Building trust in proprietary model benchmarks using cryptographically secure environments
grith — Zero Trust for AI Agents
Security-first local AI agent platform with per-syscall interception and multi-filter scoring.
Darkbloom — Cost-Efficient Private AI Inference on Verified Macs
Encrypted inference on hardware-verified Apple Silicon. Comparable model performance, operator-blind privacy, and about 50% lower cost.
Darkbloom — Cost-Efficient Private AI Inference on Verified Macs
Encrypted inference on hardware-verified Apple Silicon. Comparable model performance, operator-blind privacy, and about 50% lower cost.
Darkbloom — Cost-Efficient Private AI Inference on Verified Macs
Encrypted inference on hardware-verified Apple Silicon. Comparable model performance, operator-blind privacy, and about 50% lower cost.
OpenClaw – NEAR AI
Run the internet’s favorite new AI agent with NEAR AI’s cryptographic privacy guarantees.
Third-party cyber evaluations involving OpenAI models
OpenAI explains recent third-party cybersecurity evaluation incidents and outlines new safeguards to strengthen AI model testing and evaluation.
Refine — AI Verification Trusted by World-Class Experts
Good decisions require verified quality. Refine devotes hours of frontier compute to protect your work and reputation from fixable mistakes.
The AI Trust Gap: 82% Are Skeptical, Yet Only 8% Always Check Sources
Original Exploding Topics survey data explores public sentiment on AI Overviews and AI-generated content, highlighting trust, skepticism, and shifting content consumption habits.
Confidential Inference via Trusted Virtual Machines
Announcing a new collaborative research paper on Confidential Inference, a set of tools to improve the security of our model weights and of our users' data
AI CVE Slop: The Crisis Drowning Open Source Security
The proliferation of AI-generated vulnerability reports — commonly termed “AI slop” — has emerged as one of the most significant…
Separating signal from noise in coding evaluations
A new analysis from OpenAI reveals issues in SWE-Bench Pro, a popular coding benchmark, raising concerns about reliability and accuracy in evaluating AI models.
Trust and reliance on AI — An experimental study on the extent and costs of overreliance on AI
Decision-making is undergoing rapid changes due to the introduction of artificial intelligence (AI), as AI recommender systems can help mitigate human flaws and increase decision accuracy and efficiency. However, AI can also commit errors or suffer from algorithmic bias. Hence, blind trust in technologies carries risks, as users may follow detrimental advice resulting in undesired consequences. Building upon research on algorithm appreciation and trust in AI, the current study investigates whether users who receive AI advice in an uncertain situation overrely on this advice — to their own detriment and that of other parties. In a domain-independent, incentivized, and interactive behavioral experiment, we find that the mere knowledge of advice being generated by an AI causes people to overrely on it, that is, to follow AI advice even when it contradicts available contextual information as well as their own assessment. Frequently, this overreliance leads not only to inefficient outcomes for the advisee, but also to undesired effects regarding third parties. The results call into question how AI is being used in assisted decision making, emphasizing the importance of AI literacy and effective trust calibration for productive deployment of such systems.
Tinfoil - Private AI
AI that keeps your data private at all times. Fast, powerful, and verifiable, thanks to secure hardware enclaves.
Safety and alignment in an era of long-horizon models
OpenAI shares lessons from deploying long-running AI models, highlighting new safety risks, observed failures, and improved safeguards through iterative deployment.
