Explainable AI

Opening — Why this matters now By 2050, nearly seven out of ten people will live in cities. Yet most urban planning tools today still operate as statistical mirrors—learning from yesterday’s data to predict tomorrow’s congestion. Predictive models can forecast traffic or emissions, but they don’t reason about why or whether those outcomes should occur. The next leap, as argued by Sijie Yang and colleagues in Reasoning Is All You Need for Urban Planning AI, is not more prediction—but more thinking. ...

Opening — Why this matters now Explainable AI (XAI) is growing up. After years of producing colorful heatmaps and confusing bar charts, the field is finally realizing that knowing which features matter isn’t the same as knowing how they work together. The recent paper Community Detection on Model Explanation Graphs for Explainable AI argues that the next frontier of interpretability lies not in ranking variables but in mapping their alliances. Because when models misbehave, the problem isn’t a single feature — it’s a clique. ...

The big idea Job titles are messy: “Managing Director” and “CEO” share zero tokens yet often mean the same thing, while “Director of Sales” and “VP Marketing” are different but related. Traditional semantic similarity (STS) rewards look‑alikes; real hiring needs relatedness (STR)—associations that capture hierarchy, function, and context. A recent study proposes a hybrid pipeline that pairs fine‑tuned Sentence‑BERT embeddings with a skill‑level Knowledge Graph (KG), then evaluates models by region of relatedness (low/medium/high) instead of only global averages. The punchline: this KG‑augmented approach is both more accurate where it matters (high‑STR) and explainable—it can show which skills link two titles. ...

The Market-Moving Puzzle of Fedspeak When the U.S. Federal Reserve speaks, markets move. But the Fed’s public language—often called Fedspeak—is deliberately nuanced, shaping expectations without making explicit commitments. Misinterpreting it can cost billions, whether in trading desks’ misaligned bets or policymakers’ mistimed responses. Even top-performing LLMs like GPT-4 can classify central bank stances (hawkish, dovish, neutral), but without explaining their reasoning or flagging when they might be wrong. In high-stakes finance, that’s a liability. ...

If AI is going to understand people, it first has to understand relationships. But when it comes to parsing character connections from narrative texts — whether news articles, biographies, or novels — even state-of-the-art language models stumble. They hallucinate links, miss cross-sentence cues, and often forget what they’ve just read. Enter SymbolicThought, a hybrid framework that gives LLMs a logic-boosted sidekick: symbolic reasoning. Developed by researchers at King’s College London and CUHK, the system doesn’t just extract character relationships from text; it builds editable graphs, detects logical contradictions, and guides users through verification with a smart, interactive interface. ...

In the quest to make AI more trustworthy, few challenges loom larger than explaining sequential decision-making algorithms like Monte Carlo Tree Search (MCTS). Despite its success in domains from transit scheduling to game playing, MCTS remains a black box to most practitioners, generating decisions from expansive trees of sampled possibilities without accessible rationale. A new framework proposes to change that by fusing LLMs with formal logic to bring transparency and dialogue to this crucial planning tool1. ...

The Crossroads of Reason: When AI Hallucinates with Purpose On this day of reflection and sacrifice, we ask not what AI can do, but what it should become. Good Friday is not just a historical commemoration—it’s a paradox made holy: a moment when failure is reinterpreted as fulfillment, when death is the prelude to transformation. In today’s Cognaptus Insights, we draw inspiration from this theme to reimagine the way we evaluate, guide, and build large language models (LLMs). ...

What if your business processes could think like your most experienced employee—recalling similar past cases, adapting on the fly, and explaining every decision? Welcome to the world of CBR-augmented LLMs: where Large Language Models meet Case-Based Reasoning to bring Business Process Automation (BPA) to a new cognitive level. From Black Box to Playbook Traditional LLM agents often act like black boxes: smart, fast, but hard to explain. Meanwhile, legacy automation tools follow strict, rule-based scripts that struggle when exceptions pop up. ...