Fairness, accountability and transparency: notes on algorithmic decision-making in criminal justice
AbstractOver the last few years, legal scholars, policy-makers, activists and others have generated a vast and rapidly expanding literature concerning the ethical ramifications of using artificial intelligence, machine learning, big data and predictive software in criminal justice contexts. These concerns...
AgentLAB: Benchmarking LLM Agents against Long-Horizon Attacks
arXiv:2602.16901v1 Announce Type: new Abstract: LLM agents are increasingly deployed in long-horizon, complex environments to solve challenging problems, but this expansion exposes them to long-horizon attacks that exploit multi-turn user-agent-environment interactions to achieve objectives infeasible in single-turn settings. To measure...
AIDG: Evaluating Asymmetry Between Information Extraction and Containment in Multi-Turn Dialogue
arXiv:2602.17443v1 Announce Type: new Abstract: Evaluating the strategic reasoning capabilities of Large Language Models (LLMs) requires moving beyond static benchmarks to dynamic, multi-turn interactions. We introduce AIDG (Adversarial Information Deduction Game), a game-theoretic framework that probes the asymmetry between information...
Learning to Stay Safe: Adaptive Regularization Against Safety Degradation during Fine-Tuning
arXiv:2602.17546v1 Announce Type: new Abstract: Instruction-following language models are trained to be helpful and safe, yet their safety behavior can deteriorate under benign fine-tuning and worsen under adversarial updates. Existing defenses often offer limited protection or force a trade-off between...
Mitigating Gradient Inversion Risks in Language Models via Token Obfuscation
arXiv:2602.15897v1 Announce Type: new Abstract: Training and fine-tuning large-scale language models largely benefit from collaborative learning, but the approach has been proven vulnerable to gradient inversion attacks (GIAs), which allow adversaries to reconstruct private training data from shared gradients. Existing...
Hybrid Federated and Split Learning for Privacy Preserving Clinical Prediction and Treatment Optimization
arXiv:2602.15304v1 Announce Type: new Abstract: Collaborative clinical decision support is often constrained by governance and privacy rules that prevent pooling patient-level records across institutions. We present a hybrid privacy-preserving framework that combines Federated Learning (FL) and Split Learning (SL) to...
The anticipated criminal law decisions and arguments for the rest of this term
ScotusCrim is a recurring series by Rory Little focusing on intersections between the Supreme Court and criminal law. Today’s column is my busman’s holiday project: providing nerd-like numbers and information […]The postThe anticipated criminal law decisions and arguments for the...
Criminalising ‘Conversion Therapy’
An increasing number of jurisdictions have introduced legal bans on so-called ‘conversion therapy’ practices. Yet significant uncertainty and disagreement persist among legal scholars, policymakers and advocates about whether criminal law is an appropriate tool in this area and, if so,...