Seeing the Big Picture: AI and Climate Change Communication
New research assesses how effectively artificial intelligence can analyze visual content on social media to understand public discourse around climate change.
Science
Shattered Networks: Reconstructing Graphs from Fragmented Data
New research demonstrates how easily graph structures can be revealed even when using privacy-preserving spectral embeddings, and introduces tools to both benchmark this leakage and rebuild fragmented networks.
Echoes in the Data: Uncovering Hidden Market Signals
New research reveals that analyzing past text data with frozen language models can reveal economically relevant information missed by current market valuations.
Beyond Incident Counts: Mapping the Rise of AI Harms
A new framework classifies the evolving patterns of AI-related incidents to move beyond simple tracking and toward proactive risk mitigation.
Mapping Flood Risk with AI: A Faster, Data-Driven Approach
Researchers have developed an artificial intelligence framework that significantly speeds up flood hazard mapping by learning from complex hydraulic simulations.
When Algorithms Don’t Know What They Don’t Know: Fairness in Uncertain Systems
![Efforts to minimize false negatives, while initially effective, demonstrate a tendency toward instability and overshoot across decision rounds-a phenomenon exacerbated by interaction proxy bias, which causes diverging trajectories and underscores the inherent limitations of addressing uncertainty when foundational proxies are structurally compromised, as reflected in the observed [latex]\Delta\text{FNR}[/latex] fluctuations.](https://arxiv.org/html/2604.21711v1/x12.png)
New research explores how acknowledging and quantifying uncertainty in sequential decision-making-particularly when data is biased-can lead to more equitable and effective AI systems.
Opening the AI Black Box: A Path to Verifiable Safety
New research proposes a rigorous statistical framework for certifying the safety of artificial intelligence systems, moving beyond abstract risk assessments.
Predicting Sepsis Before It Strikes: A New Approach to Early Warning
Researchers have developed a novel system that forecasts the onset of sepsis by simulating a patient’s physiological trends, offering a crucial window for intervention.
Can AI Really Analyze Stocks?
A new benchmark reveals the current capabilities-and limitations-of artificial intelligence in conducting professional financial equity research.
Tracking the Tipping Point: How Network Analysis Can Predict Market Crashes
![A network analysis of co-trading relationships reveals distinct communities of principal participants-identified through comprehensive time-series data ([latex]DNM1[/latex]) and those focused on trading point processes ([latex]DNM2[/latex])-with participants unique to each approach highlighted in blue and red, respectively, while those present in both are shown in orange, and network layouts either maintain consistency with prior visualizations or prioritize proximity between tightly connected nodes.](https://arxiv.org/html/2604.21297v1/x6.png)
New research reveals how analyzing the interactions of traders can provide early warnings of financial instability, moving beyond traditional economic indicators.