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This article explains JD's complex recommendation system data pipeline—from indexing, sampling, and feature engineering to explainability and real‑time metrics—highlighting challenges such as data consistency, latency, and the use of Flink for massive, low‑latency processing.
The paper introduces an explainable LLM framework (ELLM‑rele) that uses chain‑of‑thought reasoning and a multi‑dimensional knowledge distillation pipeline to compress large‑model relevance judgments into lightweight student models, achieving superior offline relevance scores and online click‑through and conversion improvements in Taobao’s search advertising.
Kuaishou and Tsinghua University were honored with the first‑place Qian Weichang Chinese Information Processing Science and Technology Award for their collaborative content recommendation project, which achieved international‑level innovations in explainable recommendation, bias correction, and edge intelligence, and has been applied widely in Kuaishou's platform and top academic conferences.
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Neglecting AI ethics and governance can expose companies to severe public‑relations crises, biased outcomes, regulatory penalties, unexplainable systems, and employee disengagement, ultimately threatening both societal trust and business sustainability.
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This article presents an in‑depth overview of Baidu's Data Federation Platform, detailing its layered architecture, core technical capabilities, privacy‑preserving collaborative research on epidemic prediction and shared vehicle optimization, and explores federated learning types, PaddleFL implementations, and model explainability techniques.
The article examines the rapid growth of recommendation systems, highlighting the need for industrial‑grade benchmarks, transparent explainability, and addressing algorithmic confounding caused by feedback loops, while discussing how these issues affect both users and content providers in the AI‑driven ecosystem.
This article surveys recent advances in graph neural network explainability, systematically categorizing instance‑level and model‑level methods, reviewing datasets, evaluation metrics, and proposing new benchmark graph datasets for interpretable GNN research, and highlighting future research directions.
Machine learning models often suffer from poor explainability and unstable predictions due to reliance on spurious correlations, but by applying causal inference to separate true causal relationships from confounding and selection bias, a causal‑constrained stable learning framework can achieve more interpretable and robust predictions across varying data distributions.
Recommendation systems, driven by recent economic and deep‑learning advances, face critical issues such as the lack of unified industrial benchmarks, limited explainability for users and content providers, and feedback‑loop induced data confounding, prompting calls for open datasets, transparent models, and collaborative optimization across stakeholders.
JD Security presents a series of AI‑driven security innovations—including black‑box explanation methods, deep‑learning crash analysis, AI‑vs‑AI e‑commerce fraud defenses, and open‑source collaboration—to illustrate how artificial intelligence can be made transparent, effective, and safely integrated into modern security operations.