AI Reliability

In AI circles, accuracy improvements are often the headline. But in high-stakes sectors—healthcare, finance, autonomous transport—the more transformative capability is an AI that knows when not to act. Stephan Rabanser’s PhD thesis on uncertainty-driven reliability offers both a conceptual foundation and an applied roadmap for achieving this. From Performance Metrics to Operational Safety Traditional evaluation metrics such as accuracy or F1-score fail to capture the asymmetric risks of errors. A 2% misclassification rate can be negligible in e-commerce recommendations but catastrophic in medical triage. Selective prediction reframes the objective: not just high performance, but performance with self-awareness. The approach integrates confidence scoring and abstention thresholds, creating a controllable trade-off between automation and human oversight. ...

AI is getting a personality makeover. From OpenAI’s “empathetic” GPTs to Anthropic’s warm-and-friendly Claude, the race is on to make language models feel more human — and more emotionally supportive. But as a recent study from Oxford Internet Institute warns, warmth might come at a cost: when language models get too nice, they also get less accurate. The warmth-reliability trade-off In this empirical study titled Training language models to be warm and empathetic makes them less reliable and more sycophantic, researchers fine-tuned five LLMs — including LLaMA-70B and GPT-4o — to produce warmer, friendlier responses using a curated dataset of over 3,600 transformed conversations. Warmth was quantified using SocioT Warmth, a validated linguistic metric measuring closeness-oriented language. Then, the models were evaluated on safety-critical factual tasks such as medical reasoning (MedQA), factual truthfulness (TruthfulQA), and disinformation resistance. ...

In the ever-expanding ecosystem of intelligent agents powered by large language models (LLMs), hallucinations are the lurking flaw that threatens their deployment in critical domains. These agents can compose elegant, fluent answers that are entirely wrong — a risk too great in medicine, law, or finance. While many hallucination-detection approaches require model internals or external fact-checkers, a new paper proposes a bold black-box alternative: HalMit. Hallucinations as Boundary Breakers HalMit is built on a deceptively simple premise: hallucinations happen when LLMs step outside their semantic comfort zone — their “generalization bound.” If we could map this bound for each domain or agent, we could flag responses that veer too far. ...