เปรียบเทียบวิธี

	LSTM ที่อธิบายได้	หน่วยความจำระยะสั้นแบบยาว (LSTM)
สาขาวิชา	การเรียนรู้เชิงลึก	การเรียนรู้เชิงลึก
ตระกูล	Machine learning	Machine learning
ปีกำเนิด	2017–2019	1997
ผู้ริเริ่ม	Lundberg & Lee (SHAP); Ribeiro et al. (LIME); community synthesis	Hochreiter, S. & Schmidhuber, J.
ประเภท	Interpretable deep learning (post-hoc explainability)	Recurrent neural network with gated memory cells
แหล่งต้นตำรับ	Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774.	Hochreiter, S. & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.
ชื่อเรียกอื่น	XAI-LSTM, interpretable LSTM, LSTM with SHAP, transparent LSTM	LSTM, LSTM network, LSTM-RNN, long short-term memory RNN
ที่เกี่ยวข้อง	5	4
สรุป	Explainable LSTM pairs a trained Long Short-Term Memory network with post-hoc interpretability techniques — chiefly SHAP, LIME, integrated gradients, or attention visualization — to reveal which time steps, tokens, or features drive each prediction. It bridges the accuracy of recurrent deep learning with the transparency demanded by high-stakes domains such as clinical decision support, fraud detection, and regulatory compliance.	Long Short-Term Memory (LSTM) is a gated recurrent neural network architecture introduced by Hochreiter and Schmidhuber in 1997. It was designed to learn dependencies across long sequences by using dedicated memory cells and three learned gates — forget, input, and output — that control what information is retained, updated, or passed forward at each time step.