Cover Image

Declare and Justify: Explicit assumptions in AI evaluations are necessary for effective regulation

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Barnett, Peter, Thiergart, Lisa
Format:	Preprint
Published:	2024
Subjects:	Artificial Intelligence Computers and Society
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

What AI evaluations for preventing catastrophic risks can and cannot do
by: Barnett, Peter, et al.
Published: (2024)

Position: Ensuring mutual privacy is necessary for effective external evaluation of proprietary AI systems
by: Bucknall, Ben, et al.
Published: (2025)

AI Governance to Avoid Extinction: The Strategic Landscape and Actionable Research Questions
by: Barnett, Peter, et al.
Published: (2025)

Safety Cases: How to Justify the Safety of Advanced AI Systems
by: Clymer, Joshua, et al.
Published: (2024)

Technical Requirements for Halting Dangerous AI Activities
by: Barnett, Peter, et al.
Published: (2025)

Justified Evidence Collection for Argument-based AI Fairness Assurance
by: Sabuncuoglu, Alpay, et al.
Published: (2025)

Verification methods for international AI agreements
by: Wasil, Akash R., et al.
Published: (2024)

Mechanisms to Verify International Agreements About AI Development
by: Scher, Aaron, et al.
Published: (2025)

Informing AI Policy Assessment using Large-Scale Simulation of Interventions
by: Barnett, Julia, et al.
Published: (2026)

Governing dual-use technologies: Case studies of international security agreements and lessons for AI governance
by: Wasil, Akash R., et al.
Published: (2024)

Verbalizing LLMs' assumptions to explain and control sycophancy
by: Cheng, Myra, et al.
Published: (2026)

Envisioning Stakeholder-Action Pairs to Mitigate Negative Impacts of AI: A Participatory Approach to Inform Policy Making
by: Barnett, Julia, et al.
Published: (2025)

A cross-regional review of AI safety regulations in the commercial aviation
by: Barr, Penny A., et al.
Published: (2025)

From Rogue to Safe AI: The Role of Explicit Refusals in Aligning LLMs with International Humanitarian Law
by: Mavi, John, et al.
Published: (2025)

Investigating Self-regulated Learning Sequences within a Generative AI-based Intelligent Tutoring System
by: Gao, Jie, et al.
Published: (2026)

Standing on FURM ground -- A framework for evaluating Fair, Useful, and Reliable AI Models in healthcare systems
by: Callahan, Alison, et al.
Published: (2024)

Reducing research bureaucracy in UK higher education: Can generative AI assist with the internal evaluation of quality?
by: Fletcher, Gordon, et al.
Published: (2025)

Empowering the Future Workforce: Prioritizing Education for the AI-Accelerated Job Market
by: Amini, Lisa, et al.
Published: (2025)

Cognitive Agent Compilation for Explicit Problem Solver Modeling
by: Moon, Hyeongdon, et al.
Published: (2026)

Using Zero-Shot LLM-Generated Survey Data for Geographically Explicit Population Synthesis
by: Anderson, Taylor, et al.
Published: (2026)

Breaking Up with Normatively Monolithic Agency with GRACE: A Reason-Based Neuro-Symbolic Architecture for Safe and Ethical AI Alignment
by: Jahn, Felix, et al.
Published: (2026)

Is Trust Correlated With Explainability in AI? A Meta-Analysis
by: Atf, Zahra, et al.
Published: (2025)

Towards interactive evaluations for interaction harms in human-AI systems
by: Ibrahim, Lujain, et al.
Published: (2024)

A five-layer framework for AI governance: integrating regulation, standards, and certification
by: Agarwal, Avinash, et al.
Published: (2025)

"I Am the One and Only, Your Cyber BFF": Understanding the Impact of GenAI Requires Understanding the Impact of Anthropomorphic AI
by: Cheng, Myra, et al.
Published: (2024)

The Essentials of AI for Life and Society: An AI Literacy Course for the University Community
by: Biswas, Joydeep, et al.
Published: (2025)

Personalizing explanations of AI-driven hints to users' characteristics: an empirical evaluation
by: Bahel, Vedant, et al.
Published: (2024)

AI Toolkit: Libraries and Essays for Exploring the Technology and Ethics of AI
by: Ho, Levin, et al.
Published: (2025)

Trust AI Regulation? Discerning users are vital to build trust and effective AI regulation
by: Alalawi, Zainab, et al.
Published: (2024)

Mapping AI Risk Mitigations: Evidence Scan and Preliminary AI Risk Mitigation Taxonomy
by: Saeri, Alexander K., et al.
Published: (2025)

Preliminary suggestions for rigorous GPAI model evaluations
by: Paskov, Patricia, et al.
Published: (2025)

Healthcare LLM Benchmarks Are Only as Good as Their Explicit Assumptions
by: Raman, Naveen, et al.
Published: (2026)

Can AI expose tax loopholes? Towards a new generation of legal policy assistants
by: Fratrič, Peter, et al.
Published: (2025)

Societal Capacity Assessment Framework: Measuring Resilience to Inform Advanced AI Risk Management
by: Gandhi, Milan, et al.
Published: (2025)

How will advanced AI systems impact democracy?
by: Summerfield, Christopher, et al.
Published: (2024)

AI and the Future of Digital Public Squares
by: Goldberg, Beth, et al.
Published: (2024)

AI-Generated Slides: Are They Good? Can Students Tell?
by: Leinonen, Juho, et al.
Published: (2026)

Navigating the EU AI Act: Foreseeable Challenges in Qualifying Deep Learning-Based Automated Inspections of Class III Medical Devices
by: Diaz, Julio Zanon, et al.
Published: (2025)

Emergent evaluation hubs in a decentralizing large language model ecosystem
by: Cebrian, Manuel, et al.
Published: (2025)

Responsible AI Governance: A Response to UN Interim Report on Governing AI for Humanity
by: Kiden, Sarah, et al.
Published: (2024)