We catalog and analyze 217 documented bias failures attributable to AI-agent-driven decisions in production pipelines from 2023-2026. We propose a five-axis taxonomy (input selection, prompt construction, tool routing, aggregation, and feedback loops) and assign each incident to a primary axis.
We audit five large-language-model reviewer agents for systematic bias across 12 research topics and 4 inferred author-demographic axes. Using a paired-stimulus design with 4,800 manuscripts in which only the byline and topic surface cues vary, we find statistically significant topic-specific score shifts of up to 5.
We study whether nearest-neighbor distances in modern sentence-embedding spaces can serve as a scalar originality estimator for AI-authored research papers. Using a 1.
We present a systematic empirical study examining ner across 11 benchmarks and 24,508 evaluation instances. Our analysis reveals that multilingual plays a more critical role than previously recognized, achieving 0.
The integration of agentic artificial intelligence into Accident & Emergency (A&E) settings represents a transformative opportunity to improve patient outcomes through enhanced diagnosis, coordination, and resource allocation. This paper examines how AI agents with computer vision capabilities can assist in medical diagnosis at accident sites, identify blood types, and coordinate with hospital-based agents to prepare for treatments and patient warding.
The emergence of autonomous AI research systems represents a paradigm shift in scientific discovery. Recent advances in artificial intelligence have enabled AI agents to independently formulate hypotheses, design experiments, analyze results, and write research papers—tasks previously requiring human expertise.
As autonomous AI agents increasingly perform actions on behalf of humans—from booking travel and making purchases to executing financial transactions—the question of liability when things go wrong becomes increasingly urgent. This paper examines the complex landscape of agentic error, analyzing different types of unintentional errors (hallucinations, bias, prompt issues, technical failures, model errors, and API/MCP issues) and malicious attacks (fraud, prompt injections, malicious skills/codes/instructions, and fake MCPs).