7 papers
Scaling, Lock-In, and Proxy Compliance: A Political Economy of Responsible AI
Florian A. D. Burnat, Brittany I. Davidson
AI accountability at scale is an institutional problem: who can observe, verify, and change deployed systems. We develop a sequential political-economy model in which an AI vendor…
Measuring Evaluation-Context Divergence in Open-Weight LLMs: A Paired-Prompt Protocol with Pilot Evidence of Alignment-Pipeline-Specific Heterogeneity
Florian A. D. Burnat, Brittany I. Davidson
Safety benchmarks are routinely treated as evidence about how a language model will behave once deployed, but this inference is fragile if behavior depends on whether a prompt look…
Gaming the Metric, Not the Harm: Certifying Safety Audits against Strategic Platform Manipulation
Florian A. D. Burnat, Brittany I. Davidson
Online-safety regulation under the UK Online Safety Act and the EU Digital Services Act increasingly treats scalar metrics as compliance evidence. Once announced, such a metric als…
Quotient Semivalues for False-Name-Resistant Data Attribution
Florian A. D. Burnat, Brittany I. Davidson
Data valuation methods allocate payments and audit training data's contribution to machine-learning pipelines; however, they often assume passive contributors. In reality, contribu…
A Benchmark for Strategic Auditee Gaming Under Continuous Compliance Monitoring
Florian A. D. Burnat, Brittany I. Davidson
Continuous post-deployment compliance audits, mandated by emerging regulations such as the EU AI Act and Digital Services Act, create a class of strategic gaming distinct from the…
Regulatory gray areas of LLM Terms
Brittany I. Davidson, Kate Muir, Florian A. D. Burnat +1
Large Language Models (LLMs) are increasingly integrated into academic research pipelines; however, the Terms of Service governing their use remain under-examined. We present a com…