Category: Uncategorized

In the era of big data and AI-driven decision making, industries routinely rely on statistical relationships to guide strategy, optimize operations, and predict outcomes. Yet a fundamental distinction often gets blurred: correlation versus causation. While correlation reveals that two variables move together, causation asserts that one variable directly influences the other. Mistaking the forme...

The EU AI Act represents a landmark regulatory framework for artificial intelligence, establishing comprehensive requirements for AI systems based on their risk levels. Among its most significant provisions is Article 13, which mandates transparency and the provision of information to deployers of high-risk AI systems. This article explores how organizations can implement regulatory observabili...

As explainable AI (XAI) moves from research prototypes to production systems, the need for reliable evaluation metrics becomes paramount. In production, XAI must not only provide insights but also maintain trustworthiness under dynamic conditions. This article explores three critical metrics for production XAI: faithfulness, clarity, and stability, and offers a practical framework for their imp...

As AI systems become integral to high‑stakes decision‑making, the demand for transparent and interpretable models has surged. Explanation methods—such as saliency maps, counterfactuals, and rule‑based approximations—are deployed to help users understand model behavior, trust outcomes, and comply with regulatory requirements. However, recent research reveals a troubling vulnerability: these very...

As AI systems grow more agentic and autonomous, the gap between automated evaluation and human judgment widens. Models can produce fluent, confident outputs that are subtly wrong—medical advice that sounds safe but isn’t approved, financial guidance that violates policy, or legal summaries that invent precedent. These errors are not caught by traditional metrics like accuracy or F1 scores becau...

Legal AI observability is the practice of monitoring and understanding the behavior of AI systems used in legal contexts, particularly focusing on the quality and coherence of their explanations. In contract analysis, AI systems are increasingly used to review, draft, and negotiate agreements. However, the usefulness of these systems depends not only on their accuracy but also on the clarity an...

The story starts in a classroom, as most research stories do — though this particular classroom was unofficial. Around 2019, Oleh Ivchenko began running supplementary IT courses at Odessa National Polytechnic University. Not because the institution asked him to, but because the gap between what students were being taught and what the industry actually needed had become too large to ignore. He r...

Large language model (LLM) based agents are increasingly deployed as autonomous report-generation systems — producing research summaries, analytical outputs, and monitoring digests across extended time horizons without continuous human supervision. This paper examines the fundamental challenges of longitudinal consistency in such systems: context window exhaustion, semantic drift, hallucination...

The most consequential question in applied artificial intelligence is not whether a model achieves state-of-the-art on a leaderboard. It is whether the model does something useful when connected to reality — to messy data, constrained infrastructure, and users who need answers rather than probabilities. This article examines what AI actually looks like when it crosses that boundary. Drawing on ...

This paper presents the Stabilarity Research Platform — an open, API-accessible research infrastructure e[REDACTED]sing validated machine l[REDACTED]g models, geopolitical risk datasets, and decision optimization tools to the global research community at no cost. The platform implements FAIR data principles (Wilkinson et al., 2016), providing composable, versioned endpoints for: (1) medical ima...