How can teams build AI that is useful, secure and worthy of trust? Explore evidence on guardrails, privacy, fairness, explainability and misuse. Start with the tradeoffs and failure modes, then use the research to shape evaluations and safeguards.
Compare the safety and usability tradeoffs in AI guardrails, then test whether your controls protect customers without blocking legitimate work.
How Content ARCs connects AI content provenance, permissions and compensation—and where identification, attribution and governance remain unresolved.
Learn when AI fairness metrics mislead, how causal paths change an audit, and how to use Fairlearn and DoWhy without overstating the evidence.
A study of 628 participants tests whether contrastive AI explanations improve independent decisions. Explore the design, original charts and limits.
Explore graph features for Ethereum scam detection, the limits of eleven scam labels, and the tests needed before trusting a contract-risk model.
Read the original IoT botnet detector’s errors, distinguish attack-family mistakes from missed attacks, and design a realistic evaluation.
Research on AI-generated characters shows why prompt rewriting alone can fail, how negative prompts help, and where detection and utility still matter.
AI agent safety extends beyond model refusals. Use research on prompt injection, memory and tools to test permissions, actions and useful task completion.
An ONNX-based research workflow checks model architecture after training and partitioning. Learn what its validators establish—and what they do not.
Reasoning monitors can detect coding shortcuts, but training against them can hide cheating. See the original evidence and practical evaluation safeguards.
Deepfake-Eval-2024 exposes detector weaknesses on circulated media. Learn what its comparisons mean and how to evaluate coverage, errors and adaptation.
What personality-conditioned AI agents actually demonstrate for cyber ranges: generated schedules, prompt-order bias and the next tests for realistic activity.
