9 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…
Auditing Privacy in Multi-Tenant RAG under Account Collusion
Florian A. D. Burnat
Multi-tenant RAG services often treat the account as the privacy boundary: each account receives an -DP retrieval guarantee against the t…
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…
Differentially Private Auditing Under Strategic Response
Florian A. D. Burnat
Regulatory audits of AI systems increasingly rely on differential privacy (DP) to protect training data and model internals. We study audit design when the audited developer can st…
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…