Deployment Corrections: An incident response framework for frontier AI
models
- URL: http://arxiv.org/abs/2310.00328v1
- Date: Sat, 30 Sep 2023 10:07:39 GMT
- Title: Deployment Corrections: An incident response framework for frontier AI
models
- Authors: Joe O'Brien, Shaun Ee, Zoe Williams
- Abstract summary: This paper explores contingency plans for cases where pre-deployment risk management falls short.
We describe a toolkit of deployment corrections that AI developers can use to respond to dangerous capabilities.
We recommend frontier AI developers, standard-setting organizations, and regulators should collaborate to define a standardized industry-wide approach.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: A comprehensive approach to addressing catastrophic risks from AI models
should cover the full model lifecycle. This paper explores contingency plans
for cases where pre-deployment risk management falls short: where either very
dangerous models are deployed, or deployed models become very dangerous.
Informed by incident response practices from industries including
cybersecurity, we describe a toolkit of deployment corrections that AI
developers can use to respond to dangerous capabilities, behaviors, or use
cases of AI models that develop or are detected after deployment. We also
provide a framework for AI developers to prepare and implement this toolkit.
We conclude by recommending that frontier AI developers should (1) maintain
control over model access, (2) establish or grow dedicated teams to design and
maintain processes for deployment corrections, including incident response
plans, and (3) establish these deployment corrections as allowable actions with
downstream users. We also recommend frontier AI developers, standard-setting
organizations, and regulators should collaborate to define a standardized
industry-wide approach to the use of deployment corrections in incident
response.
Caveat: This work applies to frontier AI models that are made available
through interfaces (e.g., API) that provide the AI developer or another
upstream party means of maintaining control over access (e.g., GPT-4 or
Claude). It does not apply to management of catastrophic risk from open-source
models (e.g., BLOOM or Llama-2), for which the restrictions we discuss are
largely unenforceable.
Related papers
- Generative AI Models: Opportunities and Risks for Industry and Authorities [1.3914994102950027]
Generative AI models are capable of performing a wide range of tasks that traditionally require creativity and human understanding.
They learn patterns from existing data during training and can subsequently generate new content.
The use of generative AI models introduces novel IT security risks that need to be considered.
arXiv Detail & Related papers (2024-06-07T08:34:30Z) - 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) - Near to Mid-term Risks and Opportunities of Open-Source Generative AI [94.06233419171016]
Applications of Generative 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 potential risks 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 (2024-04-25T21:14:24Z) - A Path Towards Legal Autonomy: An interoperable and explainable approach to extracting, transforming, loading and computing legal information using large language models, expert systems and Bayesian networks [2.2192488799070444]
Legal autonomy can be achieved either by imposing constraints on AI actors such as developers, deployers and users, or by imposing constraints on the range and scope of the impact that AI agents can have on the environment.
The latter approach involves encoding extant rules concerning AI driven devices into the software of AI agents controlling those devices.
This is a challenge since the effectivity of such an approach requires a method of extracting, loading, transforming and computing legal information that would be both explainable and legally interoperable.
arXiv Detail & Related papers (2024-03-27T13:12:57Z) - Detectors for Safe and Reliable LLMs: Implementations, Uses, and Limitations [76.19419888353586]
Large language models (LLMs) are susceptible to a variety of risks, from non-faithful output to biased and toxic generations.
We present our efforts to create and deploy a library of detectors: compact and easy-to-build classification models that provide labels for various harms.
arXiv Detail & Related papers (2024-03-09T21:07:16Z) - Coordinated pausing: An evaluation-based coordination scheme for
frontier AI developers [0.2913760942403036]
This paper focuses on one possible response: coordinated pausing.
It proposes an evaluation-based coordination scheme that consists of five main steps.
It concludes that coordinated pausing is a promising mechanism for tackling emerging risks from frontier AI models.
arXiv Detail & Related papers (2023-09-30T13:38:33Z) - Frontier AI Regulation: Managing Emerging Risks to Public Safety [15.85618115026625]
"Frontier AI" models could possess dangerous capabilities sufficient to pose severe risks to public safety.
Industry self-regulation is an important first step.
We propose an initial set of safety standards.
arXiv Detail & Related papers (2023-07-06T17:03:25Z) - Regulating ChatGPT and other Large Generative AI Models [0.0]
Large generative AI models (LGAIMs) are rapidly transforming the way we communicate, illustrate, and create.
This paper will situate these new generative models in the current debate on trustworthy AI regulation.
It suggests a novel terminology to capture the AI value chain in LGAIM settings.
arXiv Detail & Related papers (2023-02-05T08:56:45Z) - Self-Destructing Models: Increasing the Costs of Harmful Dual Uses of
Foundation Models [103.71308117592963]
We present an algorithm for training self-destructing models leveraging techniques from meta-learning and adversarial learning.
In a small-scale experiment, we show MLAC can largely prevent a BERT-style model from being re-purposed to perform gender identification.
arXiv Detail & Related papers (2022-11-27T21:43:45Z) - Monitoring ROS2: from Requirements to Autonomous Robots [58.720142291102135]
This paper provides an overview of a formal approach to generating runtime monitors for autonomous robots from requirements written in a structured natural language.
Our approach integrates the Formal Requirement Elicitation Tool (FRET) with Copilot, a runtime verification framework, through the Ogma integration tool.
arXiv Detail & Related papers (2022-09-28T12:19:13Z) - Safe Reinforcement Learning via Curriculum Induction [94.67835258431202]
In safety-critical applications, autonomous agents may need to learn in an environment where mistakes can be very costly.
Existing safe reinforcement learning methods make an agent rely on priors that let it avoid dangerous situations.
This paper presents an alternative approach inspired by human teaching, where an agent learns under the supervision of an automatic instructor.
arXiv Detail & Related papers (2020-06-22T10:48:17Z)
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.