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This paper demonstrates that naive train/test splitting on sliding-window sequences can severely inflate or deflate performance metrics in multi-task learning for predictive maintenance, and proposes a leakage-robust evaluation protocol.
This systematic review evaluates methodological reliability in machine learning models for early Chronic Kidney Disease prediction, revealing that data leakage inflates reported accuracy by over 15% and that more than 80% of predictors lack stability across studies.
Microsoft CEO Satya Nadella warns that companies using AI models from labs like OpenAI and Anthropic are unknowingly handing over proprietary business data, and advocates for companies to retain ownership of their data and build orchestration layers to switch between models.
A Twitter thread listing 13 core machine learning concepts that interviewers expect candidates to know, covering topics from bias-variance tradeoff to the curse of dimensionality.
This paper presents a decision-theoretic framework for detecting data leakage in predictive models using only model outputs and outcomes, proving that certain leakage types can be identified without external benchmarks or training code.
PropMe is a propensity-aware framework for evaluating LLM memorization, distinguishing between forced reproduction capabilities and natural propensity using SimpleTrace for deterministic attribution across open models and datasets.
This paper introduces TypewriterLM, a 7.24B parameter language model trained exclusively on English text predating 1913, along with TypewriterCorpus (a 54B-token cleaned historical corpus) and instruction-tuning datasets to avoid temporal leakage and lookahead bias. It also presents a benchmark suite, History-Event, for evaluating temporal grounding and leakage.
A Princeton study found data leakage in nearly 300 AI papers across 17 fields, causing overoptimistic results. The author highlights how easy it is to accidentally leak data and cautions against trusting impressive AI claims without checking for leakage.
A free tool has been released to help users detect personally identifiable information (PII) leaking from their LLM prompts before they reach the provider's servers.
OpenGuardrails is an open-source platform for AI safety, offering context-aware content-safety and manipulation detection (e.g., prompt injection, jailbreaking) via a unified model, plus a separate NER pipeline for data-leakage identification. It achieves state-of-the-art performance on safety benchmarks and supports private, enterprise-grade deployment.
MIT 6.566 course lecture introduces security challenges for AI agents, including non-adversarial errors (e.g., accidental database deletion) and adversarial attacks (e.g., prompt injection, data leakage), and explains the basics of building systems from language models to conversational agents.