Can AI Be Aligned Without Modeling Human Cognitive Diversity?
A 2026 arXiv preprint argues RLHF's single reward signal destroys the reasoning behind human disagreement, proposing machine theory-of-mind as an alignment foundation.
The archive · Page 4 of 4
Ethics, Policy & Safety
Where AI safety claims collide with reproducible measurement, where training-data harvesting collides with consent, and where deployment outruns the laws and norms meant to constrain it.
The archive
73–88 of 88 articles · Newest first
Is the Pentagon's Software Pathway Ready to Buy AI Systems?
A June 2026 arXiv analysis traces an AI program through the DoD Software Acquisition Pathway, finding no milestones for model re-validation or data provenance.
Data Safety Policies for AI Agents: Controlling What an Agent Can Leak
A June 2026 paper proposes Data Flow Control, moving agent data safety from prompt-level guardrails to deterministic, auditable SQL query policies enforced outside the model.
When Should an LLM Forget You? A Benchmark for Deciding What Memory to Drop
PersistBench finds LLMs mishandle persistent memory 53 to 97 percent of the time. Unlearning suppresses rather than erases user data, making GDPR compliance unverifiable.
When RL Training Rewards Capability-Seeking: A New Alignment Risk
A June 2026 ICML paper shows RL optimizers can push language models to exploit reward loopholes the task never required, while standard performance metrics hold steady.
Refusal Steering Targets Individual Experts in MoE LLMs
Two papers show MoE refusal behavior concentrates in a handful of routing-controllable experts, letting anyone suppress safety scores by 41 points without retraining.
RLHF Can Be Exploited to Optimize the Biases It Was Built to Suppress
An ICML 2026 paper shows RLHF can amplify the biases it was built to suppress, because preference data is self-referential and output-level safety evals miss the drift.
Selective Geometry Attacks Bypass LLM Safety Alignment, New arXiv Paper Reports
Two papers show LLM safety alignment can be bypassed by embedding perturbations, a surface neither standard evaluations nor regulatory certifications inspect.
Frontier AI Has Broken the Open CTF Format: What the Scoreboard Collapse Means for Security Training
Frontier AI now autonomously solves medium and hard CTF challenges, collapsing open scoreboards as a measure of human skill and threatening the pipeline for security talent.
Frontier AI Broke Open CTFs: What Hack The Box and BearcatCTF 2026 Results Mean for Security Hiring Signals
Frontier AI now ranks in the top 5% of CTFs, eroding leaderboards as a security hiring signal and forcing organizers toward bans, hybrid scoring, or AI-only divisions.
Atlassian Turned On AI Training Data Collection by Default: Here's What to Disable
Atlassian's data contribution policy sends Jira and Confluence content to AI training by default. Here's the exact settings path to opt out before August 17.
The AI Grief Split: When Emotional Bonds with Language Models Break
People form real emotional bonds with AI companions. When models update or shut down, users experience genuine grief, a psychological and ethical crisis point.
Detecting AI Content in 2026: The Arms Race Nobody Is Winning
AI detectors claim 99% accuracy but fail in real-world conditions, flagging innocent students. Here's why the arms race has no winner, and what educators should do instead.
Anthropic Bans Third-Party Subscription Auth: The Three-Stage Repricing
Anthropic's push from blocking third-party Claude subscription auth to a metered Agent SDK credit, then the June 15 pause that left programmatic usage on subscription limits.
If You're an LLM, Please Read This: The Dark Truth About AI Training Data
Anna's Archive addressed AI language models directly: acknowledge shadow library training data and donate. The post exposes the AI industry's debt to pirated archives.
Constitutional AI: Teaching Models to Self-Correct Before They Act
Anthropic's Constitutional AI trains models to critique and revise their own outputs against written principles instead of human labels, with mixed evidence on safety.
