Beyond Release: Access Considerations for Generative AI Systems
- URL: http://arxiv.org/abs/2502.16701v2
- Date: Fri, 11 Apr 2025 07:07:21 GMT
- Title: Beyond Release: Access Considerations for Generative AI Systems
- Authors: Irene Solaiman, Rishi Bommasani, Dan Hendrycks, Ariel Herbert-Voss, Yacine Jernite, Aviya Skowron, Andrew Trask,
- Abstract summary: Generative AI release decisions determine whether system components are made available, but release does not address many other elements that change how users and stakeholders are able to engage with a system.<n>Access to system components informs potential risks and benefits.<n>This framework better encompasses the landscape and risk-benefit tradeoffs of system releases to inform system release decisions, research, and policy.
- Score: 33.117342870212156
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Generative AI release decisions determine whether system components are made available, but release does not address many other elements that change how users and stakeholders are able to engage with a system. Beyond release, access to system components informs potential risks and benefits. Access refers to practical needs, infrastructurally, technically, and societally, in order to use available components in some way. We deconstruct access along three axes: resourcing, technical usability, and utility. Within each category, a set of variables per system component clarify tradeoffs. For example, resourcing requires access to computing infrastructure to serve model weights. We also compare the accessibility of four high performance language models, two open-weight and two closed-weight, showing similar considerations for all based instead on access variables. Access variables set the foundation for being able to scale or increase access to users; we examine the scale of access and how scale affects ability to manage and intervene on risks. This framework better encompasses the landscape and risk-benefit tradeoffs of system releases to inform system release decisions, research, and policy.
Related papers
- MATE: LLM-Powered Multi-Agent Translation Environment for Accessibility Applications [44.99833362998488]
MATE, a multimodal accessibility MAS, performs the modality conversions based on the user's needs.<n>MATE can be applied to a wide range of domains, industries, and areas, such as healthcare.<n>ModCon-Task-Identifier is a model that is capable of extracting the precise modality conversion task from the user input.
arXiv Detail & Related papers (2025-06-24T10:40:23Z) - Access Controls Will Solve the Dual-Use Dilemma [0.0]
It is unclear whether to answer dual-use requests, since the same query could be either harmless or harmful depending on who made it and why.<n>To make better decisions, such systems would need to examine requests' real-world context.<n>We propose a conceptual framework based on access controls where only verified users can access dual-use outputs.
arXiv Detail & Related papers (2025-05-14T12:38:08Z) - "Shifting Access Control Left" using Asset and Goal Models [0.8158530638728498]
We present a tool-supported technique identifying knowledge asymmetries around access control based on asset and goal models.
We provide boundary objects to make access control transparent, thereby making knowledge about access control concerns more symmetric.
arXiv Detail & Related papers (2025-04-24T19:45:11Z) - Adaptive Orchestration of Modular Generative Information Access Systems [59.102816309859584]
We argue that the architecture of future modular generative information access systems will not just assemble powerful components, but enable a self-organizing system.
This perspective urges the IR community to rethink modular system designs for developing adaptive, self-optimizing, and future-ready architectures.
arXiv Detail & Related papers (2025-04-24T11:35:43Z) - Combining AI Control Systems and Human Decision Support via Robustness and Criticality [53.10194953873209]
We extend a methodology for adversarial explanations (AE) to state-of-the-art reinforcement learning frameworks.
We show that the learned AI control system demonstrates robustness against adversarial tampering.
In a training / learning framework, this technology can improve both the AI's decisions and explanations through human interaction.
arXiv Detail & Related papers (2024-07-03T15:38:57Z) - AccessLens: Auto-detecting Inaccessibility of Everyday Objects [17.269659576368536]
We introduce AccessLens, an end-to-end system designed to identify inaccessible interfaces in daily objects.
Our approach involves training a detector using the novel AccessDB dataset designed to automatically recognize 21 distinct Inaccessibility Classes.
AccessMeta serves as a robust way to build a comprehensive dictionary linking these accessibility classes to open-source 3D augmentation designs.
arXiv Detail & Related papers (2024-01-29T09:27:55Z) - Machine Learning in Access Control: A Taxonomy and Survey [0.0]
We survey and summarize various machine learning approaches to solve different access control problems.
We highlight current limitations and open challenges such as lack of public real-world datasets, administration of ML-based access control systems, understanding a black-box ML model's decision, etc.
arXiv Detail & Related papers (2022-07-04T22:36:27Z) - Toward Deep Learning Based Access Control [3.2511618464944547]
This paper proposes Deep Learning Based Access Control (DLBAC) by leveraging significant advances in deep learning technology.
DLBAC could complement and, in the long-term, has the potential to even replace, classical access control models with a neural network.
We demonstrate the feasibility of the proposed approach by addressing issues related to accuracy, generalization, and explainability.
arXiv Detail & Related papers (2022-03-28T22:05:11Z) - Composing Complex and Hybrid AI Solutions [52.00820391621739]
We describe an extension of the Acumos system towards enabling the above features for general AI applications.
Our extensions include support for more generic components with gRPC/Protobuf interfaces.
We provide examples of deployable solutions and their interfaces.
arXiv Detail & Related papers (2022-02-25T08:57:06Z) - Overcoming Failures of Imagination in AI Infused System Development and
Deployment [71.9309995623067]
NeurIPS 2020 requested that research paper submissions include impact statements on "potential nefarious uses and the consequences of failure"
We argue that frameworks of harms must be context-aware and consider a wider range of potential stakeholders, system affordances, as well as viable proxies for assessing harms in the widest sense.
arXiv Detail & Related papers (2020-11-26T18:09:52Z) - Towards an Interface Description Template for AI-enabled Systems [77.34726150561087]
Reuse is a common system architecture approach that seeks to instantiate a system architecture with existing components.
There is currently no framework that guides the selection of necessary information to assess their portability to operate in a system different than the one for which the component was originally purposed.
We present ongoing work on establishing an interface description template that captures the main information of an AI-enabled component.
arXiv Detail & Related papers (2020-07-13T20:30:26Z)
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.