learn patterns from existing data and can subsequently generate new content.
Many risks associated with generative AI must be addressed during development or can only be influenced by the operating organisation.
- Score: 1.3196892898418466
- License:
- Abstract: Generative AI models are capable of performing a wide variety of tasks that have traditionally required creativity and human understanding. During training, they learn patterns from existing data and can subsequently generate new content such as texts, images, audio, and videos that align with these patterns. Due to their versatility and generally high-quality results, they represent, on the one hand, an opportunity for digitalisation. On the other hand, the use of generative AI models introduces novel IT security risks that must be considered as part of a comprehensive analysis of the IT security threat landscape. In response to this risk potential, companies or authorities intending to use generative AI should conduct an individual risk analysis before integrating it into their workflows. The same applies to developers and operators, as many risks associated with generative AI must be addressed during development or can only be influenced by the operating organisation. Based on this, existing security measures can be adapted, and additional measures implemented.
Related papers
- Computational Safety for Generative AI: A Signal Processing Perspective [65.268245109828]
computational safety is a mathematical framework that enables the quantitative assessment, formulation, and study of safety challenges in GenAI.
We show how sensitivity analysis and loss landscape analysis can be used to detect malicious prompts with jailbreak attempts.
We discuss key open research challenges, opportunities, and the essential role of signal processing in computational AI safety.
arXiv Detail & Related papers (2025-02-18T02:26:50Z) - Fully Autonomous AI Agents Should Not be Developed [58.88624302082713]
This paper argues that fully autonomous AI agents should not be developed.
In support of this position, we build from prior scientific literature and current product marketing to delineate different AI agent levels.
Our analysis reveals that risks to people increase with the autonomy of a system.
arXiv Detail & Related papers (2025-02-04T19:00:06Z) - Engineering Trustworthy AI: A Developer Guide for Empirical Risk Minimization [53.80919781981027]
Key requirements for trustworthy AI can be translated into design choices for the components of empirical risk minimization.
We hope to provide actionable guidance for building AI systems that meet emerging standards for trustworthiness of AI.
arXiv Detail & Related papers (2024-10-25T07:53:32Z) - Data Analysis in the Era of Generative AI [56.44807642944589]
This paper explores the potential of AI-powered tools to reshape data analysis, focusing on design considerations and challenges.
We explore how the emergence of large language and multimodal models offers new opportunities to enhance various stages of data analysis workflow.
We then examine human-centered design principles that facilitate intuitive interactions, build user trust, and streamline the AI-assisted analysis workflow across multiple apps.
arXiv Detail & Related papers (2024-09-27T06:31:03Z) - EARBench: Towards Evaluating Physical Risk Awareness for Task Planning of Foundation Model-based Embodied AI Agents [53.717918131568936]
Embodied artificial intelligence (EAI) integrates advanced AI models into physical entities for real-world interaction.
Foundation models as the "brain" of EAI agents for high-level task planning have shown promising results.
However, the deployment of these agents in physical environments presents significant safety challenges.
This study introduces EARBench, a novel framework for automated physical risk assessment in EAI scenarios.
arXiv Detail & Related papers (2024-08-08T13:19:37Z) - Risks and Opportunities of Open-Source Generative AI [64.86989162783648]
Applications of Generative AI (Gen AI) are expected to revolutionize a number of different areas, ranging from science & medicine to education.
The potential for these seismic changes has triggered a lively debate about the potential risks of the technology, and resulted in calls for tighter regulation.
This regulation is likely to put at risk the budding field of open-source generative AI.
arXiv Detail & Related papers (2024-05-14T13:37:36Z) - Model evaluation for extreme risks [46.53170857607407]
Further progress in AI development could lead to capabilities that pose extreme risks, such as offensive cyber capabilities or strong manipulation skills.
We explain why model evaluation is critical for addressing extreme risks.
arXiv Detail & Related papers (2023-05-24T16:38:43Z) - A Brief Overview of AI Governance for Responsible Machine Learning
Systems [3.222802562733787]
This position paper seeks to present a brief introduction to AI governance, which is a framework designed to oversee the responsible use of AI.
Due to the probabilistic nature of AI, the risks associated with it are far greater than traditional technologies.
arXiv Detail & Related papers (2022-11-21T23:48:51Z) - Quantitative AI Risk Assessments: Opportunities and Challenges [7.35411010153049]
Best way to reduce risks is to implement comprehensive AI lifecycle governance.
Risks can be quantified using metrics from the technical community.
This paper explores these issues, focusing on the opportunities, challenges, and potential impacts of such an approach.
arXiv Detail & Related papers (2022-09-13T21:47:25Z) - Towards Risk Modeling for Collaborative AI [5.941104748966331]
Collaborative AI systems aim at working together with humans in a shared space to achieve a common goal.
This setting imposes potentially hazardous circumstances due to contacts that could harm human beings.
We introduce a risk modeling approach tailored to Collaborative AI systems.
arXiv Detail & Related papers (2021-03-12T18:53:06Z)
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.